#### Sample records for modeling time series

1. Modelling bursty time series

International Nuclear Information System (INIS)

Vajna, Szabolcs; Kertész, János; Tóth, Bálint

2013-01-01

Many human-related activities show power-law decaying interevent time distribution with exponents usually varying between 1 and 2. We study a simple task-queuing model, which produces bursty time series due to the non-trivial dynamics of the task list. The model is characterized by a priority distribution as an input parameter, which describes the choice procedure from the list. We give exact results on the asymptotic behaviour of the model and we show that the interevent time distribution is power-law decaying for any kind of input distributions that remain normalizable in the infinite list limit, with exponents tunable between 1 and 2. The model satisfies a scaling law between the exponents of interevent time distribution (β) and autocorrelation function (α): α + β = 2. This law is general for renewal processes with power-law decaying interevent time distribution. We conclude that slowly decaying autocorrelation function indicates long-range dependence only if the scaling law is violated. (paper)

2. Introduction to Time Series Modeling

CERN Document Server

Kitagawa, Genshiro

2010-01-01

In time series modeling, the behavior of a certain phenomenon is expressed in relation to the past values of itself and other covariates. Since many important phenomena in statistical analysis are actually time series and the identification of conditional distribution of the phenomenon is an essential part of the statistical modeling, it is very important and useful to learn fundamental methods of time series modeling. Illustrating how to build models for time series using basic methods, "Introduction to Time Series Modeling" covers numerous time series models and the various tools f

3. Models for dependent time series

CERN Document Server

Tunnicliffe Wilson, Granville; Haywood, John

2015-01-01

Models for Dependent Time Series addresses the issues that arise and the methodology that can be applied when the dependence between time series is described and modeled. Whether you work in the economic, physical, or life sciences, the book shows you how to draw meaningful, applicable, and statistically valid conclusions from multivariate (or vector) time series data.The first four chapters discuss the two main pillars of the subject that have been developed over the last 60 years: vector autoregressive modeling and multivariate spectral analysis. These chapters provide the foundational mater

4. Stochastic models for time series

CERN Document Server

Doukhan, Paul

2018-01-01

This book presents essential tools for modelling non-linear time series. The first part of the book describes the main standard tools of probability and statistics that directly apply to the time series context to obtain a wide range of modelling possibilities. Functional estimation and bootstrap are discussed, and stationarity is reviewed. The second part describes a number of tools from Gaussian chaos and proposes a tour of linear time series models. It goes on to address nonlinearity from polynomial or chaotic models for which explicit expansions are available, then turns to Markov and non-Markov linear models and discusses Bernoulli shifts time series models. Finally, the volume focuses on the limit theory, starting with the ergodic theorem, which is seen as the first step for statistics of time series. It defines the distributional range to obtain generic tools for limit theory under long or short-range dependences (LRD/SRD) and explains examples of LRD behaviours. More general techniques (central limit ...

5. Lag space estimation in time series modelling

DEFF Research Database (Denmark)

Goutte, Cyril

1997-01-01

The purpose of this article is to investigate some techniques for finding the relevant lag-space, i.e. input information, for time series modelling. This is an important aspect of time series modelling, as it conditions the design of the model through the regressor vector a.k.a. the input layer...

6. Time series modeling, computation, and inference

CERN Document Server

2010-01-01

The authors systematically develop a state-of-the-art analysis and modeling of time series. … this book is well organized and well written. The authors present various statistical models for engineers to solve problems in time series analysis. Readers no doubt will learn state-of-the-art techniques from this book.-Hsun-Hsien Chang, Computing Reviews, March 2012My favorite chapters were on dynamic linear models and vector AR and vector ARMA models.-William Seaver, Technometrics, August 2011… a very modern entry to the field of time-series modelling, with a rich reference list of the current lit

7. Forecasting with nonlinear time series models

DEFF Research Database (Denmark)

Kock, Anders Bredahl; Teräsvirta, Timo

applied to economic fore- casting problems, is briefly highlighted. A number of large published studies comparing macroeconomic forecasts obtained using different time series models are discussed, and the paper also contains a small simulation study comparing recursive and direct forecasts in a partic......In this paper, nonlinear models are restricted to mean nonlinear parametric models. Several such models popular in time series econo- metrics are presented and some of their properties discussed. This in- cludes two models based on universal approximators: the Kolmogorov- Gabor polynomial model...

8. Building Chaotic Model From Incomplete Time Series

Science.gov (United States)

Siek, Michael; Solomatine, Dimitri

2010-05-01

This paper presents a number of novel techniques for building a predictive chaotic model from incomplete time series. A predictive chaotic model is built by reconstructing the time-delayed phase space from observed time series and the prediction is made by a global model or adaptive local models based on the dynamical neighbors found in the reconstructed phase space. In general, the building of any data-driven models depends on the completeness and quality of the data itself. However, the completeness of the data availability can not always be guaranteed since the measurement or data transmission is intermittently not working properly due to some reasons. We propose two main solutions dealing with incomplete time series: using imputing and non-imputing methods. For imputing methods, we utilized the interpolation methods (weighted sum of linear interpolations, Bayesian principle component analysis and cubic spline interpolation) and predictive models (neural network, kernel machine, chaotic model) for estimating the missing values. After imputing the missing values, the phase space reconstruction and chaotic model prediction are executed as a standard procedure. For non-imputing methods, we reconstructed the time-delayed phase space from observed time series with missing values. This reconstruction results in non-continuous trajectories. However, the local model prediction can still be made from the other dynamical neighbors reconstructed from non-missing values. We implemented and tested these methods to construct a chaotic model for predicting storm surges at Hoek van Holland as the entrance of Rotterdam Port. The hourly surge time series is available for duration of 1990-1996. For measuring the performance of the proposed methods, a synthetic time series with missing values generated by a particular random variable to the original (complete) time series is utilized. There exist two main performance measures used in this work: (1) error measures between the actual

9. MODELLING OF ORDINAL TIME SERIES BY PROPORTIONAL ODDS MODEL

Directory of Open Access Journals (Sweden)

Serpil AKTAŞ ALTUNAY

2013-06-01

Full Text Available Categorical time series data with random time dependent covariates often arise when the variable categories are assigned as categorical. There are several other models that have been proposed in the literature for the analysis of categorical time series. For example, Markov chain models, integer autoregressive processes, discrete ARMA models can be utilized for modeling of categorical time series. In general, the choice of model depends on the measurement of study variables: nominal, ordinal and interval. However, regression theory is successful approach for categorical time series which is based on generalized linear models and partial likelihood inference. One of the models for ordinal time series in regression theory is proportional odds model. In this study, proportional odds model approach to ordinal categorical time series is investigated based on a real air pollution data set and the results are discussed.

10. Fisher information framework for time series modeling

Science.gov (United States)

Venkatesan, R. C.; Plastino, A.

2017-08-01

A robust prediction model invoking the Takens embedding theorem, whose working hypothesis is obtained via an inference procedure based on the minimum Fisher information principle, is presented. The coefficients of the ansatz, central to the working hypothesis satisfy a time independent Schrödinger-like equation in a vector setting. The inference of (i) the probability density function of the coefficients of the working hypothesis and (ii) the establishing of constraint driven pseudo-inverse condition for the modeling phase of the prediction scheme, is made, for the case of normal distributions, with the aid of the quantum mechanical virial theorem. The well-known reciprocity relations and the associated Legendre transform structure for the Fisher information measure (FIM, hereafter)-based model in a vector setting (with least square constraints) are self-consistently derived. These relations are demonstrated to yield an intriguing form of the FIM for the modeling phase, which defines the working hypothesis, solely in terms of the observed data. Cases for prediction employing time series' obtained from the: (i) the Mackey-Glass delay-differential equation, (ii) one ECG signal from the MIT-Beth Israel Deaconess Hospital (MIT-BIH) cardiac arrhythmia database, and (iii) one ECG signal from the Creighton University ventricular tachyarrhythmia database. The ECG samples were obtained from the Physionet online repository. These examples demonstrate the efficiency of the prediction model. Numerical examples for exemplary cases are provided.

11. Modeling Time Series Data for Supervised Learning

Science.gov (United States)

Baydogan, Mustafa Gokce

2012-01-01

Temporal data are increasingly prevalent and important in analytics. Time series (TS) data are chronological sequences of observations and an important class of temporal data. Fields such as medicine, finance, learning science and multimedia naturally generate TS data. Each series provide a high-dimensional data vector that challenges the learning…

12. Time series modeling for syndromic surveillance

Directory of Open Access Journals (Sweden)

Mandl Kenneth D

2003-01-01

Full Text Available Abstract Background Emergency department (ED based syndromic surveillance systems identify abnormally high visit rates that may be an early signal of a bioterrorist attack. For example, an anthrax outbreak might first be detectable as an unusual increase in the number of patients reporting to the ED with respiratory symptoms. Reliably identifying these abnormal visit patterns requires a good understanding of the normal patterns of healthcare usage. Unfortunately, systematic methods for determining the expected number of (ED visits on a particular day have not yet been well established. We present here a generalized methodology for developing models of expected ED visit rates. Methods Using time-series methods, we developed robust models of ED utilization for the purpose of defining expected visit rates. The models were based on nearly a decade of historical data at a major metropolitan academic, tertiary care pediatric emergency department. The historical data were fit using trimmed-mean seasonal models, and additional models were fit with autoregressive integrated moving average (ARIMA residuals to account for recent trends in the data. The detection capabilities of the model were tested with simulated outbreaks. Results Models were built both for overall visits and for respiratory-related visits, classified according to the chief complaint recorded at the beginning of each visit. The mean absolute percentage error of the ARIMA models was 9.37% for overall visits and 27.54% for respiratory visits. A simple detection system based on the ARIMA model of overall visits was able to detect 7-day-long simulated outbreaks of 30 visits per day with 100% sensitivity and 97% specificity. Sensitivity decreased with outbreak size, dropping to 94% for outbreaks of 20 visits per day, and 57% for 10 visits per day, all while maintaining a 97% benchmark specificity. Conclusions Time series methods applied to historical ED utilization data are an important tool

13. Time Series Modeling for Structural Response Prediction

Science.gov (United States)

1988-11-14

results for 2nd mode. 69 5. 3DOF simulated data. 71 6. Experimental data. 72 7. Simulated data. 75 8. MPEM estimates for MDOF data with closely spaced...vector Ssteering matrix of residual time series 2DOF Two-degree-of-freedom 2LS Two-stage Least Squares Method 3DOF Three-degree-of-freedom x SUMMARY A...70 Table 5: 3DOF Simulated Data (fd= 1 ,10 ,25 ; C=.01,.0l,.0l; Amp=1,l,l; 256 pts, f,=2000 Hz) Algorithm grv noise higher mode grv, 4th mode, bias 40

14. TIME SERIES ANALYSIS USING A UNIQUE MODEL OF TRANSFORMATION

Directory of Open Access Journals (Sweden)

Goran Klepac

2007-12-01

Full Text Available REFII1 model is an authorial mathematical model for time series data mining. The main purpose of that model is to automate time series analysis, through a unique transformation model of time series. An advantage of this approach of time series analysis is the linkage of different methods for time series analysis, linking traditional data mining tools in time series, and constructing new algorithms for analyzing time series. It is worth mentioning that REFII model is not a closed system, which means that we have a finite set of methods. At first, this is a model for transformation of values of time series, which prepares data used by different sets of methods based on the same model of transformation in a domain of problem space. REFII model gives a new approach in time series analysis based on a unique model of transformation, which is a base for all kind of time series analysis. The advantage of REFII model is its possible application in many different areas such as finance, medicine, voice recognition, face recognition and text mining.

15. Vector bilinear autoregressive time series model and its superiority ...

African Journals Online (AJOL)

In this research, a vector bilinear autoregressive time series model was proposed and used to model three revenue series (X1, X2, X3) . The “orders” of the three series were identified on the basis of the distribution of autocorrelation and partial autocorrelation functions and were used to construct the vector bilinear models.

16. Hidden Markov Models for Time Series An Introduction Using R

CERN Document Server

Zucchini, Walter

2009-01-01

Illustrates the flexibility of HMMs as general-purpose models for time series data. This work presents an overview of HMMs for analyzing time series data, from continuous-valued, circular, and multivariate series to binary data, bounded and unbounded counts and categorical observations.

17. vector bilinear autoregressive time series model and its superiority ...

African Journals Online (AJOL)

In this research, a vector bilinear autoregressive time series model was proposed and used to model three revenue series(. )t ... showed that vector bilinear autoregressive (BIVAR) models provide better estimates than the long embraced linear models. ... order moving average (MA) polynomials on backward shift operator B ...

18. forecasting with nonlinear time series model: a monte-carlo

African Journals Online (AJOL)

PUBLICATIONS1

erated recursively up to any step greater than one. For nonlinear time series model, point forecast for step one can be done easily like in the linear case but forecast for a step greater than or equal to ..... London. Franses, P. H. (1998). Time series models for business and Economic forecasting, Cam- bridge University press.

19. Multiple Time Series Ising Model for Financial Market Simulations

International Nuclear Information System (INIS)

Takaishi, Tetsuya

2015-01-01

In this paper we propose an Ising model which simulates multiple financial time series. Our model introduces the interaction which couples to spins of other systems. Simulations from our model show that time series exhibit the volatility clustering that is often observed in the real financial markets. Furthermore we also find non-zero cross correlations between the volatilities from our model. Thus our model can simulate stock markets where volatilities of stocks are mutually correlated

20. Time series modelling of overflow structures

DEFF Research Database (Denmark)

Carstensen, J.; Harremoës, P.

1997-01-01

The dynamics of a storage pipe is examined using a grey-box model based on on-line measured data. The grey-box modelling approach uses a combination of physically-based and empirical terms in the model formulation. The model provides an on-line state estimate of the overflows, pumping capacities...... to the overflow structures. The capacity of a pump draining the storage pipe has been estimated for two rain events, revealing that the pump was malfunctioning during the first rain event. The grey-box modelling approach is applicable for automated on-line surveillance and control. (C) 1997 IAWQ. Published...

1. Time series sightability modeling of animal populations.

Directory of Open Access Journals (Sweden)

Althea A ArchMiller

Full Text Available Logistic regression models-or "sightability models"-fit to detection/non-detection data from marked individuals are often used to adjust for visibility bias in later detection-only surveys, with population abundance estimated using a modified Horvitz-Thompson (mHT estimator. More recently, a model-based alternative for analyzing combined detection/non-detection and detection-only data was developed. This approach seemed promising, since it resulted in similar estimates as the mHT when applied to data from moose (Alces alces surveys in Minnesota. More importantly, it provided a framework for developing flexible models for analyzing multiyear detection-only survey data in combination with detection/non-detection data. During initial attempts to extend the model-based approach to multiple years of detection-only data, we found that estimates of detection probabilities and population abundance were sensitive to the amount of detection-only data included in the combined (detection/non-detection and detection-only analysis. Subsequently, we developed a robust hierarchical modeling approach where sightability model parameters are informed only by the detection/non-detection data, and we used this approach to fit a fixed-effects model (FE model with year-specific parameters and a temporally-smoothed model (TS model that shares information across years via random effects and a temporal spline. The abundance estimates from the TS model were more precise, with decreased interannual variability relative to the FE model and mHT abundance estimates, illustrating the potential benefits from model-based approaches that allow information to be shared across years.

2. Time series sightability modeling of animal populations

Science.gov (United States)

ArchMiller, Althea A.; Dorazio, Robert; St. Clair, Katherine; Fieberg, John R.

2018-01-01

Logistic regression models—or “sightability models”—fit to detection/non-detection data from marked individuals are often used to adjust for visibility bias in later detection-only surveys, with population abundance estimated using a modified Horvitz-Thompson (mHT) estimator. More recently, a model-based alternative for analyzing combined detection/non-detection and detection-only data was developed. This approach seemed promising, since it resulted in similar estimates as the mHT when applied to data from moose (Alces alces) surveys in Minnesota. More importantly, it provided a framework for developing flexible models for analyzing multiyear detection-only survey data in combination with detection/non-detection data. During initial attempts to extend the model-based approach to multiple years of detection-only data, we found that estimates of detection probabilities and population abundance were sensitive to the amount of detection-only data included in the combined (detection/non-detection and detection-only) analysis. Subsequently, we developed a robust hierarchical modeling approach where sightability model parameters are informed only by the detection/non-detection data, and we used this approach to fit a fixed-effects model (FE model) with year-specific parameters and a temporally-smoothed model (TS model) that shares information across years via random effects and a temporal spline. The abundance estimates from the TS model were more precise, with decreased interannual variability relative to the FE model and mHT abundance estimates, illustrating the potential benefits from model-based approaches that allow information to be shared across years.

3. SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODEL FOR PRECIPITATION TIME SERIES

OpenAIRE

Yan Wang; Meng Gao; Xinghua Chang; Xiyong Hou

2012-01-01

Predicting the trend of precipitation is a difficult task in meteorology and environmental sciences. Statistical approaches from time series analysis provide an alternative way for precipitation prediction. The ARIMA model incorporating seasonal characteristics, which is referred to as seasonal ARIMA model was presented. The time series data is the monthly precipitation data in Yantai, China and the period is from 1961 to 2011. The model was denoted as SARIMA (1, 0, 1) (0, 1, 1)12 in this stu...

4. With string model to time series forecasting

Science.gov (United States)

Pinčák, Richard; Bartoš, Erik

2015-10-01

Overwhelming majority of econometric models applied on a long term basis in the financial forex market do not work sufficiently well. The reason is that transaction costs and arbitrage opportunity are not included, as this does not simulate the real financial markets. Analyses are not conducted on the non equidistant date but rather on the aggregate date, which is also not a real financial case. In this paper, we would like to show a new way how to analyze and, moreover, forecast financial market. We utilize the projections of the real exchange rate dynamics onto the string-like topology in the OANDA market. The latter approach allows us to build the stable prediction models in trading in the financial forex market. The real application of the multi-string structures is provided to demonstrate our ideas for the solution of the problem of the robust portfolio selection. The comparison with the trend following strategies was performed, the stability of the algorithm on the transaction costs for long trade periods was confirmed.

5. Time domain series system definition and gear set reliability modeling

International Nuclear Information System (INIS)

Xie, Liyang; Wu, Ningxiang; Qian, Wenxue

2016-01-01

Time-dependent multi-configuration is a typical feature for mechanical systems such as gear trains and chain drives. As a series system, a gear train is distinct from a traditional series system, such as a chain, in load transmission path, system-component relationship, system functioning manner, as well as time-dependent system configuration. Firstly, the present paper defines time-domain series system to which the traditional series system reliability model is not adequate. Then, system specific reliability modeling technique is proposed for gear sets, including component (tooth) and subsystem (tooth-pair) load history description, material priori/posterior strength expression, time-dependent and system specific load-strength interference analysis, as well as statistically dependent failure events treatment. Consequently, several system reliability models are developed for gear sets with different tooth numbers in the scenario of tooth root material ultimate tensile strength failure. The application of the models is discussed in the last part, and the differences between the system specific reliability model and the traditional series system reliability model are illustrated by virtue of several numerical examples. - Highlights: • A new type of series system, i.e. time-domain multi-configuration series system is defined, that is of great significance to reliability modeling. • Multi-level statistical analysis based reliability modeling method is presented for gear transmission system. • Several system specific reliability models are established for gear set reliability estimation. • The differences between the traditional series system reliability model and the new model are illustrated.

6. Tempered fractional time series model for turbulence in geophysical flows

Science.gov (United States)

Meerschaert, Mark M.; Sabzikar, Farzad; Phanikumar, Mantha S.; Zeleke, Aklilu

2014-09-01

We propose a new time series model for velocity data in turbulent flows. The new model employs tempered fractional calculus to extend the classical 5/3 spectral model of Kolmogorov. Application to wind speed and water velocity in a large lake are presented, to demonstrate the practical utility of the model.

7. Tempered fractional time series model for turbulence in geophysical flows

International Nuclear Information System (INIS)

Meerschaert, Mark M; Sabzikar, Farzad; Phanikumar, Mantha S; Zeleke, Aklilu

2014-01-01

We propose a new time series model for velocity data in turbulent flows. The new model employs tempered fractional calculus to extend the classical 5/3 spectral model of Kolmogorov. Application to wind speed and water velocity in a large lake are presented, to demonstrate the practical utility of the model. (paper)

8. forecasting with nonlinear time series model: a monte-carlo ...

African Journals Online (AJOL)

PUBLICATIONS1

with nonlinear time series model by comparing the RMSE with the traditional bootstrap and. Monte-Carlo method of forecasting. We use the logistic smooth transition autoregressive. (LSTAR) model as a case study. We first consider a linear model called the AR. (p) model of order p which satisfies the follow- ing linear ...

9. Stochastic modeling of hourly rainfall times series in Campania (Italy)

Science.gov (United States)

Giorgio, M.; Greco, R.

2009-04-01

Occurrence of flowslides and floods in small catchments is uneasy to predict, since it is affected by a number of variables, such as mechanical and hydraulic soil properties, slope morphology, vegetation coverage, rainfall spatial and temporal variability. Consequently, landslide risk assessment procedures and early warning systems still rely on simple empirical models based on correlation between recorded rainfall data and observed landslides and/or river discharges. Effectiveness of such systems could be improved by reliable quantitative rainfall prediction, which can allow gaining larger lead-times. Analysis of on-site recorded rainfall height time series represents the most effective approach for a reliable prediction of local temporal evolution of rainfall. Hydrological time series analysis is a widely studied field in hydrology, often carried out by means of autoregressive models, such as AR, ARMA, ARX, ARMAX (e.g. Salas [1992]). Such models gave the best results when applied to the analysis of autocorrelated hydrological time series, like river flow or level time series. Conversely, they are not able to model the behaviour of intermittent time series, like point rainfall height series usually are, especially when recorded with short sampling time intervals. More useful for this issue are the so-called DRIP (Disaggregated Rectangular Intensity Pulse) and NSRP (Neymann-Scott Rectangular Pulse) model [Heneker et al., 2001; Cowpertwait et al., 2002], usually adopted to generate synthetic point rainfall series. In this paper, the DRIP model approach is adopted, in which the sequence of rain storms and dry intervals constituting the structure of rainfall time series is modeled as an alternating renewal process. Final aim of the study is to provide a useful tool to implement an early warning system for hydrogeological risk management. Model calibration has been carried out with hourly rainfall hieght data provided by the rain gauges of Campania Region civil

10. Combined Forecasts from Linear and Nonlinear Time Series Models

NARCIS (Netherlands)

N. Terui (Nobuhiko); H.K. van Dijk (Herman)

1999-01-01

textabstractCombined forecasts from a linear and a nonlinear model are investigated for time series with possibly nonlinear characteristics. The forecasts are combined by a constant coefficient regression method as well as a time varying method. The time varying method allows for a locally

11. Combined forecasts from linear and nonlinear time series models

NARCIS (Netherlands)

N. Terui (Nobuhiko); H.K. van Dijk (Herman)

1999-01-01

textabstractCombined forecasts from a linear and a nonlinear model are investigated for time series with possibly nonlinear characteristics. The forecasts are combined by a constant coefficient regression method as well as a time varying method. The time varying method allows for a locally

12. Koopman Operator Framework for Time Series Modeling and Analysis

Science.gov (United States)

Surana, Amit

2018-01-01

We propose an interdisciplinary framework for time series classification, forecasting, and anomaly detection by combining concepts from Koopman operator theory, machine learning, and linear systems and control theory. At the core of this framework is nonlinear dynamic generative modeling of time series using the Koopman operator which is an infinite-dimensional but linear operator. Rather than working with the underlying nonlinear model, we propose two simpler linear representations or model forms based on Koopman spectral properties. We show that these model forms are invariants of the generative model and can be readily identified directly from data using techniques for computing Koopman spectral properties without requiring the explicit knowledge of the generative model. We also introduce different notions of distances on the space of such model forms which is essential for model comparison/clustering. We employ the space of Koopman model forms equipped with distance in conjunction with classical machine learning techniques to develop a framework for automatic feature generation for time series classification. The forecasting/anomaly detection framework is based on using Koopman model forms along with classical linear systems and control approaches. We demonstrate the proposed framework for human activity classification, and for time series forecasting/anomaly detection in power grid application.

13. Sparse time series chain graphical models for reconstructing genetic networks

NARCIS (Netherlands)

Abegaz, Fentaw; Wit, Ernst

We propose a sparse high-dimensional time series chain graphical model for reconstructing genetic networks from gene expression data parametrized by a precision matrix and autoregressive coefficient matrix. We consider the time steps as blocks or chains. The proposed approach explores patterns of

14. Models for Pooled Time-Series Cross-Section Data

Directory of Open Access Journals (Sweden)

Lawrence E Raffalovich

2015-07-01

Full Text Available Several models are available for the analysis of pooled time-series cross-section (TSCS data, defined as “repeated observations on fixed units” (Beck and Katz 1995. In this paper, we run the following models: (1 a completely pooled model, (2 fixed effects models, and (3 multi-level/hierarchical linear models. To illustrate these models, we use a Generalized Least Squares (GLS estimator with cross-section weights and panel-corrected standard errors (with EViews 8 on the cross-national homicide trends data of forty countries from 1950 to 2005, which we source from published research (Messner et al. 2011. We describe and discuss the similarities and differences between the models, and what information each can contribute to help answer substantive research questions. We conclude with a discussion of how the models we present may help to mitigate validity threats inherent in pooled time-series cross-section data analysis.

15. RADON CONCENTRATION TIME SERIES MODELING AND APPLICATION DISCUSSION.

Science.gov (United States)

Stránský, V; Thinová, L

2017-11-01

16. Forecasting with nonlinear time series model: A Monte-Carlo ...

African Journals Online (AJOL)

In this paper, we propose a new method of forecasting with nonlinear time series model using Monte-Carlo Bootstrap method. This new method gives better result in terms of forecast root mean squared error (RMSE) when compared with the traditional Bootstrap method and Monte-Carlo method of forecasting using a ...

17. Multivariate time series modeling of selected childhood diseases in ...

African Journals Online (AJOL)

This paper is focused on modeling the five most prevalent childhood diseases in Akwa Ibom State using a multivariate approach to time series. An aggregate of 78,839 reported cases of malaria, upper respiratory tract infection (URTI), Pneumonia, anaemia and tetanus were extracted from five randomly selected hospitals in ...

18. Parameterizing unconditional skewness in models for financial time series

DEFF Research Database (Denmark)

He, Changli; Silvennoinen, Annastiina; Teräsvirta, Timo

In this paper we consider the third-moment structure of a class of time series models. It is often argued that the marginal distribution of financial time series such as returns is skewed. Therefore it is of importance to know what properties a model should possess if it is to accommodate...... unconditional skewness. We consider modelling the unconditional mean and variance using models that respond nonlinearly or asymmetrically to shocks. We investigate the implications of these models on the third-moment structure of the marginal distribution as well as conditions under which the unconditional...... distribution exhibits skewness and nonzero third-order autocovariance structure. In this respect, an asymmetric or nonlinear specification of the conditional mean is found to be of greater importance than the properties of the conditional variance. Several examples are discussed and, whenever possible...

19. Model and Variable Selection Procedures for Semiparametric Time Series Regression

Directory of Open Access Journals (Sweden)

Risa Kato

2009-01-01

Full Text Available Semiparametric regression models are very useful for time series analysis. They facilitate the detection of features resulting from external interventions. The complexity of semiparametric models poses new challenges for issues of nonparametric and parametric inference and model selection that frequently arise from time series data analysis. In this paper, we propose penalized least squares estimators which can simultaneously select significant variables and estimate unknown parameters. An innovative class of variable selection procedure is proposed to select significant variables and basis functions in a semiparametric model. The asymptotic normality of the resulting estimators is established. Information criteria for model selection are also proposed. We illustrate the effectiveness of the proposed procedures with numerical simulations.

20. Quality Quandaries- Time Series Model Selection and Parsimony

DEFF Research Database (Denmark)

Bisgaard, Søren; Kulahci, Murat

2009-01-01

Some of the issues involved in selecting adequate models for time series data are discussed using an example concerning the number of users of an Internet server. The process of selecting an appropriate model is subjective and requires experience and judgment. The authors believe an important...... consideration in model selection should be parameter parsimony. They favor the use of parsimonious mixed ARMA models, noting that research has shown that a model building strategy that considers only autoregressive representations will lead to non-parsimonious models and to loss of forecasting accuracy....

1. Modeling Philippine Stock Exchange Composite Index Using Time Series Analysis

Science.gov (United States)

Gayo, W. S.; Urrutia, J. D.; Temple, J. M. F.; Sandoval, J. R. D.; Sanglay, J. E. A.

2015-06-01

This study was conducted to develop a time series model of the Philippine Stock Exchange Composite Index and its volatility using the finite mixture of ARIMA model with conditional variance equations such as ARCH, GARCH, EG ARCH, TARCH and PARCH models. Also, the study aimed to find out the reason behind the behaviorof PSEi, that is, which of the economic variables - Consumer Price Index, crude oil price, foreign exchange rate, gold price, interest rate, money supply, price-earnings ratio, Producers’ Price Index and terms of trade - can be used in projecting future values of PSEi and this was examined using Granger Causality Test. The findings showed that the best time series model for Philippine Stock Exchange Composite index is ARIMA(1,1,5) - ARCH(1). Also, Consumer Price Index, crude oil price and foreign exchange rate are factors concluded to Granger cause Philippine Stock Exchange Composite Index.

2. Fundamental State Space Time Series Models for JEPX Electricity Prices

Science.gov (United States)

Ofuji, Kenta; Kanemoto, Shigeru

Time series models are popular in attempts to model and forecast price dynamics in various markets. In this paper, we have formulated two state space models and tested them for its applicability to power price modeling and forecasting using JEPX (Japan Electric Power eXchange) data. The state space models generally have a high degree of flexibility with its time-dependent state transition matrix and system equation configurations. Based on empirical data analysis and past literatures, we used calculation assumptions to a) extract stochastic trend component to capture non-stationarity, and b) detect structural changes underlying in the market. The stepwise calculation algorithm followed that of Kalman Filter. We then evaluated the two models' forecasting capabilities, in comparison with ordinary AR (autoregressive) and ARCH (autoregressive conditional heteroskedasticity) models. By choosing proper explanatory variables, the latter state space model yielded as good a forecasting capability as that of the AR and the ARCH models for a short forecasting horizon.

3. Time Series Analysis, Modeling and Applications A Computational Intelligence Perspective

CERN Document Server

Chen, Shyi-Ming

2013-01-01

Temporal and spatiotemporal data form an inherent fabric of the society as we are faced with streams of data coming from numerous sensors, data feeds, recordings associated with numerous areas of application embracing physical and human-generated phenomena (environmental data, financial markets, Internet activities, etc.). A quest for a thorough analysis, interpretation, modeling and prediction of time series comes with an ongoing challenge for developing models that are both accurate and user-friendly (interpretable). The volume is aimed to exploit the conceptual and algorithmic framework of Computational Intelligence (CI) to form a cohesive and comprehensive environment for building models of time series. The contributions covered in the volume are fully reflective of the wealth of the CI technologies by bringing together ideas, algorithms, and numeric studies, which convincingly demonstrate their relevance, maturity and visible usefulness. It reflects upon the truly remarkable diversity of methodological a...

4. Identification of neutral biochemical network models from time series data

Directory of Open Access Journals (Sweden)

Maia Marco

2009-05-01

Full Text Available Abstract Background The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.e., if it is constructed according to strict guidelines. Results In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity. Conclusion The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.

5. Identification of neutral biochemical network models from time series data.

Science.gov (United States)

Vilela, Marco; Vinga, Susana; Maia, Marco A Grivet Mattoso; Voit, Eberhard O; Almeida, Jonas S

2009-05-05

The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.e., if it is constructed according to strict guidelines. In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity. The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.

6. Time series regression model for infectious disease and weather.

Science.gov (United States)

Imai, Chisato; Armstrong, Ben; Chalabi, Zaid; Mangtani, Punam; Hashizume, Masahiro

2015-10-01

7. Forecasting the Reference Evapotranspiration Using Time Series Model

Directory of Open Access Journals (Sweden)

H. Zare Abyaneh

2016-10-01

Full Text Available Introduction: Reference evapotranspiration is one of the most important factors in irrigation timing and field management. Moreover, reference evapotranspiration forecasting can play a vital role in future developments. Therefore in this study, the seasonal autoregressive integrated moving average (ARIMA model was used to forecast the reference evapotranspiration time series in the Esfahan, Semnan, Shiraz, Kerman, and Yazd synoptic stations. Materials and Methods: In the present study in all stations (characteristics of the synoptic stations are given in Table 1, the meteorological data, including mean, maximum and minimum air temperature, relative humidity, dry-and wet-bulb temperature, dew-point temperature, wind speed, precipitation, air vapor pressure and sunshine hours were collected from the Islamic Republic of Iran Meteorological Organization (IRIMO for the 41 years from 1965 to 2005. The FAO Penman-Monteith equation was used to calculate the monthly reference evapotranspiration in the five synoptic stations and the evapotranspiration time series were formed. The unit root test was used to identify whether the time series was stationary, then using the Box-Jenkins method, seasonal ARIMA models were applied to the sample data. Table 1. The geographical location and climate conditions of the synoptic stations Station\tGeographical location\tAltitude (m\tMean air temperature (°C\tMean precipitation (mm\tClimate, according to the De Martonne index classification Longitude (E\tLatitude (N Annual\tMin. and Max. Esfahan\t51° 40'\t32° 37'\t1550.4\t16.36\t9.4-23.3\t122\tArid Semnan\t53° 33'\t35° 35'\t1130.8\t18.0\t12.4-23.8\t140\tArid Shiraz\t52° 36'\t29° 32'\t1484\t18.0\t10.2-25.9\t324\tSemi-arid Kerman\t56° 58'\t30° 15'\t1753.8\t15.6\t6.7-24.6\t142\tArid Yazd\t54° 17'\t31° 54'\t1237.2\t19.2\t11.8-26.0\t61\tArid Results and Discussion: The monthly meteorological data were used as input for the Ref-ET software and monthly reference

8. Deriving dynamic marketing effectiveness from econometric time series models

OpenAIRE

Horváth, C.; Franses, Ph.H.B.F.

2003-01-01

textabstractTo understand the relevance of marketing efforts, it has become standard practice to estimate the long-run and short-run effects of the marketing-mix, using, say, weekly scanner data. A common vehicle for this purpose is an econometric time series model. Issues that are addressed in the literature are unit roots, cointegration, structural breaks and impulse response functions. In this paper we summarize the most important concepts by reviewing all possible empirical cases that can...

9. Optimization of recurrent neural networks for time series modeling

DEFF Research Database (Denmark)

Pedersen, Morten With

1997-01-01

The present thesis is about optimization of recurrent neural networks applied to time series modeling. In particular is considered fully recurrent networks working from only a single external input, one layer of nonlinear hidden units and a li near output unit applied to prediction of discrete time...... series. The overall objective s are to improve training by application of second-order methods and to improve generalization ability by architecture optimization accomplished by pruning. The major topics covered in the thesis are: 1. The problem of training recurrent networks is analyzed from a numerical...... of solution obtained as well as computation time required. 3. A theoretical definition of the generalization error for recurrent networks is provided. This definition justifies a commonly adopted approach for estimating generalization ability. 4. The viability of pruning recurrent networks by the Optimal...

10. A Comparative Study of Portmanteau Tests for Univariate Time Series Models

Directory of Open Access Journals (Sweden)

Sohail Chand

2006-07-01

Full Text Available Time series model diagnostic checking is the most important stage of time series model building. In this paper the comparison among several suggested diagnostic tests has been made using the simulation time series data.

11. Unsupervised Classification During Time-Series Model Building.

Science.gov (United States)

Gates, Kathleen M; Lane, Stephanie T; Varangis, E; Giovanello, K; Guiskewicz, K

2017-01-01

Researchers who collect multivariate time-series data across individuals must decide whether to model the dynamic processes at the individual level or at the group level. A recent innovation, group iterative multiple model estimation (GIMME), offers one solution to this dichotomy by identifying group-level time-series models in a data-driven manner while also reliably recovering individual-level patterns of dynamic effects. GIMME is unique in that it does not assume homogeneity in processes across individuals in terms of the patterns or weights of temporal effects. However, it can be difficult to make inferences from the nuances in varied individual-level patterns. The present article introduces an algorithm that arrives at subgroups of individuals that have similar dynamic models. Importantly, the researcher does not need to decide the number of subgroups. The final models contain reliable group-, subgroup-, and individual-level patterns that enable generalizable inferences, subgroups of individuals with shared model features, and individual-level patterns and estimates. We show that integrating community detection into the GIMME algorithm improves upon current standards in two important ways: (1) providing reliable classification and (2) increasing the reliability in the recovery of individual-level effects. We demonstrate this method on functional MRI from a sample of former American football players.

12. Single-Index Additive Vector Autoregressive Time Series Models

KAUST Repository

LI, YEHUA

2009-09-01

We study a new class of nonlinear autoregressive models for vector time series, where the current vector depends on single-indexes defined on the past lags and the effects of different lags have an additive form. A sufficient condition is provided for stationarity of such models. We also study estimation of the proposed model using P-splines, hypothesis testing, asymptotics, selection of the order of the autoregression and of the smoothing parameters and nonlinear forecasting. We perform simulation experiments to evaluate our model in various settings. We illustrate our methodology on a climate data set and show that our model provides more accurate yearly forecasts of the El Niño phenomenon, the unusual warming of water in the Pacific Ocean. © 2009 Board of the Foundation of the Scandinavian Journal of Statistics.

13. Modeling financial time series with S-plus

CERN Document Server

Zivot, Eric

2003-01-01

The field of financial econometrics has exploded over the last decade This book represents an integration of theory, methods, and examples using the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate the practice of financial econometrics This is the first book to show the power of S-PLUS for the analysis of time series data It is written for researchers and practitioners in the finance industry, academic researchers in economics and finance, and advanced MBA and graduate students in economics and finance Readers are assumed to have a basic knowledge of S-PLUS and a solid grounding in basic statistics and time series concepts Eric Zivot is an associate professor and Gary Waterman Distinguished Scholar in the Economics Department at the University of Washington, and is co-director of the nascent Professional Master's Program in Computational Finance He regularly teaches courses on econometric theory, financial econometrics and time series econometrics, and is the recipient of the He...

14. Clustering Multivariate Time Series Using Hidden Markov Models

Directory of Open Access Journals (Sweden)

Shima Ghassempour

2014-03-01

Full Text Available In this paper we describe an algorithm for clustering multivariate time series with variables taking both categorical and continuous values. Time series of this type are frequent in health care, where they represent the health trajectories of individuals. The problem is challenging because categorical variables make it difficult to define a meaningful distance between trajectories. We propose an approach based on Hidden Markov Models (HMMs, where we first map each trajectory into an HMM, then define a suitable distance between HMMs and finally proceed to cluster the HMMs with a method based on a distance matrix. We test our approach on a simulated, but realistic, data set of 1,255 trajectories of individuals of age 45 and over, on a synthetic validation set with known clustering structure, and on a smaller set of 268 trajectories extracted from the longitudinal Health and Retirement Survey. The proposed method can be implemented quite simply using standard packages in R and Matlab and may be a good candidate for solving the difficult problem of clustering multivariate time series with categorical variables using tools that do not require advanced statistic knowledge, and therefore are accessible to a wide range of researchers.

15. Assimilation of LAI time-series in crop production models

Science.gov (United States)

Kooistra, Lammert; Rijk, Bert; Nannes, Louis

2014-05-01

Agriculture is worldwide a large consumer of freshwater, nutrients and land. Spatial explicit agricultural management activities (e.g., fertilization, irrigation) could significantly improve efficiency in resource use. In previous studies and operational applications, remote sensing has shown to be a powerful method for spatio-temporal monitoring of actual crop status. As a next step, yield forecasting by assimilating remote sensing based plant variables in crop production models would improve agricultural decision support both at the farm and field level. In this study we investigated the potential of remote sensing based Leaf Area Index (LAI) time-series assimilated in the crop production model LINTUL to improve yield forecasting at field level. The effect of assimilation method and amount of assimilated observations was evaluated. The LINTUL-3 crop production model was calibrated and validated for a potato crop on two experimental fields in the south of the Netherlands. A range of data sources (e.g., in-situ soil moisture and weather sensors, destructive crop measurements) was used for calibration of the model for the experimental field in 2010. LAI from cropscan field radiometer measurements and actual LAI measured with the LAI-2000 instrument were used as input for the LAI time-series. The LAI time-series were assimilated in the LINTUL model and validated for a second experimental field on which potatoes were grown in 2011. Yield in 2011 was simulated with an R2 of 0.82 when compared with field measured yield. Furthermore, we analysed the potential of assimilation of LAI into the LINTUL-3 model through the 'updating' assimilation technique. The deviation between measured and simulated yield decreased from 9371 kg/ha to 8729 kg/ha when assimilating weekly LAI measurements in the LINTUL model over the season of 2011. LINTUL-3 furthermore shows the main growth reducing factors, which are useful for farm decision support. The combination of crop models and sensor

16. Time Series

DEFF Research Database (Denmark)

Johansen, Søren

2015-01-01

An overview of results for the cointegrated VAR model for nonstationary I(1) variables is given. The emphasis is on the analysis of the model and the tools for asymptotic inference. These include: formulation of criteria on the parameters, for the process to be nonstationary and I(1), formulation...

17. Degeneracy of time series models: The best model is not always the correct model

International Nuclear Information System (INIS)

Judd, Kevin; Nakamura, Tomomichi

2006-01-01

There are a number of good techniques for finding, in some sense, the best model of a deterministic system given a time series of observations. We examine a problem called model degeneracy, which has the consequence that even when a perfect model of a system exists, one does not find it using the best techniques currently available. The problem is illustrated using global polynomial models and the theory of Groebner bases

18. Time Series

DEFF Research Database (Denmark)

Johansen, Søren

2015-01-01

An overview of results for the cointegrated VAR model for nonstationary I(1) variables is given. The emphasis is on the analysis of the model and the tools for asymptotic inference. These include: formulation of criteria on the parameters, for the process to be nonstationary and I(1), formulation...... of hypotheses of interest on the rank, the cointegrating relations and the adjustment coefficients. A discussion of the asymptotic distribution results that are used for inference. The results are illustrated by a few examples. A number of extensions of the theory are pointed out....

19. a model for nonlinear innovation in time series

African Journals Online (AJOL)

DJFLEX

heteroscedastic errors are common in financial and econometric time series. The conditional variance may be specified as nonlinear autoregressive conditional heteroscedasticity ...... applied econometrics, 8, 31 – 49. Rao, C. R., 1973. Linear statistical inference and its applications, 2nd edition. New york: John Wiley.

20. Predicting Time Series Outputs and Time-to-Failure for an Aircraft Controller Using Bayesian Modeling

Science.gov (United States)

He, Yuning

2015-01-01

Safety of unmanned aerial systems (UAS) is paramount, but the large number of dynamically changing controller parameters makes it hard to determine if the system is currently stable, and the time before loss of control if not. We propose a hierarchical statistical model using Treed Gaussian Processes to predict (i) whether a flight will be stable (success) or become unstable (failure), (ii) the time-to-failure if unstable, and (iii) time series outputs for flight variables. We first classify the current flight input into success or failure types, and then use separate models for each class to predict the time-to-failure and time series outputs. As different inputs may cause failures at different times, we have to model variable length output curves. We use a basis representation for curves and learn the mappings from input to basis coefficients. We demonstrate the effectiveness of our prediction methods on a NASA neuro-adaptive flight control system.

1. Learning restricted Boolean network model by time-series data.

Science.gov (United States)

Ouyang, Hongjia; Fang, Jie; Shen, Liangzhong; Dougherty, Edward R; Liu, Wenbin

2014-01-01

Restricted Boolean networks are simplified Boolean networks that are required for either negative or positive regulations between genes. Higa et al. (BMC Proc 5:S5, 2011) proposed a three-rule algorithm to infer a restricted Boolean network from time-series data. However, the algorithm suffers from a major drawback, namely, it is very sensitive to noise. In this paper, we systematically analyze the regulatory relationships between genes based on the state switch of the target gene and propose an algorithm with which restricted Boolean networks may be inferred from time-series data. We compare the proposed algorithm with the three-rule algorithm and the best-fit algorithm based on both synthetic networks and a well-studied budding yeast cell cycle network. The performance of the algorithms is evaluated by three distance metrics: the normalized-edge Hamming distance [Formula: see text], the normalized Hamming distance of state transition [Formula: see text], and the steady-state distribution distance μ (ssd). Results show that the proposed algorithm outperforms the others according to both [Formula: see text] and [Formula: see text], whereas its performance according to μ (ssd) is intermediate between best-fit and the three-rule algorithms. Thus, our new algorithm is more appropriate for inferring interactions between genes from time-series data.

2. Self-organising mixture autoregressive model for non-stationary time series modelling.

Science.gov (United States)

Ni, He; Yin, Hujun

2008-12-01

Modelling non-stationary time series has been a difficult task for both parametric and nonparametric methods. One promising solution is to combine the flexibility of nonparametric models with the simplicity of parametric models. In this paper, the self-organising mixture autoregressive (SOMAR) network is adopted as a such mixture model. It breaks time series into underlying segments and at the same time fits local linear regressive models to the clusters of segments. In such a way, a global non-stationary time series is represented by a dynamic set of local linear regressive models. Neural gas is used for a more flexible structure of the mixture model. Furthermore, a new similarity measure has been introduced in the self-organising network to better quantify the similarity of time series segments. The network can be used naturally in modelling and forecasting non-stationary time series. Experiments on artificial, benchmark time series (e.g. Mackey-Glass) and real-world data (e.g. numbers of sunspots and Forex rates) are presented and the results show that the proposed SOMAR network is effective and superior to other similar approaches.

3. Extracting the relevant delays in time series modelling

DEFF Research Database (Denmark)

Goutte, Cyril

1997-01-01

selection, and more precisely stepwise forward selection. The method is compared to other forward selection schemes, as well as to a nonparametric tests aimed at estimating the embedding dimension of time series. The final application extends these results to the efficient estimation of FIR filters on some......In this contribution, we suggest a convenient way to use generalisation error to extract the relevant delays from a time-varying process, i.e. the delays that lead to the best prediction performance. We design a generalisation-based algorithm that takes its inspiration from traditional variable...

4. Analysing Stable Time Series

National Research Council Canada - National Science Library

1997-01-01

We describe how to take a stable, ARMA, time series through the various stages of model identification, parameter estimation, and diagnostic checking, and accompany the discussion with a goodly number...

5. SEM Based CARMA Time Series Modeling for Arbitrary N.

Science.gov (United States)

Oud, Johan H L; Voelkle, Manuel C; Driver, Charles C

2018-01-01

This article explains in detail the state space specification and estimation of first and higher-order autoregressive moving-average models in continuous time (CARMA) in an extended structural equation modeling (SEM) context for N = 1 as well as N > 1. To illustrate the approach, simulations will be presented in which a single panel model (T = 41 time points) is estimated for a sample of N = 1,000 individuals as well as for samples of N = 100 and N = 50 individuals, followed by estimating 100 separate models for each of the one-hundred N = 1 cases in the N = 100 sample. Furthermore, we will demonstrate how to test the difference between the full panel model and each N = 1 model by means of a subject-group-reproducibility test. Finally, the proposed analyses will be applied in an empirical example, in which the relationships between mood at work and mood at home are studied in a sample of N = 55 women. All analyses are carried out by ctsem, an R-package for continuous time modeling, interfacing to OpenMx.

6. Bayesian Modelling of fMRI Time Series

DEFF Research Database (Denmark)

Højen-Sørensen, Pedro; Hansen, Lars Kai; Rasmussen, Carl Edward

2000-01-01

We present a Hidden Markov Model (HMM) for inferring the hidden psychological state (or neural activity) during single trial fMRI activation experiments with blocked task paradigms. Inference is based on Bayesian methodology, using a combination of analytical and a variety of Markov Chain Monte...... Carlo (MCMC) sampling techniques. The advantage of this method is that detection of short time learning effects between repeated trials is possible since inference is based only on single trial experiments....

7. A Sandwich-Type Standard Error Estimator of SEM Models with Multivariate Time Series

Science.gov (United States)

Zhang, Guangjian; Chow, Sy-Miin; Ong, Anthony D.

2011-01-01

Structural equation models are increasingly used as a modeling tool for multivariate time series data in the social and behavioral sciences. Standard error estimators of SEM models, originally developed for independent data, require modifications to accommodate the fact that time series data are inherently dependent. In this article, we extend a…

8. A prediction method based on wavelet transform and multiple models fusion for chaotic time series

International Nuclear Information System (INIS)

Zhongda, Tian; Shujiang, Li; Yanhong, Wang; Yi, Sha

2017-01-01

In order to improve the prediction accuracy of chaotic time series, a prediction method based on wavelet transform and multiple models fusion is proposed. The chaotic time series is decomposed and reconstructed by wavelet transform, and approximate components and detail components are obtained. According to different characteristics of each component, least squares support vector machine (LSSVM) is used as predictive model for approximation components. At the same time, an improved free search algorithm is utilized for predictive model parameters optimization. Auto regressive integrated moving average model (ARIMA) is used as predictive model for detail components. The multiple prediction model predictive values are fusion by Gauss–Markov algorithm, the error variance of predicted results after fusion is less than the single model, the prediction accuracy is improved. The simulation results are compared through two typical chaotic time series include Lorenz time series and Mackey–Glass time series. The simulation results show that the prediction method in this paper has a better prediction.

9. On Fire regime modelling using satellite TM time series

Science.gov (United States)

Oddi, F.; . Ghermandi, L.; Lanorte, A.; Lasaponara, R.

2009-04-01

Wildfires can cause an environment deterioration modifying vegetation dynamics because they have the capacity of changing vegetation diversity and physiognomy. In semiarid regions, like the northwestern Patagonia, fire disturbance is also important because it could impact on the potential productivity of the ecosystem. There is reduction plant biomass and with that reducing the animal carrying capacity and/or the forest site quality with negative economics implications. Therefore knowledge of the fires regime in a region is of great importance to understand and predict the responses of vegetation and its possible effect on the regional economy. Studies of this type at a landscape level can be addressed using GIS tools. Satellite imagery allows detect burned areas and through a temporary analysis can be determined to fire regime and detecting changes at landscape scale. The study area of work is located on the east of the city of Bariloche including the San Ramon Ranch (22,000 ha) and its environs in the ecotone formed by the sub Antarctic forest and the patagonian steppe. We worked with multiespectral Landsat TM images and Landsat ETM + 30m spatial resolution obtained at different times. For the spatial analysis we used the software Erdas Imagine 9.0 and ArcView 3.3. A discrimination of vegetation types has made and was determined areas affected by fires in different years. We determined the level of change on vegetation induced by fire. In the future the use of high spatial resolution images combined with higher spectral resolution will allows distinguish burned areas with greater precision on study area. Also the use of digital terrain models derived from satellite imagery associated with climatic variables will allows model the relationship between them and the dynamics of vegetation.

10. time series modeling of daily abandoned calls in a call centre

African Journals Online (AJOL)

DJFLEX

Models for evaluating and predicting the short periodic time series in daily abandoned calls in a call center are developed. Abandonment of calls due to impatient is an identified problem among most call centers. The two competing models were derived using Fourier series and the Box and Jenkins modeling approaches.

11. Time series modeling of daily abandoned calls in a call centre ...

African Journals Online (AJOL)

Models for evaluating and predicting the short periodic time series in daily abandoned calls in a call center are developed. Abandonment of calls due to impatient is an identified problem among most call centers. The two competing models were derived using Fourier series and the Box and Jenkins modeling approaches.

12. Fitting ARMA Time Series by Structural Equation Models.

Science.gov (United States)

van Buuren, Stef

1997-01-01

This paper outlines how the stationary ARMA (p,q) model (G. Box and G. Jenkins, 1976) can be specified as a structural equation model. Maximum likelihood estimates for the parameters in the ARMA model can be obtained by software for fitting structural equation models. The method is applied to three problem types. (SLD)

13. Modeling Large Time Series for Efficient Approximate Query Processing

DEFF Research Database (Denmark)

Perera, Kasun S; Hahmann, Martin; Lehner, Wolfgang

2015-01-01

-wise aggregation to derive the models. These models are initially created from the original data and are kept in the database along with it. Subsequent queries are answered using the stored models rather than scanning and processing the original datasets. In order to support model query processing, we maintain...

14. A generalized exponential time series regression model for electricity prices

DEFF Research Database (Denmark)

Haldrup, Niels; Knapik, Oskar; Proietti, Tomasso

on the estimated model, the best linear predictor is constructed. Our modeling approach provides good fit within sample and outperforms competing benchmark predictors in terms of forecasting accuracy. We also find that building separate models for each hour of the day and averaging the forecasts is a better...

15. Time Series Modelling of Syphilis Incidence in China from 2005 to 2012

Science.gov (United States)

Zhang, Xingyu; Zhang, Tao; Pei, Jiao; Liu, Yuanyuan; Li, Xiaosong; Medrano-Gracia, Pau

2016-01-01

Background The infection rate of syphilis in China has increased dramatically in recent decades, becoming a serious public health concern. Early prediction of syphilis is therefore of great importance for heath planning and management. Methods In this paper, we analyzed surveillance time series data for primary, secondary, tertiary, congenital and latent syphilis in mainland China from 2005 to 2012. Seasonality and long-term trend were explored with decomposition methods. Autoregressive integrated moving average (ARIMA) was used to fit a univariate time series model of syphilis incidence. A separate multi-variable time series for each syphilis type was also tested using an autoregressive integrated moving average model with exogenous variables (ARIMAX). Results The syphilis incidence rates have increased three-fold from 2005 to 2012. All syphilis time series showed strong seasonality and increasing long-term trend. Both ARIMA and ARIMAX models fitted and estimated syphilis incidence well. All univariate time series showed highest goodness-of-fit results with the ARIMA(0,0,1)×(0,1,1) model. Conclusion Time series analysis was an effective tool for modelling the historical and future incidence of syphilis in China. The ARIMAX model showed superior performance than the ARIMA model for the modelling of syphilis incidence. Time series correlations existed between the models for primary, secondary, tertiary, congenital and latent syphilis. PMID:26901682

16. Time series analysis

CERN Document Server

2007-01-01

""In this book the author gives a detailed account of estimation, identification methodologies for univariate and multivariate stationary time-series models. The interesting aspect of this introductory book is that it contains several real data sets and the author made an effort to explain and motivate the methodology with real data. … this introductory book will be interesting and useful not only to undergraduate students in the UK universities but also to statisticians who are keen to learn time-series techniques and keen to apply them. I have no hesitation in recommending the book.""-Journa

17. Mixed Portmanteau Test for Diagnostic Checking of Time Series Models

Directory of Open Access Journals (Sweden)

Sohail Chand

2014-01-01

Full Text Available Model criticism is an important stage of model building and thus goodness of fit tests provides a set of tools for diagnostic checking of the fitted model. Several tests are suggested in literature for diagnostic checking. These tests use autocorrelation or partial autocorrelation in the residuals to criticize the adequacy of fitted model. The main idea underlying these portmanteau tests is to identify if there is any dependence structure which is yet unexplained by the fitted model. In this paper, we suggest mixed portmanteau tests based on autocorrelation and partial autocorrelation functions of the residuals. We derived the asymptotic distribution of the mixture test and studied its size and power using Monte Carlo simulations.

18. A Feature Fusion Based Forecasting Model for Financial Time Series

Science.gov (United States)

Guo, Zhiqiang; Wang, Huaiqing; Liu, Quan; Yang, Jie

2014-01-01

Predicting the stock market has become an increasingly interesting research area for both researchers and investors, and many prediction models have been proposed. In these models, feature selection techniques are used to pre-process the raw data and remove noise. In this paper, a prediction model is constructed to forecast stock market behavior with the aid of independent component analysis, canonical correlation analysis, and a support vector machine. First, two types of features are extracted from the historical closing prices and 39 technical variables obtained by independent component analysis. Second, a canonical correlation analysis method is utilized to combine the two types of features and extract intrinsic features to improve the performance of the prediction model. Finally, a support vector machine is applied to forecast the next day's closing price. The proposed model is applied to the Shanghai stock market index and the Dow Jones index, and experimental results show that the proposed model performs better in the area of prediction than other two similar models. PMID:24971455

19. Visual time series analysis

DEFF Research Database (Denmark)

Fischer, Paul; Hilbert, Astrid

2012-01-01

We introduce a platform which supplies an easy-to-handle, interactive, extendable, and fast analysis tool for time series analysis. In contrast to other software suits like Maple, Matlab, or R, which use a command-line-like interface and where the user has to memorize/look-up the appropriate...... commands, our application is select-and-click-driven. It allows to derive many different sequences of deviations for a given time series and to visualize them in different ways in order to judge their expressive power and to reuse the procedure found. For many transformations or model-ts, the user may...... choose between manual and automated parameter selection. The user can dene new transformations and add them to the system. The application contains efficient implementations of advanced and recent techniques for time series analysis including techniques related to extreme value analysis and filtering...

20. Applying Time Series Analysis Model to Temperature Data in Greenhouses

Directory of Open Access Journals (Sweden)

Abdelhafid Hasni

2011-03-01

Full Text Available The objective of the research is to find an appropriate Seasonal Auto-Regressive Integrated Moving Average (SARIMA Model for fitting the inside air temperature (Tin of a naturally ventilated greenhouse under Mediterranean conditions by considering the minimum of Akaike Information Criterion (AIC. The results of fitting were as follows: the best SARIMA Model for fitting air temperature of greenhouse is SARIMA (1,0,0 (1,0,224.

1. Model of a synthetic wind speed time series generator

DEFF Research Database (Denmark)

Negra, N.B.; Holmstrøm, O.; Bak-Jensen, B.

2008-01-01

is described and some statistical issues (seasonal characteristics, autocorrelation functions, average values and distribution functions) are used for verification. The output of the model has been designed as input for sequential Monte Carlo simulation; however, it is expected that it can be used for other...... of the main elements to consider for this purpose is the model of the wind speed that is usually required as input. Wind speed measurements may represent a solution for this problem, but, for techniques such as sequential Monte Carlo simulation, they have to be long enough in order to describe a wide range...

2. Modeling BAS Dysregulation in Bipolar Disorder : Illustrating the Potential of Time Series Analysis

NARCIS (Netherlands)

Hamaker, Ellen L.; Grasman, Raoul P P P; Kamphuis, Jan Henk

2016-01-01

Time series analysis is a technique that can be used to analyze the data from a single subject and has great potential to investigate clinically relevant processes like affect regulation. This article uses time series models to investigate the assumed dysregulation of affect that is associated with

3. Bayesian Modelling of fMRI Time Series

DEFF Research Database (Denmark)

Højen-Sørensen, Pedro; Hansen, Lars Kai; Rasmussen, Carl Edward

2000-01-01

We present a Hidden Markov Model (HMM) for inferring the hidden psychological state (or neural activity) during single trial fMRI activation experiments with blocked task paradigms. Inference is based on Bayesian methodology, using a combination of analytical and a variety of Markov Chain Monte...

4. Linear models for multivariate, time series, and spatial data

CERN Document Server

Christensen, Ronald

1991-01-01

This is a companion volume to Plane Answers to Complex Questions: The Theory 0/ Linear Models. It consists of six additional chapters written in the same spirit as the last six chapters of the earlier book. Brief introductions are given to topics related to linear model theory. No attempt is made to give a comprehensive treatment of the topics. Such an effort would be futile. Each chapter is on a topic so broad that an in depth discussion would require a book-Iength treatment. People need to impose structure on the world in order to understand it. There is a limit to the number of unrelated facts that anyone can remem­ ber. If ideas can be put within a broad, sophisticatedly simple structure, not only are they easier to remember but often new insights become avail­ able. In fact, sophisticatedly simple models of the world may be the only ones that work. I have often heard Arnold Zellner say that, to the best of his knowledge, this is true in econometrics. The process of modeling is fundamental to understand...

5. Spatially adaptive mixture modeling for analysis of FMRI time series.

Science.gov (United States)

Vincent, Thomas; Risser, Laurent; Ciuciu, Philippe

2010-04-01

Within-subject analysis in fMRI essentially addresses two problems, the detection of brain regions eliciting evoked activity and the estimation of the underlying dynamics. In Makni et aL, 2005 and Makni et aL, 2008, a detection-estimation framework has been proposed to tackle these problems jointly, since they are connected to one another. In the Bayesian formalism, detection is achieved by modeling activating and nonactivating voxels through independent mixture models (IMM) within each region while hemodynamic response estimation is performed at a regional scale in a nonparametric way. Instead of IMMs, in this paper we take advantage of spatial mixture models (SMM) for their nonlinear spatial regularizing properties. The proposed method is unsupervised and spatially adaptive in the sense that the amount of spatial correlation is automatically tuned from the data and this setting automatically varies across brain regions. In addition, the level of regularization is specific to each experimental condition since both the signal-to-noise ratio and the activation pattern may vary across stimulus types in a given brain region. These aspects require the precise estimation of multiple partition functions of underlying Ising fields. This is addressed efficiently using first path sampling for a small subset of fields and then using a recently developed fast extrapolation technique for the large remaining set. Simulation results emphasize that detection relying on supervised SMM outperforms its IMM counterpart and that unsupervised spatial mixture models achieve similar results without any hand-tuning of the correlation parameter. On real datasets, the gain is illustrated in a localizer fMRI experiment: brain activations appear more spatially resolved using SMM in comparison with classical general linear model (GLM)-based approaches, while estimating a specific parcel-based HRF shape. Our approach therefore validates the treatment of unsmoothed fMRI data without fixed GLM

6. On the Practice of Bayesian Inference in Basic Economic Time Series Models using Gibbs Sampling

NARCIS (Netherlands)

M.D. de Pooter (Michiel); R. Segers (René); H.K. van Dijk (Herman)

2006-01-01

textabstractSeveral lessons learned from a Bayesian analysis of basic economic time series models by means of the Gibbs sampling algorithm are presented. Models include the Cochrane-Orcutt model for serial correlation, the Koyck distributed lag model, the Unit Root model, the Instrumental Variables

7. Real-time GPS Satellite Clock Error Prediction Based On No-stationary Time Series Model

Science.gov (United States)

Wang, Q.; Xu, G.; Wang, F.

2009-04-01

Analysis Centers of the IGS provide precise satellite ephemeris for GPS data post-processing. The accuracy of orbit products is better than 5cm, and that of the satellite clock errors (SCE) approaches 0.1ns (igscb.jpl.nasa.gov), which can meet with the requirements of precise point positioning (PPP). Due to the 13 day-latency of the IGS final products, only the broadcast ephemeris and IGS ultra rapid products (predicted) are applicable for real time PPP (RT-PPP). Therefore, development of an approach to estimate high precise GPS SCE in real time is of particular importance for RT-PPP. Many studies have been carried out for forecasting the corrections using models, such as Linear Model (LM), Quadratic Polynomial Model (QPM), Quadratic Polynomial Model with Cyclic corrected Terms (QPM+CT), Grey Model (GM) and Kalman Filter Model (KFM), etc. However, the precisions of these models are generally in nanosecond level. The purpose of this study is to develop a method using which SCE forecasting for RT-PPP can be reached with a precision of sub-nanosecond. Analysis of the last 8 years IGS SCE data shown that predicted precision depend on the stability of the individual satellite clock. The clocks of the most recent GPS satellites (BLOCK IIR and BLOCK IIR-M) are more stable than that of the former GPS satellites (BLOCK IIA). For the stable satellite clock, the next 6 hours SCE can be easily predict with LM. The residuals of unstable satellite clocks are periodic ones with noise components. Dominant periods of residuals are found by using Fourier Transform and Spectrum Analysis. For the rest part of the residuals, an auto-regression model is used to determine their systematic trends. Summarized from this study, a no-stationary time series model can be proposed to predict GPS SCE in real time. This prediction model includes: linear term, cyclic corrected terms and auto-regression term, which are used to represent SCE trend, cyclic parts and rest of the errors, respectively

8. Algorithms for global total least squares modelling of finite multivariable time series

NARCIS (Netherlands)

Roorda, Berend

1995-01-01

In this paper we present several algorithms related to the global total least squares (GTLS) modelling of multivariable time series observed over a finite time interval. A GTLS model is a linear, time-invariant finite-dimensional system with a behaviour that has minimal Frobenius distance to a given

9. Travel Cost Inference from Sparse, Spatio-Temporally Correlated Time Series Using Markov Models

DEFF Research Database (Denmark)

Yang, Bin; Guo, Chenjuan; Jensen, Christian S.

2013-01-01

of such time series offers insight into the underlying system and enables prediction of system behavior. While the techniques presented in the paper apply more generally, we consider the case of transportation systems and aim to predict travel cost from GPS tracking data from probe vehicles. Specifically, each...... road segment has an associated travel-cost time series, which is derived from GPS data. We use spatio-temporal hidden Markov models (STHMM) to model correlations among different traffic time series. We provide algorithms that are able to learn the parameters of an STHMM while contending...... with the sparsity, spatio-temporal correlation, and heterogeneity of the time series. Using the resulting STHMM, near future travel costs in the transportation network, e.g., travel time or greenhouse gas emissions, can be inferred, enabling a variety of routing services, e.g., eco-routing. Empirical studies...

10. Travel cost inference from sparse, spatio-temporally correlated time series using markov models

DEFF Research Database (Denmark)

Yang, B.; Guo, C.; Jensen, C.S.

2013-01-01

of such time series offers insight into the underlying system and enables prediction of system behavior. While the techniques presented in the paper apply more generally, we consider the case of transportation systems and aim to predict travel cost from GPS tracking data from probe vehicles. Specifically, each...... road segment has an associated travel-cost time series, which is derived from GPS data. We use spatio-temporal hidden Markov models (STHMM) to model correlations among different traffic time series. We provide algorithms that are able to learn the parameters of an STHMM while contending...... with the sparsity, spatio-temporal correlation, and heterogeneity of the time series. Using the resulting STHMM, near future travel costs in the transportation network, e.g., travel time or greenhouse gas emissions, can be inferred, enabling a variety of routing services, e.g., eco-routing. Empirical studies...

11. Sensitivity analysis of machine-learning models of hydrologic time series

Science.gov (United States)

O'Reilly, A. M.

2017-12-01

Sensitivity analysis traditionally has been applied to assessing model response to perturbations in model parameters, where the parameters are those model input variables adjusted during calibration. Unlike physics-based models where parameters represent real phenomena, the equivalent of parameters for machine-learning models are simply mathematical "knobs" that are automatically adjusted during training/testing/verification procedures. Thus the challenge of extracting knowledge of hydrologic system functionality from machine-learning models lies in their very nature, leading to the label "black box." Sensitivity analysis of the forcing-response behavior of machine-learning models, however, can provide understanding of how the physical phenomena represented by model inputs affect the physical phenomena represented by model outputs.As part of a previous study, hybrid spectral-decomposition artificial neural network (ANN) models were developed to simulate the observed behavior of hydrologic response contained in multidecadal datasets of lake water level, groundwater level, and spring flow. Model inputs used moving window averages (MWA) to represent various frequencies and frequency-band components of time series of rainfall and groundwater use. Using these forcing time series, the MWA-ANN models were trained to predict time series of lake water level, groundwater level, and spring flow at 51 sites in central Florida, USA. A time series of sensitivities for each MWA-ANN model was produced by perturbing forcing time-series and computing the change in response time-series per unit change in perturbation. Variations in forcing-response sensitivities are evident between types (lake, groundwater level, or spring), spatially (among sites of the same type), and temporally. Two generally common characteristics among sites are more uniform sensitivities to rainfall over time and notable increases in sensitivities to groundwater usage during significant drought periods.

12. Research on power grid loss prediction model based on Granger causality property of time series

Energy Technology Data Exchange (ETDEWEB)

Wang, J. [North China Electric Power Univ., Beijing (China); State Grid Corp., Beijing (China); Yan, W.P.; Yuan, J. [North China Electric Power Univ., Beijing (China); Xu, H.M.; Wang, X.L. [State Grid Information and Telecommunications Corp., Beijing (China)

2009-03-11

This paper described a method of predicting power transmission line losses using the Granger causality property of time series. The stable property of the time series was investigated using unit root tests. The Granger causality relationship between line losses and other variables was then determined. Granger-caused time series were then used to create the following 3 prediction models: (1) a model based on line loss binomials that used electricity sales to predict variables, (2) a model that considered both power sales and grid capacity, and (3) a model based on autoregressive distributed lag (ARDL) approaches that incorporated both power sales and the square of power sales as variables. A case study of data from China's electric power grid between 1980 and 2008 was used to evaluate model performance. Results of the study showed that the model error rates ranged between 2.7 and 3.9 percent. 6 refs., 3 tabs., 1 fig.

13. The Exponential Model for the Spectrum of a Time Series: Extensions and Applications

DEFF Research Database (Denmark)

Proietti, Tommaso; Luati, Alessandra

The exponential model for the spectrum of a time series and its fractional extensions are based on the Fourier series expansion of the logarithm of the spectral density. The coefficients of the expansion form the cepstrum of the time series. After deriving the cepstrum of important classes of time...... to the log-spectrum. We then propose two extensions. The first deals with replacing the logarithmic link with a more general Box-Cox link, which encompasses also the identity and the inverse links: this enables nesting alternative spectral estimation methods (autoregressive, exponential, etc.) under the same...

14. A probabilistic method for constructing wave time-series at inshore locations using model scenarios

Science.gov (United States)

Long, Joseph W.; Plant, Nathaniel G.; Dalyander, P. Soupy; Thompson, David M.

2014-01-01

Continuous time-series of wave characteristics (height, period, and direction) are constructed using a base set of model scenarios and simple probabilistic methods. This approach utilizes an archive of computationally intensive, highly spatially resolved numerical wave model output to develop time-series of historical or future wave conditions without performing additional, continuous numerical simulations. The archive of model output contains wave simulations from a set of model scenarios derived from an offshore wave climatology. Time-series of wave height, period, direction, and associated uncertainties are constructed at locations included in the numerical model domain. The confidence limits are derived using statistical variability of oceanographic parameters contained in the wave model scenarios. The method was applied to a region in the northern Gulf of Mexico and assessed using wave observations at 12 m and 30 m water depths. Prediction skill for significant wave height is 0.58 and 0.67 at the 12 m and 30 m locations, respectively, with similar performance for wave period and direction. The skill of this simplified, probabilistic time-series construction method is comparable to existing large-scale, high-fidelity operational wave models but provides higher spatial resolution output at low computational expense. The constructed time-series can be developed to support a variety of applications including climate studies and other situations where a comprehensive survey of wave impacts on the coastal area is of interest.

15. Nonlinearity, Breaks, and Long-Range Dependence in Time-Series Models

DEFF Research Database (Denmark)

Hillebrand, Eric Tobias; Medeiros, Marcelo C.

We study the simultaneous occurrence of long memory and nonlinear effects, such as parameter changes and threshold effects, in ARMA time series models and apply our modeling framework to daily realized volatility. Asymptotic theory for parameter estimation is developed and two model building...

16. Factor models in high-dimensional time series : A time-domain approach

NARCIS (Netherlands)

Hallin, M.; Lippi, M.

2013-01-01

High-dimensional time series may well be the most common type of dataset in the so-called “big data” revolution, and have entered current practice in many areas, including meteorology, genomics, chemometrics, connectomics, complex physics simulations, biological and environmental research, finance

17. Biomedical time series clustering based on non-negative sparse coding and probabilistic topic model.

Science.gov (United States)

Wang, Jin; Liu, Ping; F H She, Mary; Nahavandi, Saeid; Kouzani, Abbas

2013-09-01

Biomedical time series clustering that groups a set of unlabelled temporal signals according to their underlying similarity is very useful for biomedical records management and analysis such as biosignals archiving and diagnosis. In this paper, a new framework for clustering of long-term biomedical time series such as electrocardiography (ECG) and electroencephalography (EEG) signals is proposed. Specifically, local segments extracted from the time series are projected as a combination of a small number of basis elements in a trained dictionary by non-negative sparse coding. A Bag-of-Words (BoW) representation is then constructed by summing up all the sparse coefficients of local segments in a time series. Based on the BoW representation, a probabilistic topic model that was originally developed for text document analysis is extended to discover the underlying similarity of a collection of time series. The underlying similarity of biomedical time series is well captured attributing to the statistic nature of the probabilistic topic model. Experiments on three datasets constructed from publicly available EEG and ECG signals demonstrates that the proposed approach achieves better accuracy than existing state-of-the-art methods, and is insensitive to model parameters such as length of local segments and dictionary size. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

18. Hybrid model for forecasting time series with trend, seasonal and salendar variation patterns

Science.gov (United States)

Suhartono; Rahayu, S. P.; Prastyo, D. D.; Wijayanti, D. G. P.; Juliyanto

2017-09-01

Most of the monthly time series data in economics and business in Indonesia and other Moslem countries not only contain trend and seasonal, but also affected by two types of calendar variation effects, i.e. the effect of the number of working days or trading and holiday effects. The purpose of this research is to develop a hybrid model or a combination of several forecasting models to predict time series that contain trend, seasonal and calendar variation patterns. This hybrid model is a combination of classical models (namely time series regression and ARIMA model) and/or modern methods (artificial intelligence method, i.e. Artificial Neural Networks). A simulation study was used to show that the proposed procedure for building the hybrid model could work well for forecasting time series with trend, seasonal and calendar variation patterns. Furthermore, the proposed hybrid model is applied for forecasting real data, i.e. monthly data about inflow and outflow of currency at Bank Indonesia. The results show that the hybrid model tend to provide more accurate forecasts than individual forecasting models. Moreover, this result is also in line with the third results of the M3 competition, i.e. the hybrid model on average provides a more accurate forecast than the individual model.

19. Bayesian dynamic modeling of time series of dengue disease case counts.

Science.gov (United States)

Martínez-Bello, Daniel Adyro; López-Quílez, Antonio; Torres-Prieto, Alexander

2017-07-01

The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model's short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful

20. Bayesian dynamic modeling of time series of dengue disease case counts.

Directory of Open Access Journals (Sweden)

2017-07-01

Full Text Available The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model's short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease

1. TIME SERIES MODELS OF THREE SETS OF RXTE OBSERVATIONS OF 4U 1543–47

International Nuclear Information System (INIS)

Koen, C.

2013-01-01

The X-ray nova 4U 1543–47 was in a different physical state (low/hard, high/soft, and very high) during the acquisition of each of the three time series analyzed in this paper. Standard time series models of the autoregressive moving average (ARMA) family are fitted to these series. The low/hard data can be adequately modeled by a simple low-order model with fixed coefficients, once the slowly varying mean count rate has been accounted for. The high/soft series requires a higher order model, or an ARMA model with variable coefficients. The very high state is characterized by a succession of 'dips', with roughly equal depths. These seem to appear independently of one another. The underlying stochastic series can again be modeled by an ARMA form, or roughly as the sum of an ARMA series and white noise. The structuring of each model in terms of short-lived aperiodic and 'quasi-periodic' components is discussed.

2. A Four-Stage Hybrid Model for Hydrological Time Series Forecasting

Science.gov (United States)

Di, Chongli; Yang, Xiaohua; Wang, Xiaochao

2014-01-01

Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of ‘denoising, decomposition and ensemble’. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models. PMID:25111782

3. Fluctuation complexity of agent-based financial time series model by stochastic Potts system

Science.gov (United States)

Hong, Weijia; Wang, Jun

2015-03-01

Financial market is a complex evolved dynamic system with high volatilities and noises, and the modeling and analyzing of financial time series are regarded as the rather challenging tasks in financial research. In this work, by applying the Potts dynamic system, a random agent-based financial time series model is developed in an attempt to uncover the empirical laws in finance, where the Potts model is introduced to imitate the trading interactions among the investing agents. Based on the computer simulation in conjunction with the statistical analysis and the nonlinear analysis, we present numerical research to investigate the fluctuation behaviors of the proposed time series model. Furthermore, in order to get a robust conclusion, we consider the daily returns of Shanghai Composite Index and Shenzhen Component Index, and the comparison analysis of return behaviors between the simulation data and the actual data is exhibited.

4. MODELLING INTERNATIONAL OILSEED PRICES: AN APPLICATION OF THE STRUCTURAL TIME SERIES MODEL

Directory of Open Access Journals (Sweden)

Jaweriah Hazrana

2017-04-01

Full Text Available The fundamentals characterizing agricultural commodity prices have often been debated in research and policy circles. Building on limitations in the existing literature, the present study conducts an integrated test and empirically analyses the international price of palm and soybean oil from 1960(1 to 2016(8. For this purpose the univariate Structural Time Series Model based on the state space framework is applied. This approach allows flexibility to model complex stochastic movements, seasonality, cyclical patterns and incorporate intervention analysis. Estimation is based on the Maximum Likelihood method via the Kalman Filter. The results establish that both series exhibit a stochastic long term trend punctuated by multiple breaks. The findings also uncover the presence of cyclicality which results in price swings of varying duration and amplitude. The model works well as a description of oilseed prices and improves awareness of their separate structural components. These are fundamental to design country and commodity specific policy strategies and respond to volatile market conditions. The results underscore that contrary to previous price spikes most of the drivers of the mid 2000s price spikes are structural and on the demand side. These new drivers in oilseed markets suggest the possibility of fundamental change in price behaviour with longer-lasting effects

5. Applying ARIMA model for annual volume time series of the Magdalena River

Directory of Open Access Journals (Sweden)

Gloria Amaris

2017-04-01

Conclusions: The simulated results obtained with the ARIMA model compared to the observed data showed a fairly good adjustment of the minimum and maximum magnitudes. This allows concluding that it is a good tool for estimating minimum and maximum volumes, even though this model is not capable of simulating the exact behaviour of an annual volume time series.

6. Modeling Financial Time Series Based on a Market Microstructure Model with Leverage Effect

Directory of Open Access Journals (Sweden)

Yanhui Xi

2016-01-01

Full Text Available The basic market microstructure model specifies that the price/return innovation and the volatility innovation are independent Gaussian white noise processes. However, the financial leverage effect has been found to be statistically significant in many financial time series. In this paper, a novel market microstructure model with leverage effects is proposed. The model specification assumed a negative correlation in the errors between the price/return innovation and the volatility innovation. With the new representations, a theoretical explanation of leverage effect is provided. Simulated data and daily stock market indices (Shanghai composite index, Shenzhen component index, and Standard and Poor’s 500 Composite index via Bayesian Markov Chain Monte Carlo (MCMC method are used to estimate the leverage market microstructure model. The results verify the effectiveness of the model and its estimation approach proposed in the paper and also indicate that the stock markets have strong leverage effects. Compared with the classical leverage stochastic volatility (SV model in terms of DIC (Deviance Information Criterion, the leverage market microstructure model fits the data better.

7. A spatial time series framework for modeling daily precipitationat regional scales

Energy Technology Data Exchange (ETDEWEB)

Kyriakidis, Phaedon C.; Miller, Norman L.; Kim, Jinwon

2001-11-14

In this paper, a framework for stochastic spatiotemporal modeling of daily precipitation in a hindcast mode is presented. Observed precipitation levels in space and time are modeled as a joint realization of a collection of space-indexed time series, one for each spatial location. Time series model parameters are spatially varying, thus capturing space-time interactions. Stochastic simulation, i.e., the procedure of generating alternative precipitation realizations (synthetic fields) over the space-time domain of interest (Deutsch and Journel, 1998), is employed for ensemble prediction. The simulated daily precipitation fields reproduce a data-based histogram and spatiotemporal covariance model, and identify the measured precipitation values at the rain gauges (conditional simulation). Such synthetic precipitation fields can be used in a Monte Carlo framework for risk analysis studies in hydrologic impact assessment investigations.

8. Long Memory of Financial Time Series and Hidden Markov Models with Time-Varying Parameters

DEFF Research Database (Denmark)

Nystrup, Peter; Madsen, Henrik; Lindström, Erik

2016-01-01

Hidden Markov models are often used to model daily returns and to infer the hidden state of financial markets. Previous studies have found that the estimated models change over time, but the implications of the time-varying behavior have not been thoroughly examined. This paper presents an adaptive...... estimation approach that allows for the parameters of the estimated models to be time varying. It is shown that a two-state Gaussian hidden Markov model with time-varying parameters is able to reproduce the long memory of squared daily returns that was previously believed to be the most difficult fact...... to reproduce with a hidden Markov model. Capturing the time-varying behavior of the parameters also leads to improved one-step density forecasts. Finally, it is shown that the forecasting performance of the estimated models can be further improved using local smoothing to forecast the parameter variations....

9. Effective low-order models for atmospheric dynamics and time series analysis.

Science.gov (United States)

2016-02-01

The paper focuses on two interrelated problems: developing physically sound low-order models (LOMs) for atmospheric dynamics and employing them as novel time-series models to overcome deficiencies in current atmospheric time series analysis. The first problem is warranted since arbitrary truncations in the Galerkin method (commonly used to derive LOMs) may result in LOMs that violate fundamental conservation properties of the original equations, causing unphysical behaviors such as unbounded solutions. In contrast, the LOMs we offer (G-models) are energy conserving, and some retain the Hamiltonian structure of the original equations. This work examines LOMs from recent publications to show that all of them that are physically sound can be converted to G-models, while those that cannot lack energy conservation. Further, motivated by recent progress in statistical properties of dynamical systems, we explore G-models for a new role of atmospheric time series models as their data generating mechanisms are well in line with atmospheric dynamics. Currently used time series models, however, do not specifically utilize the physics of the governing equations and involve strong statistical assumptions rarely met in real data.

10. A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress

Directory of Open Access Journals (Sweden)

Ching-Hsue Cheng

2018-01-01

Full Text Available The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i the proposed model is different from the previous models lacking the concept of time series; (ii the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies.

11. Modeling and Forecasting of Water Demand in Isfahan Using Underlying Trend Concept and Time Series

Directory of Open Access Journals (Sweden)

2016-02-01

Full Text Available Introduction: Accurate water demand modeling for the city is very important for forecasting and policies adoption related to water resources management. Thus, for future requirements of water estimation, forecasting and modeling, it is important to utilize models with little errors. Water has a special place among the basic human needs, because it not hampers human life. The importance of the issue of water management in the extraction and consumption, it is necessary as a basic need. Municipal water applications is include a variety of water demand for domestic, public, industrial and commercial. Predicting the impact of urban water demand in better planning of water resources in arid and semiarid regions are faced with water restrictions. Materials and Methods: One of the most important factors affecting the changing technological advances in production and demand functions, we must pay special attention to the layout pattern. Technology development is concerned not only technically, but also other aspects such as personal, non-economic factors (population, geographical and social factors can be analyzed. Model examined in this study, a regression model is composed of a series of structural components over time allows changed invisible accidentally. Explanatory variables technology (both crystalline and amorphous in a model according to which the material is said to be better, but because of the lack of measured variables over time can not be entered in the template. Model examined in this study, a regression model is composed of a series of structural component invisible accidentally changed over time allows. In this study, structural time series (STSM and ARMA time series models have been used to model and estimate the water demand in Isfahan. Moreover, in order to find the efficient procedure, both models have been compared to each other. The desired data in this research include water consumption in Isfahan, water price and the monthly pay

12. Markov Chain Modelling for Short-Term NDVI Time Series Forecasting

Directory of Open Access Journals (Sweden)

Stepčenko Artūrs

2016-12-01

Full Text Available In this paper, the NDVI time series forecasting model has been developed based on the use of discrete time, continuous state Markov chain of suitable order. The normalised difference vegetation index (NDVI is an indicator that describes the amount of chlorophyll (the green mass and shows the relative density and health of vegetation; therefore, it is an important variable for vegetation forecasting. A Markov chain is a stochastic process that consists of a state space. This stochastic process undergoes transitions from one state to another in the state space with some probabilities. A Markov chain forecast model is flexible in accommodating various forecast assumptions and structures. The present paper discusses the considerations and techniques in building a Markov chain forecast model at each step. Continuous state Markov chain model is analytically described. Finally, the application of the proposed Markov chain model is illustrated with reference to a set of NDVI time series data.

13. Nonlinear time series modeling and forecasting the seismic data of the Hindu Kush region

Science.gov (United States)

2017-11-01

In this study, we extended the application of linear and nonlinear time models in the field of earthquake seismology and examined the out-of-sample forecast accuracy of linear Autoregressive (AR), Autoregressive Conditional Duration (ACD), Self-Exciting Threshold Autoregressive (SETAR), Threshold Autoregressive (TAR), Logistic Smooth Transition Autoregressive (LSTAR), Additive Autoregressive (AAR), and Artificial Neural Network (ANN) models for seismic data of the Hindu Kush region. We also extended the previous studies by using Vector Autoregressive (VAR) and Threshold Vector Autoregressive (TVAR) models and compared their forecasting accuracy with linear AR model. Unlike previous studies that typically consider the threshold model specifications by using internal threshold variable, we specified these models with external transition variables and compared their out-of-sample forecasting performance with the linear benchmark AR model. The modeling results show that time series models used in the present study are capable of capturing the dynamic structure present in the seismic data. The point forecast results indicate that the AR model generally outperforms the nonlinear models. However, in some cases, threshold models with external threshold variables specification produce more accurate forecasts, indicating that specification of threshold time series models is of crucial importance. For raw seismic data, the ACD model does not show an improved out-of-sample forecasting performance over the linear AR model. The results indicate that the AR model is the best forecasting device to model and forecast the raw seismic data of the Hindu Kush region.

14. Nonlinear time series modeling and forecasting the seismic data of the Hindu Kush region

Science.gov (United States)

2018-01-01

In this study, we extended the application of linear and nonlinear time models in the field of earthquake seismology and examined the out-of-sample forecast accuracy of linear Autoregressive (AR), Autoregressive Conditional Duration (ACD), Self-Exciting Threshold Autoregressive (SETAR), Threshold Autoregressive (TAR), Logistic Smooth Transition Autoregressive (LSTAR), Additive Autoregressive (AAR), and Artificial Neural Network (ANN) models for seismic data of the Hindu Kush region. We also extended the previous studies by using Vector Autoregressive (VAR) and Threshold Vector Autoregressive (TVAR) models and compared their forecasting accuracy with linear AR model. Unlike previous studies that typically consider the threshold model specifications by using internal threshold variable, we specified these models with external transition variables and compared their out-of-sample forecasting performance with the linear benchmark AR model. The modeling results show that time series models used in the present study are capable of capturing the dynamic structure present in the seismic data. The point forecast results indicate that the AR model generally outperforms the nonlinear models. However, in some cases, threshold models with external threshold variables specification produce more accurate forecasts, indicating that specification of threshold time series models is of crucial importance. For raw seismic data, the ACD model does not show an improved out-of-sample forecasting performance over the linear AR model. The results indicate that the AR model is the best forecasting device to model and forecast the raw seismic data of the Hindu Kush region.

15. Long memory of financial time series and hidden Markov models with time-varying parameters

DEFF Research Database (Denmark)

Nystrup, Peter; Madsen, Henrik; Lindström, Erik

Hidden Markov models are often used to capture stylized facts of daily returns and to infer the hidden state of financial markets. Previous studies have found that the estimated models change over time, but the implications of the time-varying behavior for the ability to reproduce the stylized...... facts have not been thoroughly examined. This paper presents an adaptive estimation approach that allows for the parameters of the estimated models to be time-varying. It is shown that a two-state Gaussian hidden Markov model with time-varying parameters is able to reproduce the long memory of squared...... daily returns that was previously believed to be the most difficult fact to reproduce with a hidden Markov model. Capturing the time-varying behavior of the parameters also leads to improved one-step predictions....

16. Time Series Momentum

DEFF Research Database (Denmark)

Moskowitz, Tobias J.; Ooi, Yao Hua; Heje Pedersen, Lasse

2012-01-01

We document significant “time series momentum” in equity index, currency, commodity, and bond futures for each of the 58 liquid instruments we consider. We find persistence in returns for one to 12 months that partially reverses over longer horizons, consistent with sentiment theories of initial...... under-reaction and delayed over-reaction. A diversified portfolio of time series momentum strategies across all asset classes delivers substantial abnormal returns with little exposure to standard asset pricing factors and performs best during extreme markets. Examining the trading activities...... of speculators and hedgers, we find that speculators profit from time series momentum at the expense of hedgers....

17. Automated Bayesian model development for frequency detection in biological time series

Directory of Open Access Journals (Sweden)

Oldroyd Giles ED

2011-06-01

Full Text Available Abstract Background A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. Results In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Conclusions Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and

18. Model Selection and Quality Estimation of Time Series Models for Artificial Technical Surface Generation

Directory of Open Access Journals (Sweden)

Matthias Eifler

2017-12-01

Full Text Available Standard compliant parameter calculation in surface topography analysis takes the manufacturing process into account. Thus, the measurement technician can be supported with automated suggestions for preprocessing, filtering and evaluation of the measurement data based on the character of the surface topography. Artificial neuronal networks (ANN are one approach for the recognition or classification of technical surfaces. However the required set of training data for ANN is often not available, especially when data acquisition is time consuming or expensive—as e.g., measuring surface topography. Thus, generation of artificial (simulated data becomes of interest. An approach from time series analysis is chosen and examined regarding its suitability for the description of technical surfaces: the ARMAsel model, an approach for time series modelling which is capable of choosing the statistical model with the smallest prediction error and the best number of coefficients for a certain surface. With a reliable model which features the relevant stochastic properties of a surface, a generation of training data for classifiers of artificial neural networks is possible. Based on the determined ARMA-coefficients from the ARMAsel-approach, with only few measured datasets many different artificial surfaces can be generated which can be used for training classifiers of an artificial neural network. In doing so, an improved calculation of the model input data for the generation of artificial surfaces is possible as the training data generation is based on actual measurement data. The trained artificial neural network is tested with actual measurement data of surfaces that were manufactured with varying manufacturing methods and a recognition rate of the according manufacturing principle between 60% and 78% can be determined. This means that based on only few measured datasets, stochastic surface information of various manufacturing principles can be extracted

19. pplication of Time-series Modeling to Predict Infiltration of Different Soil Textures

Directory of Open Access Journals (Sweden)

S. Vazirpour

2016-10-01

Full Text Available Introduction: Infiltration is one of the most important parameters affecting irrigation. For this reason, measuring and estimating this parameter is very important, particularly when designing and managing irrigation systems. Infiltration affects water flow and solute transport in the soil surface and subsurface. Due to temporal and spatial variability, Many measurements are needed to explain the average soil infiltration characteristics under field conditions. Stochastic characteristics of the different natural phenomena led to the application of random variables and time series in predicting the performance of these phenomena. Time-series analysis is a simple and efficient method for prediction, which is widely used in various sciences. However, a few researches have investigated the time-series modeling to predict soil infiltration characteristics. In this study, capability of time series in estimating infiltration rate for different soil textures was evaluated. Materials and methods: For this purpose, the 60 and 120 minutes data of double ring infiltrometer test in Lali plain, Khuzestan, Iran, with its proposed time intervals (0, 1, 3, 5, 10, 15, 20, 30, 45, 60, 80, 100, 120, 150, 180, 210, 240 minutes were used to predict cumulative infiltration until the end of the experiment time for heavy (clay, medium (loam and light (sand soil textures. Moreover, used parameters of Kostiakov-Lewis equation recommended by NRCS, 24 hours cumulative infiltration curves were applied in time-series modeling for six different soil textures (clay, clay loam, silty, silty loam, sandy loam and sand. Different time-series models including Autoregressive (AR, Moving Average (MA, Autoregressive Moving Average (ARMA, autoregressive integrated moving average (ARIMA, ARMA model with eXogenous variables (ARMAX and AR model with eXogenous variables (ARX were evaluated in predicting cumulative infiltration. Autocorrelation and partial autocorrelation charts for each

20. Modelling fourier regression for time series data- a case study: modelling inflation in foods sector in Indonesia

Science.gov (United States)

Prahutama, Alan; Suparti; Wahyu Utami, Tiani

2018-03-01

Regression analysis is an analysis to model the relationship between response variables and predictor variables. The parametric approach to the regression model is very strict with the assumption, but nonparametric regression model isn’t need assumption of model. Time series data is the data of a variable that is observed based on a certain time, so if the time series data wanted to be modeled by regression, then we should determined the response and predictor variables first. Determination of the response variable in time series is variable in t-th (yt), while the predictor variable is a significant lag. In nonparametric regression modeling, one developing approach is to use the Fourier series approach. One of the advantages of nonparametric regression approach using Fourier series is able to overcome data having trigonometric distribution. In modeling using Fourier series needs parameter of K. To determine the number of K can be used Generalized Cross Validation method. In inflation modeling for the transportation sector, communication and financial services using Fourier series yields an optimal K of 120 parameters with R-square 99%. Whereas if it was modeled by multiple linear regression yield R-square 90%.

1. Wavelet Network Model Based on Multiple Criteria Decision Making for Forecasting Temperature Time Series

OpenAIRE

Zhang, Jian; Yang, Xiao-hua; Chen, Xiao-juan

2015-01-01

Due to nonlinear and multiscale characteristics of temperature time series, a new model called wavelet network model based on multiple criteria decision making (WNMCDM) has been proposed, which combines the advantage of wavelet analysis, multiple criteria decision making, and artificial neural network. One case for forecasting extreme monthly maximum temperature of Miyun Reservoir has been conducted to examine the performance of WNMCDM model. Compared with nearest neighbor bootstrapping regr...

2. Multivariate Time Series Search

Data.gov (United States)

National Aeronautics and Space Administration — Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical...

3. Nonlinear Fluctuation Behavior of Financial Time Series Model by Statistical Physics System

Directory of Open Access Journals (Sweden)

Wuyang Cheng

2014-01-01

Full Text Available We develop a random financial time series model of stock market by one of statistical physics systems, the stochastic contact interacting system. Contact process is a continuous time Markov process; one interpretation of this model is as a model for the spread of an infection, where the epidemic spreading mimics the interplay of local infections and recovery of individuals. From this financial model, we study the statistical behaviors of return time series, and the corresponding behaviors of returns for Shanghai Stock Exchange Composite Index (SSECI and Hang Seng Index (HSI are also comparatively studied. Further, we investigate the Zipf distribution and multifractal phenomenon of returns and price changes. Zipf analysis and MF-DFA analysis are applied to investigate the natures of fluctuations for the stock market.

4. Long time series

DEFF Research Database (Denmark)

Hisdal, H.; Holmqvist, E.; Hyvärinen, V.

Awareness that emission of greenhouse gases will raise the global temperature and change the climate has led to studies trying to identify such changes in long-term climate and hydrologic time series. This report, written by the......Awareness that emission of greenhouse gases will raise the global temperature and change the climate has led to studies trying to identify such changes in long-term climate and hydrologic time series. This report, written by the...

5. Using the mean approach in pooling cross-section and time series data for regression modelling

International Nuclear Information System (INIS)

Nuamah, N.N.N.N.

1989-12-01

The mean approach is one of the methods for pooling cross section and time series data for mathematical-statistical modelling. Though a simple approach, its results are sometimes paradoxical in nature. However, researchers still continue using it for its simplicity. Here, the paper investigates the nature and source of such unwanted phenomena. (author). 7 refs

6. Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox

DEFF Research Database (Denmark)

This paper details Particle Markov chain Monte Carlo techniques for analysis of unobserved component time series models using several economic data sets. PMCMC combines the particle filter with the Metropolis-Hastings algorithm. Overall PMCMC provides a very compelling, computationally fast...

7. Modeling the impact of forecast-based regime switches on macroeconomic time series

NARCIS (Netherlands)

K. Bel (Koen); R. Paap (Richard)

2013-01-01

textabstractForecasts of key macroeconomic variables may lead to policy changes of governments, central banks and other economic agents. Policy changes in turn lead to structural changes in macroeconomic time series models. To describe this phenomenon we introduce a logistic smooth transition

8. Harmonic regression of Landsat time series for modeling attributes from national forest inventory data

Science.gov (United States)

Barry T. Wilson; Joseph F. Knight; Ronald E. McRoberts

2018-01-01

Imagery from the Landsat Program has been used frequently as a source of auxiliary data for modeling land cover, as well as a variety of attributes associated with tree cover. With ready access to all scenes in the archive since 2008 due to the USGS Landsat Data Policy, new approaches to deriving such auxiliary data from dense Landsat time series are required. Several...

9. A robust interrupted time series model for analyzing complex health care intervention data

KAUST Repository

Cruz, Maricela

2017-08-29

Current health policy calls for greater use of evidence-based care delivery services to improve patient quality and safety outcomes. Care delivery is complex, with interacting and interdependent components that challenge traditional statistical analytic techniques, in particular, when modeling a time series of outcomes data that might be

10. Clustering of financial time series

Science.gov (United States)

D'Urso, Pierpaolo; Cappelli, Carmela; Di Lallo, Dario; Massari, Riccardo

2013-05-01

This paper addresses the topic of classifying financial time series in a fuzzy framework proposing two fuzzy clustering models both based on GARCH models. In general clustering of financial time series, due to their peculiar features, needs the definition of suitable distance measures. At this aim, the first fuzzy clustering model exploits the autoregressive representation of GARCH models and employs, in the framework of a partitioning around medoids algorithm, the classical autoregressive metric. The second fuzzy clustering model, also based on partitioning around medoids algorithm, uses the Caiado distance, a Mahalanobis-like distance, based on estimated GARCH parameters and covariances that takes into account the information about the volatility structure of time series. In order to illustrate the merits of the proposed fuzzy approaches an application to the problem of classifying 29 time series of Euro exchange rates against international currencies is presented and discussed, also comparing the fuzzy models with their crisp version.

11. New insights into soil temperature time series modeling: linear or nonlinear?

Science.gov (United States)

Bonakdari, Hossein; Moeeni, Hamid; Ebtehaj, Isa; Zeynoddin, Mohammad; Mahoammadian, Abdolmajid; Gharabaghi, Bahram

2018-03-01

Soil temperature (ST) is an important dynamic parameter, whose prediction is a major research topic in various fields including agriculture because ST has a critical role in hydrological processes at the soil surface. In this study, a new linear methodology is proposed based on stochastic methods for modeling daily soil temperature (DST). With this approach, the ST series components are determined to carry out modeling and spectral analysis. The results of this process are compared with two linear methods based on seasonal standardization and seasonal differencing in terms of four DST series. The series used in this study were measured at two stations, Champaign and Springfield, at depths of 10 and 20 cm. The results indicate that in all ST series reviewed, the periodic term is the most robust among all components. According to a comparison of the three methods applied to analyze the various series components, it appears that spectral analysis combined with stochastic methods outperformed the seasonal standardization and seasonal differencing methods. In addition to comparing the proposed methodology with linear methods, the ST modeling results were compared with the two nonlinear methods in two forms: considering hydrological variables (HV) as input variables and DST modeling as a time series. In a previous study at the mentioned sites, Kim and Singh Theor Appl Climatol 118:465-479, (2014) applied the popular Multilayer Perceptron (MLP) neural network and Adaptive Neuro-Fuzzy Inference System (ANFIS) nonlinear methods and considered HV as input variables. The comparison results signify that the relative error projected in estimating DST by the proposed methodology was about 6%, while this value with MLP and ANFIS was over 15%. Moreover, MLP and ANFIS models were employed for DST time series modeling. Due to these models' relatively inferior performance to the proposed methodology, two hybrid models were implemented: the weights and membership function of MLP and

12. Statistical properties of fluctuations of time series representing appearances of words in nationwide blog data and their applications: An example of modeling fluctuation scalings of nonstationary time series

Science.gov (United States)

Watanabe, Hayafumi; Sano, Yukie; Takayasu, Hideki; Takayasu, Misako

2016-11-01

To elucidate the nontrivial empirical statistical properties of fluctuations of a typical nonsteady time series representing the appearance of words in blogs, we investigated approximately 3 ×109 Japanese blog articles over a period of six years and analyze some corresponding mathematical models. First, we introduce a solvable nonsteady extension of the random diffusion model, which can be deduced by modeling the behavior of heterogeneous random bloggers. Next, we deduce theoretical expressions for both the temporal and ensemble fluctuation scalings of this model, and demonstrate that these expressions can reproduce all empirical scalings over eight orders of magnitude. Furthermore, we show that the model can reproduce other statistical properties of time series representing the appearance of words in blogs, such as functional forms of the probability density and correlations in the total number of blogs. As an application, we quantify the abnormality of special nationwide events by measuring the fluctuation scalings of 1771 basic adjectives.

13. Generation of future high-resolution rainfall time series with a disaggregation model

Science.gov (United States)

Müller, Hannes; Haberlandt, Uwe

2017-04-01

High-resolution rainfall data are needed in many fields of hydrology and water resources management. For analyzes of future rainfall condition climate scenarios exist with hourly values of rainfall. However, the direct usage of these data is associated with uncertainties which can be indicated by comparisons of observations and C20 control runs. An alternative is the derivation of changes of rainfall behavior over the time from climate simulations. Conclusions about future rainfall conditions can be drawn by adding these changes to observed time series. A multiplicative cascade model is used in this investigation for the disaggregation of daily rainfall amounts to hourly values. Model parameters can be estimated by REMO rainfall time series (UBA-, BfG- and ENS-realization), based on ECHAM5. Parameter estimation is carried out for C20 period as well as near term and long term future (2021-2050 and 2071-2100). Change factors for both future periods are derived by parameter comparisons and added to the parameters estimated from observed time series. This enables the generation of hourly rainfall time series from observed daily values with respect to future changes. The investigation is carried out for rain gauges in Lower Saxony. Generated Time series are analyzed regarding statistical characteristics, e.g. extreme values, event-based (wet spell duration and amounts, dry spell duration, …) and continuum characteristics (average intensity, fraction of dry intervals,…). The generation of the time series is validated by comparing the changes in the statistical characteristics from the REMO data and from the disaggregated data.

14. Forecasting electricity spot-prices using linear univariate time-series models

International Nuclear Information System (INIS)

Cuaresma, Jesus Crespo; Hlouskova, Jaroslava; Kossmeier, Stephan; Obersteiner, Michael

2004-01-01

This paper studies the forecasting abilities of a battery of univariate models on hourly electricity spot prices, using data from the Leipzig Power Exchange. The specifications studied include autoregressive models, autoregressive-moving average models and unobserved component models. The results show that specifications, where each hour of the day is modelled separately present uniformly better forecasting properties than specifications for the whole time-series, and that the inclusion of simple probabilistic processes for the arrival of extreme price events can lead to improvements in the forecasting abilities of univariate models for electricity spot prices. (Author)

15. Comparison of time series models for predicting campylobacteriosis risk in New Zealand.

Science.gov (United States)

Al-Sakkaf, A; Jones, G

2014-05-01

Predicting campylobacteriosis cases is a matter of considerable concern in New Zealand, after the number of the notified cases was the highest among the developed countries in 2006. Thus, there is a need to develop a model or a tool to predict accurately the number of campylobacteriosis cases as the Microbial Risk Assessment Model used to predict the number of campylobacteriosis cases failed to predict accurately the number of actual cases. We explore the appropriateness of classical time series modelling approaches for predicting campylobacteriosis. Finding the most appropriate time series model for New Zealand data has additional practical considerations given a possible structural change, that is, a specific and sudden change in response to the implemented interventions. A univariate methodological approach was used to predict monthly disease cases using New Zealand surveillance data of campylobacteriosis incidence from 1998 to 2009. The data from the years 1998 to 2008 were used to model the time series with the year 2009 held out of the data set for model validation. The best two models were then fitted to the full 1998-2009 data and used to predict for each month of 2010. The Holt-Winters (multiplicative) and ARIMA (additive) intervention models were considered the best models for predicting campylobacteriosis in New Zealand. It was noticed that the prediction by an additive ARIMA with intervention was slightly better than the prediction by a Holt-Winter multiplicative method for the annual total in year 2010, the former predicting only 23 cases less than the actual reported cases. It is confirmed that classical time series techniques such as ARIMA with intervention and Holt-Winters can provide a good prediction performance for campylobacteriosis risk in New Zealand. The results reported by this study are useful to the New Zealand Health and Safety Authority's efforts in addressing the problem of the campylobacteriosis epidemic. © 2013 Blackwell Verlag GmbH.

16. Estimates by bootstrap interval for time series forecasts obtained by theta model

Directory of Open Access Journals (Sweden)

Daniel Steffen

2017-03-01

Full Text Available In this work, are developed an experimental computer program in Matlab language version 7.1 from the univariate method for time series forecasting called Theta, and implementation of resampling technique known as computer intensive "bootstrap" to estimate the prediction for the point forecast obtained by this method by confidence interval. To solve this problem built up an algorithm that uses Monte Carlo simulation to obtain the interval estimation for forecasts. The Theta model presented in this work was very efficient in M3 Makridakis competition, where tested 3003 series. It is based on the concept of modifying the local curvature of the time series obtained by a coefficient theta (Θ. In it's simplest approach the time series is decomposed into two lines theta representing terms of long term and short term. The prediction is made by combining the forecast obtained by fitting lines obtained with the theta decomposition. The results of Mape's error obtained for the estimates confirm the favorable results to the method of M3 competition being a good alternative for time series forecast.

17. Modeling annual Coffee production in Ghana using ARIMA time series Model

Directory of Open Access Journals (Sweden)

E. Harris

2013-07-01

Full Text Available In the international commodity trade, coffee, which represents the world’s most valuable tropical agricultural commodity, comes next to oil. Indeed, it is estimated that about 40 million people in the major producing countries in Africa derive their livelihood from coffee, with Africa accounting for about 12 per cent of global production. The paper applied Autoregressive Integrated Moving Average (ARIMA time series model to study the behavior of Ghana’s annual coffee production as well as make five years forecasts. Annual coffee production data from 1990 to 2010 was obtained from Ghana cocoa board and analyzed using ARIMA. The results showed that in general, the trend of Ghana’s total coffee production follows an upward and downward movement. The best model arrived at on the basis of various diagnostics, selection and an evaluation criterion was ARIMA (0,3,1. Finally, the forecast figures base on Box- Jenkins method showed that Ghana’s annual coffee production will decrease continuously in the next five (5 years, all things being equal

18. PSO-MISMO modeling strategy for multistep-ahead time series prediction.

Science.gov (United States)

Bao, Yukun; Xiong, Tao; Hu, Zhongyi

2014-05-01

Multistep-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research. Recently, the multiple-input several multiple-outputs (MISMO) modeling strategy has been proposed as a promising alternative for multistep-ahead time series prediction, exhibiting advantages compared with the two currently dominating strategies, the iterated and the direct strategies. Built on the established MISMO strategy, this paper proposes a particle swarm optimization (PSO)-based MISMO modeling strategy, which is capable of determining the number of sub-models in a self-adaptive mode, with varying prediction horizons. Rather than deriving crisp divides with equal-size s prediction horizons from the established MISMO, the proposed PSO-MISMO strategy, implemented with neural networks, employs a heuristic to create flexible divides with varying sizes of prediction horizons and to generate corresponding sub-models, providing considerable flexibility in model construction, which has been validated with simulated and real datasets.

19. Stylised facts of financial time series and hidden Markov models in continuous time

DEFF Research Database (Denmark)

Nystrup, Peter; Madsen, Henrik; Lindström, Erik

2015-01-01

Hidden Markov models are often applied in quantitative finance to capture the stylised facts of financial returns. They are usually discrete-time models and the number of states rarely exceeds two because of the quadratic increase in the number of parameters with the number of states. This paper...... presents an extension to continuous time where it is possible to increase the number of states with a linear rather than quadratic growth in the number of parameters. The possibility of increasing the number of states leads to a better fit to both the distributional and temporal properties of daily returns....

20. Regression and regression analysis time series prediction modeling on climate data of quetta, pakistan

International Nuclear Information System (INIS)

Jafri, Y.Z.; Kamal, L.

2007-01-01

Various statistical techniques was used on five-year data from 1998-2002 of average humidity, rainfall, maximum and minimum temperatures, respectively. The relationships to regression analysis time series (RATS) were developed for determining the overall trend of these climate parameters on the basis of which forecast models can be corrected and modified. We computed the coefficient of determination as a measure of goodness of fit, to our polynomial regression analysis time series (PRATS). The correlation to multiple linear regression (MLR) and multiple linear regression analysis time series (MLRATS) were also developed for deciphering the interdependence of weather parameters. Spearman's rand correlation and Goldfeld-Quandt test were used to check the uniformity or non-uniformity of variances in our fit to polynomial regression (PR). The Breusch-Pagan test was applied to MLR and MLRATS, respectively which yielded homoscedasticity. We also employed Bartlett's test for homogeneity of variances on a five-year data of rainfall and humidity, respectively which showed that the variances in rainfall data were not homogenous while in case of humidity, were homogenous. Our results on regression and regression analysis time series show the best fit to prediction modeling on climatic data of Quetta, Pakistan. (author)

1. Statistical tools for analysis and modeling of cosmic populations and astronomical time series: CUDAHM and TSE

Science.gov (United States)

Loredo, Thomas; Budavari, Tamas; Scargle, Jeffrey D.

2018-01-01

This presentation provides an overview of open-source software packages addressing two challenging classes of astrostatistics problems. (1) CUDAHM is a C++ framework for hierarchical Bayesian modeling of cosmic populations, leveraging graphics processing units (GPUs) to enable applying this computationally challenging paradigm to large datasets. CUDAHM is motivated by measurement error problems in astronomy, where density estimation and linear and nonlinear regression must be addressed for populations of thousands to millions of objects whose features are measured with possibly complex uncertainties, potentially including selection effects. An example calculation demonstrates accurate GPU-accelerated luminosity function estimation for simulated populations of $10^6$ objects in about two hours using a single NVIDIA Tesla K40c GPU. (2) Time Series Explorer (TSE) is a collection of software in Python and MATLAB for exploratory analysis and statistical modeling of astronomical time series. It comprises a library of stand-alone functions and classes, as well as an application environment for interactive exploration of times series data. The presentation will summarize key capabilities of this emerging project, including new algorithms for analysis of irregularly-sampled time series.

2. Integrating uncertainty in time series population forecasts: An illustration using a simple projection model

Directory of Open Access Journals (Sweden)

Guy J. Abel

2013-12-01

Full Text Available Background: Population forecasts are widely used for public policy purposes. Methods to quantify the uncertainty in forecasts tend to ignore model uncertainty and to be based on a single model. Objective: In this paper, we use Bayesian time series models to obtain future population estimates with associated measures of uncertainty. The models are compared based on Bayesian posterior model probabilities, which are then used to provide model-averaged forecasts. Methods: The focus is on a simple projection model with the historical data representing population change in England and Wales from 1841 to 2007. Bayesian forecasts to the year 2032 are obtained based on a range of models, including autoregression models, stochastic volatility models and random variance shift models. The computational steps to fit each of these models using the OpenBUGS software via R are illustrated. Results: We show that the Bayesian approach is adept in capturing multiple sources of uncertainty in population projections, including model uncertainty. The inclusion of non-constant variance improves the fit of the models and provides more realistic predictive uncertainty levels. The forecasting methodology is assessed through fitting the models to various truncated data series.

3. Intuitionistic Fuzzy Time Series Forecasting Model Based on Intuitionistic Fuzzy Reasoning

Directory of Open Access Journals (Sweden)

Ya’nan Wang

2016-01-01

Full Text Available Fuzzy sets theory cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. In this regard, an intuitionistic fuzzy time series forecasting model is built. In the new model, a fuzzy clustering algorithm is used to divide the universe of discourse into unequal intervals, and a more objective technique for ascertaining the membership function and nonmembership function of the intuitionistic fuzzy set is proposed. On these bases, forecast rules based on intuitionistic fuzzy approximate reasoning are established. At last, contrast experiments on the enrollments of the University of Alabama and the Taiwan Stock Exchange Capitalization Weighted Stock Index are carried out. The results show that the new model has a clear advantage of improving the forecast accuracy.

4. [Prediction of epidemic tendency of schistosomiasis with time-series model in Hubei Province].

Science.gov (United States)

Chen, Yan-Yan; Cai, Shun-Xiang; Xiao, Ying; Jiang, Yong; Shan, Xiao-Wei; Zhang, Juan; Liu, Jian-Bing

2014-12-01

To study the endemic trend of schistosomiasis japonica in Hubei Province, so as to provide the theoretical basis for surveillance and forecasting of schistosomiasis. The time-series auto regression integrated moving average (ARIMA) model was applied to fit the infection rate of residents of Hubei Province from 1987 to 2013, and to predict the short-term trend of infection rate. The actual values of infection rate of residents were all in the 95% confidence internals of value predicted by the ARIMA model. The prediction showed that the infection rate of residents of Hubei Province would continue to decrease slowly. The time-series ARIMA model has good prediction accuracy, and could be used for the short-term forecasting of schistosomiasis.

5. Forecasting Cryptocurrencies Financial Time Series

DEFF Research Database (Denmark)

Catania, Leopoldo; Grassi, Stefano; Ravazzolo, Francesco

2018-01-01

This paper studies the predictability of cryptocurrencies time series. We compare several alternative univariate and multivariate models in point and density forecasting of four of the most capitalized series: Bitcoin, Litecoin, Ripple and Ethereum. We apply a set of crypto–predictors and rely...

6. The Gaussian Graphical Model in Cross-Sectional and Time-Series Data.

Science.gov (United States)

Epskamp, Sacha; Waldorp, Lourens J; Mõttus, René; Borsboom, Denny

2018-04-16

We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in three kinds of psychological data sets: data sets in which consecutive cases are assumed independent (e.g., cross-sectional data), temporally ordered data sets (e.g., n = 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In time-series analysis, the GGM can be used to model the residual structure of a vector-autoregression analysis (VAR), also termed graphical VAR. Two network models can then be obtained: a temporal network and a contemporaneous network. When analyzing data from multiple subjects, a GGM can also be formed on the covariance structure of stationary means-the between-subjects network. We discuss the interpretation of these models and propose estimation methods to obtain these networks, which we implement in the R packages graphicalVAR and mlVAR. The methods are showcased in two empirical examples, and simulation studies on these methods are included in the supplementary materials.

7. a Landsat Time-Series Stacks Model for Detection of Cropland Change

Science.gov (United States)

Chen, J.; Chen, J.; Zhang, J.

2017-09-01

Global, timely, accurate and cost-effective cropland monitoring with a fine spatial resolution will dramatically improve our understanding of the effects of agriculture on greenhouse gases emissions, food safety, and human health. Time-series remote sensing imagery have been shown particularly potential to describe land cover dynamics. The traditional change detection techniques are often not capable of detecting land cover changes within time series that are severely influenced by seasonal difference, which are more likely to generate pseuso changes. Here,we introduced and tested LTSM ( Landsat time-series stacks model), an improved Continuous Change Detection and Classification (CCDC) proposed previously approach to extract spectral trajectories of land surface change using a dense Landsat time-series stacks (LTS). The method is expected to eliminate pseudo changes caused by phenology driven by seasonal patterns. The main idea of the method is that using all available Landsat 8 images within a year, LTSM consisting of two term harmonic function are estimated iteratively for each pixel in each spectral band .LTSM can defines change area by differencing the predicted and observed Landsat images. The LTSM approach was compared with change vector analysis (CVA) method. The results indicated that the LTSM method correctly detected the "true change" without overestimating the "false" one, while CVA pointed out "true change" pixels with a large number of "false changes". The detection of change areas achieved an overall accuracy of 92.37 %, with a kappa coefficient of 0.676.

8. Time-dependent deformation source model of Kilauea volcano obtained via InSAR time series and inversion modeling

Science.gov (United States)

Zhai, G.; Shirzaei, M.

2014-12-01

The Kilauea volcano, Hawaii Island, is one of the most active volcanoes worldwide. Its complex system including magma reservoirs and rift zones, provides a unique opportunity to investigate the dynamics of magma transport and supply. The relatively shallow magma reservoir beneath the caldera stores magma prior to eruption at the caldera or migration to the rift zones. Additionally, the temporally variable pressure in the magma reservoir causes changes in the stress field, driving dike propagation and occasional intrusions at the eastern rift zone. Thus constraining the time-dependent evolution of the magma reservoir plays an important role in understanding magma processes such as supply, storage, transport and eruption. The recent development of space-based monitoring technology, InSAR (Interferometric synthetic aperture radar), allows the detection of subtle deformation of the surface at high spatial resolution and accuracy. In order to understand the dynamics of the magma chamber at Kilauea summit area and the associated stress field, we explored SAR data sets acquired in two overlapping tracks of Envisat SAR data during period 2003-2010. The combined InSAR time series includes 100 samples measuring summit deformation at unprecedented spatiotemporal resolutions. To investigate the source of the summit deformation field, we propose a novel time-dependent inverse modelling approach to constrain the dynamics of the reservoir volume change within the summit magma reservoir in three dimensions. In conjunction with seismic and gas data sets, the obtained time-dependent model could resolve the temporally variable relation between shallow and deep reservoirs, as well as their connection to the rift zone via stress changes. The data and model improve the understanding of the Kilauea plumbing system, physics of eruptions, mechanics of rift intrusions, and enhance eruption forecast models.

9. Applied time series analysis

CERN Document Server

Woodward, Wayne A; Elliott, Alan C

2011-01-01

""There is scarcely a standard technique that the reader will find left out … this book is highly recommended for those requiring a ready introduction to applicable methods in time series and serves as a useful resource for pedagogical purposes.""-International Statistical Review (2014), 82""Current time series theory for practice is well summarized in this book.""-Emmanuel Parzen, Texas A&M University""What an extraordinary range of topics covered, all very insightfully. I like [the authors'] innovations very much, such as the AR factor table.""-David Findley, U.S. Census Bureau (retired)""…

10. Predicting chaotic time series

International Nuclear Information System (INIS)

Farmer, J.D.; Sidorowich, J.J.

1987-01-01

We present a forecasting technique for chaotic data. After embedding a time series in a state space using delay coordinates, we ''learn'' the induced nonlinear mapping using local approximation. This allows us to make short-term predictions of the future behavior of a time series, using information based only on past values. We present an error estimate for this technique, and demonstrate its effectiveness by applying it to several examples, including data from the Mackey-Glass delay differential equation, Rayleigh-Benard convection, and Taylor-Couette flow

11. On determining the prediction limits of mathematical models for time series

International Nuclear Information System (INIS)

Peluso, E.; Gelfusa, M.; Lungaroni, M.; Talebzadeh, S.; Gaudio, P.; Murari, A.; Contributors, JET

2016-01-01

Prediction is one of the main objectives of scientific analysis and it refers to both modelling and forecasting. The determination of the limits of predictability is an important issue of both theoretical and practical relevance. In the case of modelling time series, reached a certain level in performance in either modelling or prediction, it is often important to assess whether all the information available in the data has been exploited or whether there are still margins for improvement of the tools being developed. In this paper, an information theoretic approach is proposed to address this issue and quantify the quality of the models and/or predictions. The excellent properties of the proposed indicator have been proved with the help of a systematic series of numerical tests and a concrete example of extreme relevance for nuclear fusion.

12. Short-Term Bus Passenger Demand Prediction Based on Time Series Model and Interactive Multiple Model Approach

Directory of Open Access Journals (Sweden)

Rui Xue

2015-01-01

Full Text Available Although bus passenger demand prediction has attracted increased attention during recent years, limited research has been conducted in the context of short-term passenger demand forecasting. This paper proposes an interactive multiple model (IMM filter algorithm-based model to predict short-term passenger demand. After aggregated in 15 min interval, passenger demand data collected from a busy bus route over four months were used to generate time series. Considering that passenger demand exhibits various characteristics in different time scales, three time series were developed, named weekly, daily, and 15 min time series. After the correlation, periodicity, and stationarity analyses, time series models were constructed. Particularly, the heteroscedasticity of time series was explored to achieve better prediction performance. Finally, IMM filter algorithm was applied to combine individual forecasting models with dynamically predicted passenger demand for next interval. Different error indices were adopted for the analyses of individual and hybrid models. The performance comparison indicates that hybrid model forecasts are superior to individual ones in accuracy. Findings of this study are of theoretical and practical significance in bus scheduling.

13. A time series model: First-order integer-valued autoregressive (INAR(1))

Science.gov (United States)

Simarmata, D. M.; Novkaniza, F.; Widyaningsih, Y.

2017-07-01

Nonnegative integer-valued time series arises in many applications. A time series model: first-order Integer-valued AutoRegressive (INAR(1)) is constructed by binomial thinning operator to model nonnegative integer-valued time series. INAR (1) depends on one period from the process before. The parameter of the model can be estimated by Conditional Least Squares (CLS). Specification of INAR(1) is following the specification of (AR(1)). Forecasting in INAR(1) uses median or Bayesian forecasting methodology. Median forecasting methodology obtains integer s, which is cumulative density function (CDF) until s, is more than or equal to 0.5. Bayesian forecasting methodology forecasts h-step-ahead of generating the parameter of the model and parameter of innovation term using Adaptive Rejection Metropolis Sampling within Gibbs sampling (ARMS), then finding the least integer s, where CDF until s is more than or equal to u . u is a value taken from the Uniform(0,1) distribution. INAR(1) is applied on pneumonia case in Penjaringan, Jakarta Utara, January 2008 until April 2016 monthly.

14. Time-series regression models to study the short-term effects of environmental factors on health

OpenAIRE

Tobías, Aureli; Saez, Marc

2004-01-01

Time series regression models are especially suitable in epidemiology for evaluating short-term effects of time-varying exposures on health. The problem is that potential for confounding in time series regression is very high. Thus, it is important that trend and seasonality are properly accounted for. Our paper reviews the statistical models commonly used in time-series regression methods, specially allowing for serial correlation, make them potentially useful for selected epidemiological pu...

15. Prediction of traffic-related nitrogen oxides concentrations using Structural Time-Series models

Science.gov (United States)

Lawson, Anneka Ruth; Ghosh, Bidisha; Broderick, Brian

2011-09-01

Ambient air quality monitoring, modeling and compliance to the standards set by European Union (EU) directives and World Health Organization (WHO) guidelines are required to ensure the protection of human and environmental health. Congested urban areas are most susceptible to traffic-related air pollution which is the most problematic source of air pollution in Ireland. Long-term continuous real-time monitoring of ambient air quality at such urban centers is essential but often not realistic due to financial and operational constraints. Hence, the development of a resource-conservative ambient air quality monitoring technique is essential to ensure compliance with the threshold values set by the standards. As an intelligent and advanced statistical methodology, a Structural Time Series (STS) based approach has been introduced in this paper to develop a parsimonious and computationally simple air quality model. In STS methodology, the different components of a time-series dataset such as the trend, seasonal, cyclical and calendar variations can be modeled separately. To test the effectiveness of the proposed modeling strategy, average hourly concentrations of nitrogen dioxide and nitrogen oxides from a congested urban arterial in Dublin city center were modeled using STS methodology. The prediction error estimates from the developed air quality model indicate that the STS model can be a useful tool in predicting nitrogen dioxide and nitrogen oxides concentrations in urban areas and will be particularly useful in situations where the information on external variables such as meteorology or traffic volume is not available.

16. Big Data impacts on stochastic Forecast Models: Evidence from FX time series

Directory of Open Access Journals (Sweden)

Sebastian Dietz

2013-12-01

Full Text Available With the rise of the Big Data paradigm new tasks for prediction models appeared. In addition to the volume problem of such data sets nonlinearity becomes important, as the more detailed data sets contain also more comprehensive information, e.g. about non regular seasonal or cyclical movements as well as jumps in time series. This essay compares two nonlinear methods for predicting a high frequency time series, the USD/Euro exchange rate. The first method investigated is Autoregressive Neural Network Processes (ARNN, a neural network based nonlinear extension of classical autoregressive process models from time series analysis (see Dietz 2011. Its advantage is its simple but scalable time series process model architecture, which is able to include all kinds of nonlinearities based on the universal approximation theorem of Hornik, Stinchcombe and White 1989 and the extensions of Hornik 1993. However, restrictions related to the numeric estimation procedures limit the flexibility of the model. The alternative is a Support Vector Machine Model (SVM, Vapnik 1995. The two methods compared have different approaches of error minimization (Empirical error minimization at the ARNN vs. structural error minimization at the SVM. Our new finding is, that time series data classified as “Big Data” need new methods for prediction. Estimation and prediction was performed using the statistical programming language R. Besides prediction results we will also discuss the impact of Big Data on data preparation and model validation steps. Normal 0 21 false false false DE X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Normale Tabelle"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";}

17. Extracting Knowledge From Time Series An Introduction to Nonlinear Empirical Modeling

CERN Document Server

Bezruchko, Boris P

2010-01-01

This book addresses the fundamental question of how to construct mathematical models for the evolution of dynamical systems from experimentally-obtained time series. It places emphasis on chaotic signals and nonlinear modeling and discusses different approaches to the forecast of future system evolution. In particular, it teaches readers how to construct difference and differential model equations depending on the amount of a priori information that is available on the system in addition to the experimental data sets. This book will benefit graduate students and researchers from all natural sciences who seek a self-contained and thorough introduction to this subject.

18. A new Markov-chain-related statistical approach for modelling synthetic wind power time series

International Nuclear Information System (INIS)

Pesch, T; Hake, J F; Schröders, S; Allelein, H J

2015-01-01

The integration of rising shares of volatile wind power in the generation mix is a major challenge for the future energy system. To address the uncertainties involved in wind power generation, models analysing and simulating the stochastic nature of this energy source are becoming increasingly important. One statistical approach that has been frequently used in the literature is the Markov chain approach. Recently, the method was identified as being of limited use for generating wind time series with time steps shorter than 15–40 min as it is not capable of reproducing the autocorrelation characteristics accurately. This paper presents a new Markov-chain-related statistical approach that is capable of solving this problem by introducing a variable second lag. Furthermore, additional features are presented that allow for the further adjustment of the generated synthetic time series. The influences of the model parameter settings are examined by meaningful parameter variations. The suitability of the approach is demonstrated by an application analysis with the example of the wind feed-in in Germany. It shows that—in contrast to conventional Markov chain approaches—the generated synthetic time series do not systematically underestimate the required storage capacity to balance wind power fluctuation. (paper)

19. Environmental monitoring model for a drainage basin obtained through spectral analysis of time series.

Science.gov (United States)

Faht, Guilherme; da Silva, Marcos Rivail; Pinheiro, Adilson; Kaufmann, Vander; de Aguida, Leandro Mazzuco

2012-08-01

The quality of results of an environmental monitoring plan is limited to the weakest component, which could be the analytical approach or sampling method. Considering both the possibilities and the fragility that sampling methods offer, this environmental monitoring study focused on the uncertainties caused by the time component. Four time series of nutrient concentration at two sampling points (PB1 and PB2) in the Ribeirão Garcia basin in Blumenau, Brazil, which were significantly correlated to the spatial component, were considered with a 2-hour resolution to develop efficient sampling models. These models were based on the time at which there was the highest tendency toward adverse environmental effects. Fourier spectral analysis was used to evaluated the time series and resulted in two sampling models: (1) the SMCP (sampling model for critical period) that operated with 100% efficiency for registering the highest concentration of nutrients and was valid for 83% of the studied parameters; and (2) the SMGCP (sampling model for global critical period) that operated with 83 and 50% efficiency for PB1 and PB2, respectively.

20. Time series modeling for analysis and control advanced autopilot and monitoring systems

CERN Document Server

Ohtsu, Kohei; Kitagawa, Genshiro

2015-01-01

This book presents multivariate time series methods for the analysis and optimal control of feedback systems. Although ships’ autopilot systems are considered through the entire book, the methods set forth in this book can be applied to many other complicated, large, or noisy feedback control systems for which it is difficult to derive a model of the entire system based on theory in that subject area. The basic models used in this method are the multivariate autoregressive model with exogenous variables (ARX) model and the radial bases function net-type coefficients ARX model. The noise contribution analysis can then be performed through the estimated autoregressive (AR) model and various types of autopilot systems can be designed through the state–space representation of the models. The marine autopilot systems addressed in this book include optimal controllers for course-keeping motion, rolling reduction controllers with rudder motion, engine governor controllers, noise adaptive autopilots, route-tracki...

1. Comparing and Contrasting Traditional Membrane Bioreactor Models with Novel Ones Based on Time Series Analysis

Directory of Open Access Journals (Sweden)

Parneet Paul

2013-02-01

Full Text Available The computer modelling and simulation of wastewater treatment plant and their specific technologies, such as membrane bioreactors (MBRs, are becoming increasingly useful to consultant engineers when designing, upgrading, retrofitting, operating and controlling these plant. This research uses traditional phenomenological mechanistic models based on MBR filtration and biochemical processes to measure the effectiveness of alternative and novel time series models based upon input–output system identification methods. Both model types are calibrated and validated using similar plant layouts and data sets derived for this purpose. Results prove that although both approaches have their advantages, they also have specific disadvantages as well. In conclusion, the MBR plant designer and/or operator who wishes to use good quality, calibrated models to gain a better understanding of their process, should carefully consider which model type is selected based upon on what their initial modelling objectives are. Each situation usually proves unique.

2. Applications of soft computing in time series forecasting simulation and modeling techniques

CERN Document Server

Singh, Pritpal

2016-01-01

This book reports on an in-depth study of fuzzy time series (FTS) modeling. It reviews and summarizes previous research work in FTS modeling and also provides a brief introduction to other soft-computing techniques, such as artificial neural networks (ANNs), rough sets (RS) and evolutionary computing (EC), focusing on how these techniques can be integrated into different phases of the FTS modeling approach. In particular, the book describes novel methods resulting from the hybridization of FTS modeling approaches with neural networks and particle swarm optimization. It also demonstrates how a new ANN-based model can be successfully applied in the context of predicting Indian summer monsoon rainfall. Thanks to its easy-to-read style and the clear explanations of the models, the book can be used as a concise yet comprehensive reference guide to fuzzy time series modeling, and will be valuable not only for graduate students, but also for researchers and professionals working for academic, business and governmen...

3. Harmonic regression of Landsat time series for modeling attributes from national forest inventory data

Science.gov (United States)

Wilson, Barry T.; Knight, Joseph F.; McRoberts, Ronald E.

2018-03-01

Imagery from the Landsat Program has been used frequently as a source of auxiliary data for modeling land cover, as well as a variety of attributes associated with tree cover. With ready access to all scenes in the archive since 2008 due to the USGS Landsat Data Policy, new approaches to deriving such auxiliary data from dense Landsat time series are required. Several methods have previously been developed for use with finer temporal resolution imagery (e.g. AVHRR and MODIS), including image compositing and harmonic regression using Fourier series. The manuscript presents a study, using Minnesota, USA during the years 2009-2013 as the study area and timeframe. The study examined the relative predictive power of land cover models, in particular those related to tree cover, using predictor variables based solely on composite imagery versus those using estimated harmonic regression coefficients. The study used two common non-parametric modeling approaches (i.e. k-nearest neighbors and random forests) for fitting classification and regression models of multiple attributes measured on USFS Forest Inventory and Analysis plots using all available Landsat imagery for the study area and timeframe. The estimated Fourier coefficients developed by harmonic regression of tasseled cap transformation time series data were shown to be correlated with land cover, including tree cover. Regression models using estimated Fourier coefficients as predictor variables showed a two- to threefold increase in explained variance for a small set of continuous response variables, relative to comparable models using monthly image composites. Similarly, the overall accuracies of classification models using the estimated Fourier coefficients were approximately 10-20 percentage points higher than the models using the image composites, with corresponding individual class accuracies between six and 45 percentage points higher.

4. Creation and evaluation of a database of renewable production time series and other data for energy system modelling

International Nuclear Information System (INIS)

Janker, Karl Albert

2015-01-01

This thesis describes a model which generates renewable power generation time series as input data for energy system models. The focus is on photovoltaic systems and wind turbines. The basis is a high resolution global raster data set of weather data for many years. This data is validated, corrected and preprocessed. The composition of the hourly generation data is done via simulation of the respective technology. The generated time series are aggregated for different regions and are validated against historical production time series.

5. Structural Time Series Model for El Niño Prediction

Science.gov (United States)

Petrova, Desislava; Koopman, Siem Jan; Ballester, Joan; Rodo, Xavier

2015-04-01

ENSO is a dominant feature of climate variability on inter-annual time scales destabilizing weather patterns throughout the globe, and having far-reaching socio-economic consequences. It does not only lead to extensive rainfall and flooding in some regions of the world, and anomalous droughts in others, thus ruining local agriculture, but also substantially affects the marine ecosystems and the sustained exploitation of marine resources in particular coastal zones, especially the Pacific South American coast. As a result, forecasting of ENSO and especially of the warm phase of the oscillation (El Niño/EN) has long been a subject of intense research and improvement. Thus, the present study explores a novel method for the prediction of the Niño 3.4 index. In the state-of-the-art the advantageous statistical modeling approach of Structural Time Series Analysis has not been applied. Therefore, we have developed such a model using a State Space approach for the unobserved components of the time series. Its distinguishing feature is that observations consist of various components - level, seasonality, cycle, disturbance, and regression variables incorporated as explanatory covariates. These components are aimed at capturing the various modes of variability of the N3.4 time series. They are modeled separately, then combined in a single model for analysis and forecasting. Customary statistical ENSO prediction models essentially use SST, SLP and wind stress in the equatorial Pacific. We introduce new regression variables - subsurface ocean temperature in the western equatorial Pacific, motivated by recent (Ramesh and Murtugudde, 2012) and classical research (Jin, 1997), (Wyrtki, 1985), showing that subsurface processes and heat accumulation there are fundamental for initiation of an El Niño event; and a southern Pacific temperature-difference tracer, the Rossbell dipole, leading EN by about nine months (Ballester, 2011).

6. Sample correlations of infinite variance time series models: an empirical and theoretical study

Directory of Open Access Journals (Sweden)

Jason Cohen

1998-01-01

Full Text Available When the elements of a stationary ergodic time series have finite variance the sample correlation function converges (with probability 1 to the theoretical correlation function. What happens in the case where the variance is infinite? In certain cases, the sample correlation function converges in probability to a constant, but not always. If within a class of heavy tailed time series the sample correlation functions do not converge to a constant, then more care must be taken in making inferences and in model selection on the basis of sample autocorrelations. We experimented with simulating various heavy tailed stationary sequences in an attempt to understand what causes the sample correlation function to converge or not to converge to a constant. In two new cases, namely the sum of two independent moving averages and a random permutation scheme, we are able to provide theoretical explanations for a random limit of the sample autocorrelation function as the sample grows.

7. A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method

Directory of Open Access Journals (Sweden)

Jun-He Yang

2017-01-01

Full Text Available Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir’s water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir’s water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.

8. Time Series Model of Wind Speed for Multi Wind Turbines based on Mixed Copula

Directory of Open Access Journals (Sweden)

Nie Dan

2016-01-01

Full Text Available Because wind power is intermittent, random and so on, large scale grid will directly affect the safe and stable operation of power grid. In order to make a quantitative study on the characteristics of the wind speed of wind turbine, the wind speed time series model of the multi wind turbine generator is constructed by using the mixed Copula-ARMA function in this paper, and a numerical example is also given. The research results show that the model can effectively predict the wind speed, ensure the efficient operation of the wind turbine, and provide theoretical basis for the stability of wind power grid connected operation.

9. Water quality management using statistical analysis and time-series prediction model

Science.gov (United States)

Parmar, Kulwinder Singh; Bhardwaj, Rashmi

2014-12-01

This paper deals with water quality management using statistical analysis and time-series prediction model. The monthly variation of water quality standards has been used to compare statistical mean, median, mode, standard deviation, kurtosis, skewness, coefficient of variation at Yamuna River. Model validated using R-squared, root mean square error, mean absolute percentage error, maximum absolute percentage error, mean absolute error, maximum absolute error, normalized Bayesian information criterion, Ljung-Box analysis, predicted value and confidence limits. Using auto regressive integrated moving average model, future water quality parameters values have been estimated. It is observed that predictive model is useful at 95 % confidence limits and curve is platykurtic for potential of hydrogen (pH), free ammonia, total Kjeldahl nitrogen, dissolved oxygen, water temperature (WT); leptokurtic for chemical oxygen demand, biochemical oxygen demand. Also, it is observed that predicted series is close to the original series which provides a perfect fit. All parameters except pH and WT cross the prescribed limits of the World Health Organization /United States Environmental Protection Agency, and thus water is not fit for drinking, agriculture and industrial use.

10. International Work-Conference on Time Series

CERN Document Server

Pomares, Héctor; Valenzuela, Olga

2017-01-01

This volume of selected and peer-reviewed contributions on the latest developments in time series analysis and forecasting updates the reader on topics such as analysis of irregularly sampled time series, multi-scale analysis of univariate and multivariate time series, linear and non-linear time series models, advanced time series forecasting methods, applications in time series analysis and forecasting, advanced methods and online learning in time series and high-dimensional and complex/big data time series. The contributions were originally presented at the International Work-Conference on Time Series, ITISE 2016, held in Granada, Spain, June 27-29, 2016. The series of ITISE conferences provides a forum for scientists, engineers, educators and students to discuss the latest ideas and implementations in the foundations, theory, models and applications in the field of time series analysis and forecasting.  It focuses on interdisciplinary and multidisciplinary rese arch encompassing the disciplines of comput...

11. Modeling commodity salam contract between two parties for discrete and continuous time series

Science.gov (United States)

Hisham, Azie Farhani Badrol; Jaffar, Maheran Mohd

2017-08-01

In order for Islamic finance to remain competitive as the conventional, there needs a new development of Islamic compliance product such as Islamic derivative that can be used to manage the risk. However, under syariah principles and regulations, all financial instruments must not be conflicting with five syariah elements which are riba (interest paid), rishwah (corruption), gharar (uncertainty or unnecessary risk), maysir (speculation or gambling) and jahl (taking advantage of the counterparty's ignorance). This study has proposed a traditional Islamic contract namely salam that can be built as an Islamic derivative product. Although a lot of studies has been done on discussing and proposing the implementation of salam contract as the Islamic product however they are more into qualitative and law issues. Since there is lack of quantitative study of salam contract being developed, this study introduces mathematical models that can value the appropriate salam price for a commodity salam contract between two parties. In modeling the commodity salam contract, this study has modified the existing conventional derivative model and come out with some adjustments to comply with syariah rules and regulations. The cost of carry model has been chosen as the foundation to develop the commodity salam model between two parties for discrete and continuous time series. However, the conventional time value of money results from the concept of interest that is prohibited in Islam. Therefore, this study has adopted the idea of Islamic time value of money which is known as the positive time preference, in modeling the commodity salam contract between two parties for discrete and continuous time series.

12. Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China

Science.gov (United States)

Zhang, Yong; Zhong, Miner; Geng, Nana; Jiang, Yunjian

2017-01-01

The market demand for electric vehicles (EVs) has increased in recent years. Suitable models are necessary to understand and forecast EV sales. This study presents a singular spectrum analysis (SSA) as a univariate time-series model and vector autoregressive model (VAR) as a multivariate model. Empirical results suggest that SSA satisfactorily indicates the evolving trend and provides reasonable results. The VAR model, which comprised exogenous parameters related to the market on a monthly basis, can significantly improve the prediction accuracy. The EV sales in China, which are categorized into battery and plug-in EVs, are predicted in both short term (up to December 2017) and long term (up to 2020), as statistical proofs of the growth of the Chinese EV industry. PMID:28459872

13. Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China.

Science.gov (United States)

Zhang, Yong; Zhong, Miner; Geng, Nana; Jiang, Yunjian

2017-01-01

The market demand for electric vehicles (EVs) has increased in recent years. Suitable models are necessary to understand and forecast EV sales. This study presents a singular spectrum analysis (SSA) as a univariate time-series model and vector autoregressive model (VAR) as a multivariate model. Empirical results suggest that SSA satisfactorily indicates the evolving trend and provides reasonable results. The VAR model, which comprised exogenous parameters related to the market on a monthly basis, can significantly improve the prediction accuracy. The EV sales in China, which are categorized into battery and plug-in EVs, are predicted in both short term (up to December 2017) and long term (up to 2020), as statistical proofs of the growth of the Chinese EV industry.

14. Construction of the exact Fisher information matrix of Gaussian time series models by means of matrix differential rules

NARCIS (Netherlands)

Klein, A.A.B.; Melard, G.; Zahaf, T.

2000-01-01

The Fisher information matrix is of fundamental importance for the analysis of parameter estimation of time series models. In this paper the exact information matrix of a multivariate Gaussian time series model expressed in state space form is derived. A computationally efficient procedure is used

15. Multivariate autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry

Directory of Open Access Journals (Sweden)

S. M. Barbosa

2006-01-01

Full Text Available This work addresses the autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry mission. Datasets from remote sensing applications are typically very large and correlated both in time and space. Multivariate analysis methods are useful tools to summarise and extract information from such large space-time datasets. Multivariate autoregressive analysis is a generalisation of Principal Oscillation Pattern (POP analysis, widely used in the geosciences for the extraction of dynamical modes by eigen-decomposition of a first order autoregressive model fitted to the multivariate dataset of observations. The extension of the POP methodology to autoregressions of higher order, although increasing the difficulties in estimation, allows one to model a larger class of complex systems. Here, sea level variability in the North Atlantic is modelled by a third order multivariate autoregressive model estimated by stepwise least squares. Eigen-decomposition of the fitted model yields physically-interpretable seasonal modes. The leading autoregressive mode is an annual oscillation and exhibits a very homogeneous spatial structure in terms of amplitude reflecting the large scale coherent behaviour of the annual pattern in the Northern hemisphere. The phase structure reflects the seesaw pattern between the western and eastern regions in the tropical North Atlantic associated with the trade winds regime. The second mode is close to a semi-annual oscillation. Multivariate autoregressive models provide a useful framework for the description of time-varying fields while enclosing a predictive potential.

16. Fractality of profit landscapes and validation of time series models for stock prices

Science.gov (United States)

Yi, Il Gu; Oh, Gabjin; Kim, Beom Jun

2013-08-01

We apply a simple trading strategy for various time series of real and artificial stock prices to understand the origin of fractality observed in the resulting profit landscapes. The strategy contains only two parameters p and q, and the sell (buy) decision is made when the log return is larger (smaller) than p (-q). We discretize the unit square (p,q) ∈ [0,1] × [0,1] into the N × N square grid and the profit Π(p,q) is calculated at the center of each cell. We confirm the previous finding that local maxima in profit landscapes are scattered in a fractal-like fashion: the number M of local maxima follows the power-law form M ˜ Na, but the scaling exponent a is found to differ for different time series. From comparisons of real and artificial stock prices, we find that the fat-tailed return distribution is closely related to the exponent a ≈ 1.6 observed for real stock markets. We suggest that the fractality of profit landscape characterized by a ≈ 1.6 can be a useful measure to validate time series model for stock prices.

17. A Course in Time Series Analysis

CERN Document Server

Peña, Daniel; Tsay, Ruey S

2011-01-01

New statistical methods and future directions of research in time series A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, a

18. Dynamics modeling for sugar cane sucrose estimation using time series satellite imagery

Science.gov (United States)

Zhao, Yu; Justina, Diego Della; Kazama, Yoriko; Rocha, Jansle Vieira; Graziano, Paulo Sergio; Lamparelli, Rubens Augusto Camargo

2016-10-01

Sugarcane, as one of the most mainstay crop in Brazil, plays an essential role in ethanol production. To monitor sugarcane crop growth and predict sugarcane sucrose content, remote sensing technology plays an essential role while accurate and timely crop growth information is significant, in particularly for large scale farming. We focused on the issues of sugarcane sucrose content estimation using time-series satellite image. Firstly, we calculated the spectral features and vegetation indices to make them be correspondence to the sucrose accumulation biological mechanism. Secondly, we improved the statistical regression model considering more other factors. The evaluation was performed and we got precision of 90% which is about 20% higher than the conventional method. The validation results showed that prediction accuracy using our sugarcane growth modeling and improved mix model is satisfied.

19. An advection-based model to increase the temporal resolution of PIV time series.

Science.gov (United States)

Scarano, Fulvio; Moore, Peter

A numerical implementation of the advection equation is proposed to increase the temporal resolution of PIV time series. The method is based on the principle that velocity fluctuations are transported passively, similar to Taylor's hypothesis of frozen turbulence . In the present work, the advection model is extended to unsteady three-dimensional flows. The main objective of the method is that of lowering the requirement on the PIV repetition rate from the Eulerian frequency toward the Lagrangian one. The local trajectory of the fluid parcel is obtained by forward projection of the instantaneous velocity at the preceding time instant and backward projection from the subsequent time step. The trajectories are approximated by the instantaneous streamlines, which yields accurate results when the amplitude of velocity fluctuations is small with respect to the convective motion. The verification is performed with two experiments conducted at temporal resolutions significantly higher than that dictated by Nyquist criterion. The flow past the trailing edge of a NACA0012 airfoil closely approximates frozen turbulence , where the largest ratio between the Lagrangian and Eulerian temporal scales is expected. An order of magnitude reduction of the needed acquisition frequency is demonstrated by the velocity spectra of super-sampled series. The application to three-dimensional data is made with time-resolved tomographic PIV measurements of a transitional jet. Here, the 3D advection equation is implemented to estimate the fluid trajectories. The reduction in the minimum sampling rate by the use of super-sampling in this case is less, due to the fact that vortices occurring in the jet shear layer are not well approximated by sole advection at large time separation. Both cases reveal that the current requirements for time-resolved PIV experiments can be revised when information is poured from space to time . An additional favorable effect is observed by the analysis in the

20. Application of semi parametric modelling to times series forecasting: case of the electricity consumption

International Nuclear Information System (INIS)

Lefieux, V.

2007-10-01

Reseau de Transport d'Electricite (RTE), in charge of operating the French electric transportation grid, needs an accurate forecast of the power consumption in order to operate it correctly. The forecasts used everyday result from a model combining a nonlinear parametric regression and a SARIMA model. In order to obtain an adaptive forecasting model, nonparametric forecasting methods have already been tested without real success. In particular, it is known that a nonparametric predictor behaves badly with a great number of explanatory variables, what is commonly called the curse of dimensionality. Recently, semi parametric methods which improve the pure nonparametric approach have been proposed to estimate a regression function. Based on the concept of 'dimension reduction', one those methods (called MAVE : Moving Average -conditional- Variance Estimate) can apply to time series. We study empirically its effectiveness to predict the future values of an autoregressive time series. We then adapt this method, from a practical point of view, to forecast power consumption. We propose a partially linear semi parametric model, based on the MAVE method, which allows to take into account simultaneously the autoregressive aspect of the problem and the exogenous variables. The proposed estimation procedure is practically efficient. (author)

1. Mathematical Modeling and Dynamic Simulation of Metabolic Reaction Systems Using Metabolome Time Series Data.

Science.gov (United States)

Sriyudthsak, Kansuporn; Shiraishi, Fumihide; Hirai, Masami Yokota

2016-01-01

The high-throughput acquisition of metabolome data is greatly anticipated for the complete understanding of cellular metabolism in living organisms. A variety of analytical technologies have been developed to acquire large-scale metabolic profiles under different biological or environmental conditions. Time series data are useful for predicting the most likely metabolic pathways because they provide important information regarding the accumulation of metabolites, which implies causal relationships in the metabolic reaction network. Considerable effort has been undertaken to utilize these data for constructing a mathematical model merging system properties and quantitatively characterizing a whole metabolic system in toto. However, there are technical difficulties between benchmarking the provision and utilization of data. Although, hundreds of metabolites can be measured, which provide information on the metabolic reaction system, simultaneous measurement of thousands of metabolites is still challenging. In addition, it is nontrivial to logically predict the dynamic behaviors of unmeasurable metabolite concentrations without sufficient information on the metabolic reaction network. Yet, consolidating the advantages of advancements in both metabolomics and mathematical modeling remain to be accomplished. This review outlines the conceptual basis of and recent advances in technologies in both the research fields. It also highlights the potential for constructing a large-scale mathematical model by estimating model parameters from time series metabolome data in order to comprehensively understand metabolism at the systems level.

2. Multivariate time series modeling of short-term system scale irrigation demand

Science.gov (United States)

Perera, Kushan C.; Western, Andrew W.; George, Biju; Nawarathna, Bandara

2015-12-01

Travel time limits the ability of irrigation system operators to react to short-term irrigation demand fluctuations that result from variations in weather, including very hot periods and rainfall events, as well as the various other pressures and opportunities that farmers face. Short-term system-wide irrigation demand forecasts can assist in system operation. Here we developed a multivariate time series (ARMAX) model to forecast irrigation demands with respect to aggregated service points flows (IDCGi, ASP) and off take regulator flows (IDCGi, OTR) based across 5 command areas, which included area covered under four irrigation channels and the study area. These command area specific ARMAX models forecast 1-5 days ahead daily IDCGi, ASP and IDCGi, OTR using the real time flow data recorded at the service points and the uppermost regulators and observed meteorological data collected from automatic weather stations. The model efficiency and the predictive performance were quantified using the root mean squared error (RMSE), Nash-Sutcliffe model efficiency coefficient (NSE), anomaly correlation coefficient (ACC) and mean square skill score (MSSS). During the evaluation period, NSE for IDCGi, ASP and IDCGi, OTR across 5 command areas were ranged 0.98-0.78. These models were capable of generating skillful forecasts (MSSS ⩾ 0.5 and ACC ⩾ 0.6) of IDCGi, ASP and IDCGi, OTR for all 5 lead days and IDCGi, ASP and IDCGi, OTR forecasts were better than using the long term monthly mean irrigation demand. Overall these predictive performance from the ARMAX time series models were higher than almost all the previous studies we are aware. Further, IDCGi, ASP and IDCGi, OTR forecasts have improved the operators' ability to react for near future irrigation demand fluctuations as the developed ARMAX time series models were self-adaptive to reflect the short-term changes in the irrigation demand with respect to various pressures and opportunities that farmers' face, such as

3. Visibility Modeling and Forecasting for Abu Dhabi using Time Series Analysis Method

Science.gov (United States)

Eibedingil, I. G.; Abula, B.; Afshari, A.; Temimi, M.

2015-12-01

Land-Atmosphere interactions-their strength, directionality and evolution-are one of the main sources of uncertainty in contemporary climate modeling. A particularly crucial role in sustaining and modulating land-atmosphere interaction is the one of aerosols and dusts. Aerosols are tiny particles suspended in the air ranging from a few nanometers to a few hundred micrometers in diameter. Furthermore, the amount of dust and fog in the atmosphere is an important measure of visibility, which is another dimension of land-atmosphere interactions. Visibility affects all form of traffic, aviation, land and sailing. Being able to predict the change of visibility in the air in advance enables relevant authorities to take necessary actions before the disaster falls. Time Series Analysis (TAS) method is an emerging technique for modeling and forecasting the behavior of land-atmosphere interactions, including visibility. This research assess the dynamics and evolution of visibility around Abu Dhabi International Airport (+24.4320 latitude, +54.6510 longitude, and 27m elevation) using mean daily visibility and mean daily wind speed. TAS has been first used to model and forecast the visibility, and then the Transfer Function Model has been applied, considering the wind speed as an exogenous variable. By considering the Akaike Information Criterion (AIC) and Mean Absolute Percentage Error (MAPE) as a statistical criteria, two forecasting models namely univarite time series model and transfer function model, were developed to forecast the visibility around Abu Dhabi International Airport for three weeks. Transfer function model improved the MAPE of the forecast significantly.

4. An Application of the Coherent Noise Model for the Prediction of Aftershock Magnitude Time Series

Directory of Open Access Journals (Sweden)

Stavros-Richard G. Christopoulos

2017-01-01

Full Text Available Recently, the study of the coherent noise model has led to a simple (binary prediction algorithm for the forthcoming earthquake magnitude in aftershock sequences. This algorithm is based on the concept of natural time and exploits the complexity exhibited by the coherent noise model. Here, using the relocated catalogue from Southern California Seismic Network for 1981 to June 2011, we evaluate the application of this algorithm for the aftershocks of strong earthquakes of magnitude M≥6. The study is also extended by using the Global Centroid Moment Tensor Project catalogue to the case of the six strongest earthquakes in the Earth during the last almost forty years. The predictor time series exhibits the ubiquitous 1/f noise behavior.

5. Scaling symmetry, renormalization, and time series modeling: the case of financial assets dynamics.

Science.gov (United States)

Zamparo, Marco; Baldovin, Fulvio; Caraglio, Michele; Stella, Attilio L

2013-12-01

We present and discuss a stochastic model of financial assets dynamics based on the idea of an inverse renormalization group strategy. With this strategy we construct the multivariate distributions of elementary returns based on the scaling with time of the probability density of their aggregates. In its simplest version the model is the product of an endogenous autoregressive component and a random rescaling factor designed to embody also exogenous influences. Mathematical properties like increments' stationarity and ergodicity can be proven. Thanks to the relatively low number of parameters, model calibration can be conveniently based on a method of moments, as exemplified in the case of historical data of the S&P500 index. The calibrated model accounts very well for many stylized facts, like volatility clustering, power-law decay of the volatility autocorrelation function, and multiscaling with time of the aggregated return distribution. In agreement with empirical evidence in finance, the dynamics is not invariant under time reversal, and, with suitable generalizations, skewness of the return distribution and leverage effects can be included. The analytical tractability of the model opens interesting perspectives for applications, for instance, in terms of obtaining closed formulas for derivative pricing. Further important features are the possibility of making contact, in certain limits, with autoregressive models widely used in finance and the possibility of partially resolving the long- and short-memory components of the volatility, with consistent results when applied to historical series.

6. Modeling Dyadic Processes Using Hidden Markov Models: A Time Series Approach to Mother-Infant Interactions during Infant Immunization

Science.gov (United States)

Stifter, Cynthia A.; Rovine, Michael

2015-01-01

The focus of the present longitudinal study, to examine mother-infant interaction during the administration of immunizations at 2 and 6?months of age, used hidden Markov modelling, a time series approach that produces latent states to describe how mothers and infants work together to bring the infant to a soothed state. Results revealed a…

7. Non-stationary time series modeling on caterpillars pest of palm oil for early warning system

Science.gov (United States)

Setiyowati, Susi; Nugraha, Rida F.; Mukhaiyar, Utriweni

2015-12-01

The oil palm production has an important role for the plantation and economic sector in Indonesia. One of the important problems in the cultivation of oil palm plantation is pests which causes damage to the quality of fruits. The caterpillar pest which feed palm tree's leaves will cause decline in quality of palm oil production. Early warning system is needed to minimize losses due to this pest. Here, we applied non-stationary time series modeling, especially the family of autoregressive models to predict the number of pests based on its historical data. We realized that there is some uniqueness of these pests data, i.e. the spike value that occur almost periodically. Through some simulations and case study, we obtain that the selection of constant factor has a significance influence to the model so that it can shoot the spikes value precisely.

Energy Technology Data Exchange (ETDEWEB)

Costa, Alexandre M.S. [Universidade Estadual de Maringa (UEM), PR (Brazil)], E-mail: amscosta@uem.br

2010-07-01

9. Stochastic modeling for time series InSAR: with emphasis on atmospheric effects

Science.gov (United States)

Cao, Yunmeng; Li, Zhiwei; Wei, Jianchao; Hu, Jun; Duan, Meng; Feng, Guangcai

2018-02-01

Despite the many applications of time series interferometric synthetic aperture radar (TS-InSAR) techniques in geophysical problems, error analysis and assessment have been largely overlooked. Tropospheric propagation error is still the dominant error source of InSAR observations. However, the spatiotemporal variation of atmospheric effects is seldom considered in the present standard TS-InSAR techniques, such as persistent scatterer interferometry and small baseline subset interferometry. The failure to consider the stochastic properties of atmospheric effects not only affects the accuracy of the estimators, but also makes it difficult to assess the uncertainty of the final geophysical results. To address this issue, this paper proposes a network-based variance-covariance estimation method to model the spatiotemporal variation of tropospheric signals, and to estimate the temporal variance-covariance matrix of TS-InSAR observations. The constructed stochastic model is then incorporated into the TS-InSAR estimators both for parameters (e.g., deformation velocity, topography residual) estimation and uncertainty assessment. It is an incremental and positive improvement to the traditional weighted least squares methods to solve the multitemporal InSAR time series. The performance of the proposed method is validated by using both simulated and real datasets.

10. A Virtual Machine Migration Strategy Based on Time Series Workload Prediction Using Cloud Model

Directory of Open Access Journals (Sweden)

Yanbing Liu

2014-01-01

Full Text Available Aimed at resolving the issues of the imbalance of resources and workloads at data centers and the overhead together with the high cost of virtual machine (VM migrations, this paper proposes a new VM migration strategy which is based on the cloud model time series workload prediction algorithm. By setting the upper and lower workload bounds for host machines, forecasting the tendency of their subsequent workloads by creating a workload time series using the cloud model, and stipulating a general VM migration criterion workload-aware migration (WAM, the proposed strategy selects a source host machine, a destination host machine, and a VM on the source host machine carrying out the task of the VM migration. Experimental results and analyses show, through comparison with other peer research works, that the proposed method can effectively avoid VM migrations caused by momentary peak workload values, significantly lower the number of VM migrations, and dynamically reach and maintain a resource and workload balance for virtual machines promoting an improved utilization of resources in the entire data center.

11. Estimating the basic reproduction rate of HFMD using the time series SIR model in Guangdong, China.

Directory of Open Access Journals (Sweden)

Zhicheng Du

Full Text Available Hand, foot, and mouth disease (HFMD has caused a substantial burden of disease in China, especially in Guangdong Province. Based on notifiable cases, we use the time series Susceptible-Infected-Recovered model to estimate the basic reproduction rate (R0 and the herd immunity threshold, understanding the transmission and persistence of HFMD more completely for efficient intervention in this province. The standardized difference between the reported and fitted time series of HFMD was 0.009 (<0.2. The median basic reproduction rate of total, enterovirus 71, and coxsackievirus 16 cases in Guangdong were 4.621 (IQR: 3.907-5.823, 3.023 (IQR: 2.289-4.292 and 7.767 (IQR: 6.903-10.353, respectively. The heatmap of R0 showed semiannual peaks of activity, including a major peak in spring and early summer (about the 12th week followed by a smaller peak in autumn (about the 36th week. The county-level model showed that Longchuan (R0 = 33, Gaozhou (R0 = 24, Huazhou (R0 = 23 and Qingxin (R0 = 19 counties have higher basic reproduction rate than other counties in the province. The epidemic of HFMD in Guangdong Province is still grim, and strategies like the World Health Organization's expanded program on immunization need to be implemented. An elimination of HFMD in Guangdong might need a Herd Immunity Threshold of 78%.

12. Stochastic models in the DORIS position time series: estimates for IDS contribution to ITRF2014

Science.gov (United States)

Klos, Anna; Bogusz, Janusz; Moreaux, Guilhem

2017-11-01

This paper focuses on the investigation of the deterministic and stochastic parts of the Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) weekly time series aligned to the newest release of ITRF2014. A set of 90 stations was divided into three groups depending on when the data were collected at an individual station. To reliably describe the DORIS time series, we employed a mathematical model that included the long-term nonlinear signal, linear trend, seasonal oscillations and a stochastic part, all being estimated with maximum likelihood estimation. We proved that the values of the parameters delivered for DORIS data are strictly correlated with the time span of the observations. The quality of the most recent data has significantly improved. Not only did the seasonal amplitudes decrease over the years, but also, and most importantly, the noise level and its type changed significantly. Among several tested models, the power-law process may be chosen as the preferred one for most of the DORIS data. Moreover, the preferred noise model has changed through the years from an autoregressive process to pure power-law noise with few stations characterised by a positive spectral index. For the latest observations, the medians of the velocity errors were equal to 0.3, 0.3 and 0.4 mm/year, respectively, for the North, East and Up components. In the best cases, a velocity uncertainty of DORIS sites of 0.1 mm/year is achievable when the appropriate coloured noise model is taken into consideration.

13. Inference of quantitative models of bacterial promoters from time-series reporter gene data.

Science.gov (United States)

Stefan, Diana; Pinel, Corinne; Pinhal, Stéphane; Cinquemani, Eugenio; Geiselmann, Johannes; de Jong, Hidde

2015-01-01

The inference of regulatory interactions and quantitative models of gene regulation from time-series transcriptomics data has been extensively studied and applied to a range of problems in drug discovery, cancer research, and biotechnology. The application of existing methods is commonly based on implicit assumptions on the biological processes under study. First, the measurements of mRNA abundance obtained in transcriptomics experiments are taken to be representative of protein concentrations. Second, the observed changes in gene expression are assumed to be solely due to transcription factors and other specific regulators, while changes in the activity of the gene expression machinery and other global physiological effects are neglected. While convenient in practice, these assumptions are often not valid and bias the reverse engineering process. Here we systematically investigate, using a combination of models and experiments, the importance of this bias and possible corrections. We measure in real time and in vivo the activity of genes involved in the FliA-FlgM module of the E. coli motility network. From these data, we estimate protein concentrations and global physiological effects by means of kinetic models of gene expression. Our results indicate that correcting for the bias of commonly-made assumptions improves the quality of the models inferred from the data. Moreover, we show by simulation that these improvements are expected to be even stronger for systems in which protein concentrations have longer half-lives and the activity of the gene expression machinery varies more strongly across conditions than in the FliA-FlgM module. The approach proposed in this study is broadly applicable when using time-series transcriptome data to learn about the structure and dynamics of regulatory networks. In the case of the FliA-FlgM module, our results demonstrate the importance of global physiological effects and the active regulation of FliA and FlgM half-lives for

14. Posterior-Predictive Evidence on US Inflation using Phillips Curve Models with Non-Filtered Time Series

NARCIS (Netherlands)

N. Basturk (Nalan); C. Cakmakli (Cem); S.P. Ceyhan (Pinar); H.K. van Dijk (Herman)

2012-01-01

textabstractChanging time series properties of US inflation and economic activity are analyzed within a class of extended Phillips Curve (PC) models. First, the misspecification effects of mechanical removal of low frequency movements of these series on posterior inference of a basic PC model are

15. Posterior-Predictive Evidence on US Inflation using Phillips Curve Models with Non-Filtered Time Series

NARCIS (Netherlands)

Basturk, N.; Cakmakli, C.; Ceyhan, P.; van Dijk, H.K.

2013-01-01

Changing time series properties of US inflation and economic activity are analyzed within a class of extended Phillips Curve (PC) models. First, the misspecification effects of mechanical removal of low frequency movements of these series on posterior inference of a basic PC model are analyzed using

16. Time series modeling of soil moisture dynamics on a steep mountainous hillside

Science.gov (United States)

Kim, Sanghyun

2016-05-01

The response of soil moisture to rainfall events along hillslope transects is an important hydrologic process and a critical component of interactions between soil vegetation and the atmosphere. In this context, the research described in this article addresses the spatial distribution of soil moisture as a function of topography. In order to characterize the temporal variation in soil moisture on a steep mountainous hillside, a transfer function, including a model for noise, was introduced. Soil moisture time series with similar rainfall amounts, but different wetness gradients were measured in the spring and fall. Water flux near the soil moisture sensors was modeled and mathematical expressions were developed to provide a basis for input-output modeling of rainfall and soil moisture using hydrological processes such as infiltration, exfiltration and downslope lateral flow. The characteristics of soil moisture response can be expressed in terms of model structure. A seasonal comparison of models reveals differences in soil moisture response to rainfall, possibly associated with eco-hydrological process and evapotranspiration. Modeling results along the hillslope indicate that the spatial structure of the soil moisture response patterns mainly appears in deeper layers. Similarities between topographic attributes and stochastic model structures are spatially organized. The impact of temporal and spatial discretization scales on parameter expression is addressed in the context of modeling results that link rainfall events and soil moisture.

17. Evaluation of a Global Vegetation Model using time series of satellite vegetation indices

Directory of Open Access Journals (Sweden)

F. Maignan

2011-12-01

Full Text Available Atmospheric CO2 drives most of the greenhouse effect increase. One major uncertainty on the future rate of increase of CO2 in the atmosphere is the impact of the anticipated climate change on the vegetation. Dynamic Global Vegetation Models (DGVM are used to address this question. ORCHIDEE is such a DGVM that has proven useful for climate change studies. However, there is no objective and methodological way to accurately assess each new available version on the global scale. In this paper, we submit a methodological evaluation of ORCHIDEE by correlating satellite-derived Vegetation Index time series against those of the modeled Fraction of absorbed Photosynthetically Active Radiation (FPAR. A perfect correlation between the two is not expected, however an improvement of the model should lead to an increase of the overall performance.

We detail two case studies in which model improvements are demonstrated, using our methodology. In the first one, a new phenology version in ORCHIDEE is shown to bring a significant impact on the simulated annual cycles, in particular for C3 Grasses and C3 Crops. In the second case study, we compare the simulations when using two different weather fields to drive ORCHIDEE. The ERA-Interim forcing leads to a better description of the FPAR interannual anomalies than the simulation forced by a mixed CRU-NCEP dataset. This work shows that long time series of satellite observations, despite their uncertainties, can identify weaknesses in global vegetation models, a necessary first step to improving them.

18. Time Series Neural Network Model for Part-of-Speech Tagging Indonesian Language

Science.gov (United States)

2018-03-01

Part-of-speech tagging (POS tagging) is an important part in natural language processing. Many methods have been used to do this task, including neural network. This paper models a neural network that attempts to do POS tagging. A time series neural network is modelled to solve the problems that a basic neural network faces when attempting to do POS tagging. In order to enable the neural network to have text data input, the text data will get clustered first using Brown Clustering, resulting a binary dictionary that the neural network can use. To further the accuracy of the neural network, other features such as the POS tag, suffix, and affix of previous words would also be fed to the neural network.

19. Time series models of decadal trends in the harmful algal species Karlodinium veneficum in Chesapeake Bay.

Science.gov (United States)

Lin, Chih-Hsien Michelle; Lyubchich, Vyacheslav; Glibert, Patricia M

2018-03-01

The harmful dinoflagellate, Karlodnium veneficum, has been implicated in fish-kill and other toxic, harmful algal bloom (HAB) events in waters worldwide. Blooms of K. veneficum are known to be related to coastal nutrient enrichment but the relationship is complex because this HAB taxon relies not only on dissolved nutrients but also particulate prey, both of which have also changed over time. Here, applying cross-correlations of climate-related physical factors, nutrients and prey, with abundance of K. veneficum over a 10-year (2002-2011) period, a synthesis of the interactive effects of multiple factors on this species was developed for Chesapeake Bay, where blooms of the HAB have been increasing. Significant upward trends in the time series of K. veneficum were observed in the mesohaline stations of the Bay, but not in oligohaline tributary stations. For the mesohaline regions, riverine sources of nutrients with seasonal lags, together with particulate prey with zero lag, explained 15%-46% of the variation in the K. veneficum time series. For the oligohaline regions, nutrients and particulate prey generally showed significant decreasing trends with time, likely a reflection of nutrient reduction efforts. A conceptual model of mid-Bay blooms is presented, in which K. veneficum, derived from the oceanic end member of the Bay, may experience enhanced growth if it encounters prey originating from the tributaries with different patterns of nutrient loading and which are enriched in nitrogen. For all correlation models developed herein, prey abundance was a primary factor in predicting K. veneficum abundance. Copyright © 2018 Elsevier B.V. All rights reserved.

20. Streamflow characteristics from modelled runoff time series: Importance of calibration criteria selection

Science.gov (United States)

Poole, Sandra; Vis, Marc; Knight, Rodney; Seibert, Jan

2017-01-01

Ecologically relevant streamflow characteristics (SFCs) of ungauged catchments are often estimated from simulated runoff of hydrologic models that were originally calibrated on gauged catchments. However, SFC estimates of the gauged donor catchments and subsequently the ungauged catchments can be substantially uncertain when models are calibrated using traditional approaches based on optimization of statistical performance metrics (e.g., Nash–Sutcliffe model efficiency). An improved calibration strategy for gauged catchments is therefore crucial to help reduce the uncertainties of estimated SFCs for ungauged catchments. The aim of this study was to improve SFC estimates from modeled runoff time series in gauged catchments by explicitly including one or several SFCs in the calibration process. Different types of objective functions were defined consisting of the Nash–Sutcliffe model efficiency, single SFCs, or combinations thereof. We calibrated a bucket-type runoff model (HBV – Hydrologiska Byråns Vattenavdelning – model) for 25 catchments in the Tennessee River basin and evaluated the proposed calibration approach on 13 ecologically relevant SFCs representing major flow regime components and different flow conditions. While the model generally tended to underestimate the tested SFCs related to mean and high-flow conditions, SFCs related to low flow were generally overestimated. The highest estimation accuracies were achieved by a SFC-specific model calibration. Estimates of SFCs not included in the calibration process were of similar quality when comparing a multi-SFC calibration approach to a traditional model efficiency calibration. For practical applications, this implies that SFCs should preferably be estimated from targeted runoff model calibration, and modeled estimates need to be carefully interpreted.

1. Streamflow characteristics from modeled runoff time series - importance of calibration criteria selection

Science.gov (United States)

Pool, Sandra; Vis, Marc J. P.; Knight, Rodney R.; Seibert, Jan

2017-11-01

Ecologically relevant streamflow characteristics (SFCs) of ungauged catchments are often estimated from simulated runoff of hydrologic models that were originally calibrated on gauged catchments. However, SFC estimates of the gauged donor catchments and subsequently the ungauged catchments can be substantially uncertain when models are calibrated using traditional approaches based on optimization of statistical performance metrics (e.g., Nash-Sutcliffe model efficiency). An improved calibration strategy for gauged catchments is therefore crucial to help reduce the uncertainties of estimated SFCs for ungauged catchments. The aim of this study was to improve SFC estimates from modeled runoff time series in gauged catchments by explicitly including one or several SFCs in the calibration process. Different types of objective functions were defined consisting of the Nash-Sutcliffe model efficiency, single SFCs, or combinations thereof. We calibrated a bucket-type runoff model (HBV - Hydrologiska Byråns Vattenavdelning - model) for 25 catchments in the Tennessee River basin and evaluated the proposed calibration approach on 13 ecologically relevant SFCs representing major flow regime components and different flow conditions. While the model generally tended to underestimate the tested SFCs related to mean and high-flow conditions, SFCs related to low flow were generally overestimated. The highest estimation accuracies were achieved by a SFC-specific model calibration. Estimates of SFCs not included in the calibration process were of similar quality when comparing a multi-SFC calibration approach to a traditional model efficiency calibration. For practical applications, this implies that SFCs should preferably be estimated from targeted runoff model calibration, and modeled estimates need to be carefully interpreted.

2. Knowledge fusion: Time series modeling followed by pattern recognition applied to unusual sections of background data

International Nuclear Information System (INIS)

Burr, T.; Doak, J.; Howell, J.A.; Martinez, D.; Strittmatter, R.

1996-03-01

This report describes work performed during FY 95 for the Knowledge Fusion Project, which by the Department of Energy, Office of Nonproliferation and National Security. The project team selected satellite sensor data as the one main example to which its analysis algorithms would be applied. The specific sensor-fusion problem has many generic features that make it a worthwhile problem to attempt to solve in a general way. The generic problem is to recognize events of interest from multiple time series in a possibly noisy background. By implementing a suite of time series modeling and forecasting methods and using well-chosen alarm criteria, we reduce the number of false alarms. We then further reduce the number of false alarms by analyzing all suspicious sections of data, as judged by the alarm criteria, with pattern recognition methods. This report describes the implementation and application of this two-step process for separating events from unusual background. As a fortunate by-product of this activity, it is possible to gain a better understanding of the natural background

3. Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks.

Science.gov (United States)

Kane, Michael J; Price, Natalie; Scotch, Matthew; Rabinowitz, Peter

2014-08-13

Time series models can play an important role in disease prediction. Incidence data can be used to predict the future occurrence of disease events. Developments in modeling approaches provide an opportunity to compare different time series models for predictive power. We applied ARIMA and Random Forest time series models to incidence data of outbreaks of highly pathogenic avian influenza (H5N1) in Egypt, available through the online EMPRES-I system. We found that the Random Forest model outperformed the ARIMA model in predictive ability. Furthermore, we found that the Random Forest model is effective for predicting outbreaks of H5N1 in Egypt. Random Forest time series modeling provides enhanced predictive ability over existing time series models for the prediction of infectious disease outbreaks. This result, along with those showing the concordance between bird and human outbreaks (Rabinowitz et al. 2012), provides a new approach to predicting these dangerous outbreaks in bird populations based on existing, freely available data. Our analysis uncovers the time-series structure of outbreak severity for highly pathogenic avain influenza (H5N1) in Egypt.

4. Deterministic decomposition and seasonal ARIMA time series models applied to airport noise forecasting

Science.gov (United States)

Guarnaccia, Claudio; Quartieri, Joseph; Tepedino, Carmine

2017-06-01

One of the most hazardous physical polluting agents, considering their effects on human health, is acoustical noise. Airports are a strong source of acoustical noise, due to the airplanes turbines, to the aero-dynamical noise of transits, to the acceleration or the breaking during the take-off and landing phases of aircrafts, to the road traffic around the airport, etc.. The monitoring and the prediction of the acoustical level emitted by airports can be very useful to assess the impact on human health and activities. In the airports noise scenario, thanks to flights scheduling, the predominant sources may have a periodic behaviour. Thus, a Time Series Analysis approach can be adopted, considering that a general trend and a seasonal behaviour can be highlighted and used to build a predictive model. In this paper, two different approaches are adopted, thus two predictive models are constructed and tested. The first model is based on deterministic decomposition and is built composing the trend, that is the long term behaviour, the seasonality, that is the periodic component, and the random variations. The second model is based on seasonal autoregressive moving average, and it belongs to the stochastic class of models. The two different models are fitted on an acoustical level dataset collected close to the Nice (France) international airport. Results will be encouraging and will show good prediction performances of both the adopted strategies. A residual analysis is performed, in order to quantify the forecasting error features.

5. A Long-Term Prediction Model of Beijing Haze Episodes Using Time Series Analysis

Directory of Open Access Journals (Sweden)

Xiaoping Yang

2016-01-01

Full Text Available The rapid industrial development has led to the intermittent outbreak of pm2.5 or haze in developing countries, which has brought about great environmental issues, especially in big cities such as Beijing and New Delhi. We investigated the factors and mechanisms of haze change and present a long-term prediction model of Beijing haze episodes using time series analysis. We construct a dynamic structural measurement model of daily haze increment and reduce the model to a vector autoregressive model. Typical case studies on 886 continuous days indicate that our model performs very well on next day’s Air Quality Index (AQI prediction, and in severely polluted cases (AQI ≥ 300 the accuracy rate of AQI prediction even reaches up to 87.8%. The experiment of one-week prediction shows that our model has excellent sensitivity when a sudden haze burst or dissipation happens, which results in good long-term stability on the accuracy of the next 3–7 days’ AQI prediction.

6. Four simultaneous component models for the analysis of multivariate time series from more than one subject to model intraindividual and interindividual differences

NARCIS (Netherlands)

Timmerman, Mariek E.; Kiers, Henk A.L.

A class of four simultaneous component models for the exploratory analysis of multivariate time series collected from more than one subject simultaneously is discussed. In each of the models, the multivariate time series of each subject is decomposed into a few series of component scores and a

7. Impact of different troposphere modelling methods on ZTD time series: case study of mountainous GPS stations

Science.gov (United States)

Stepniak, Katarzyna; Klos, Anna; Bock, Olivier; Bogusz, Janusz

2016-04-01

GNSS Zenith Total Delay (ZTD) data is useful for numerical weather forecasting and climate analysis. Considering the fact that tropospheric delays over the mountainous areas are the most difficult to be modelled, we explored the influence of different troposphere models in Precise Point Positioning (PPP) mode. We used GPS data from 2008 to 2014 at 28 permanent EUPOS (European Position Determination System) stations, including 9 EPN (EUREF Permanent Network) ones, located in the Sudeten and Carpathians. The GPS data was processed in PPP mode using Bernese 5.2 GNSS software with the final IGS (International GNSS Service) orbits and clocks. Different processing variants were tested implying the newest mapping functions (Global Mapping Function - GMF, and Vienna Mapping Function - VMF1) as well as different time resolutions and constraints on estimated parameters (ZTD and gradients). Median trends and amplitudes of annual/semi-annual oscillations for ZTD series were determined with Weighted Least Squares Estimation (WLSE) obtaining 0.1±0.5 mm/year and 44.7 / 7.2 ± 5 mm, respectively. Power Spectral Densities (PSDs) were estimated using Lomb-Scargle method for each of individual variants. PSDs showed, except oscillations of year and half a year, many other significant peaks in ZTD time series at higher frequencies, about 60, 30, 24, 20, 15, 12, 10, 8, 7, 6, 5, 4 and 3 cpy. The proper subtraction of the periodicities is crucial, because they will make stochastic part appear to be artificially autocorrelated. In order to recognized the periodicities in the ZTD signal, we analyzed the ZTD differences between GPS-derived delays and ERA-Interim reanalysis. The results of analysis showed the significant change from station to station and between variants. According to these results the authors will indicate an optimal processing strategy concerning troposphere modelling.

8. An estimation model of population in China using time series DMSP night-time satellite imagery from 2002-2010

Science.gov (United States)

Zhang, Xiaoyong; Zhang, Zhijie; Chang, Yuguang; Chen, Zhengchao

2015-12-01

Accurate data on the spatial distribution and potential growth estimation of human population are playing pivotal role in addressing and mitigating heavy lose caused by earthquake. Traditional demographic data is limited in its spatial resolution and is extremely hard to update. With the accessibility of massive DMSP/OLS night time imagery, it is possible to model population distribution at the county level across China. In order to compare and improve the continuity and consistency of time-series DMSP night-time satellite imagery obtained by different satellites in same year or different years by the same satellite from 2002-2010, normalized method was deployed for the inter-correction among imageries. And we referred to the reference F162007 Jixi city, whose social-economic has been relatively stable. Through binomial model, with average R2 0.90, then derived the correction factor of each year. The normalization obviously improved consistency comparing to previous data, which enhanced the correspondent accuracy of model. Then conducted the model of population density between average night-time light intensity in eight-economic districts. According to the two parameters variation law of consecutive years, established the prediction model of next following years with R2of slope and constant typically 0.85 to 0.95 in different regions. To validate the model, taking the year of 2005 as example, retrieved quantitatively population distribution in per square kilometer based on the model, then compared the results to the statistical data based on census, the difference of the result is acceptable. In summary, the estimation model facilitates the quick estimation and prediction in relieving the damage to people, which is significant in decision-making.

9. Clustering gene expression time series data using an infinite Gaussian process mixture model.

Science.gov (United States)

McDowell, Ian C; Manandhar, Dinesh; Vockley, Christopher M; Schmid, Amy K; Reddy, Timothy E; Engelhardt, Barbara E

2018-01-01

Transcriptome-wide time series expression profiling is used to characterize the cellular response to environmental perturbations. The first step to analyzing transcriptional response data is often to cluster genes with similar responses. Here, we present a nonparametric model-based method, Dirichlet process Gaussian process mixture model (DPGP), which jointly models data clusters with a Dirichlet process and temporal dependencies with Gaussian processes. We demonstrate the accuracy of DPGP in comparison to state-of-the-art approaches using hundreds of simulated data sets. To further test our method, we apply DPGP to published microarray data from a microbial model organism exposed to stress and to novel RNA-seq data from a human cell line exposed to the glucocorticoid dexamethasone. We validate our clusters by examining local transcription factor binding and histone modifications. Our results demonstrate that jointly modeling cluster number and temporal dependencies can reveal shared regulatory mechanisms. DPGP software is freely available online at https://github.com/PrincetonUniversity/DP_GP_cluster.

10. Clustering gene expression time series data using an infinite Gaussian process mixture model.

Directory of Open Access Journals (Sweden)

Ian C McDowell

2018-01-01

Full Text Available Transcriptome-wide time series expression profiling is used to characterize the cellular response to environmental perturbations. The first step to analyzing transcriptional response data is often to cluster genes with similar responses. Here, we present a nonparametric model-based method, Dirichlet process Gaussian process mixture model (DPGP, which jointly models data clusters with a Dirichlet process and temporal dependencies with Gaussian processes. We demonstrate the accuracy of DPGP in comparison to state-of-the-art approaches using hundreds of simulated data sets. To further test our method, we apply DPGP to published microarray data from a microbial model organism exposed to stress and to novel RNA-seq data from a human cell line exposed to the glucocorticoid dexamethasone. We validate our clusters by examining local transcription factor binding and histone modifications. Our results demonstrate that jointly modeling cluster number and temporal dependencies can reveal shared regulatory mechanisms. DPGP software is freely available online at https://github.com/PrincetonUniversity/DP_GP_cluster.

11. The string prediction models as invariants of time series in the forex market

Science.gov (United States)

Pincak, R.

2013-12-01

In this paper we apply a new approach of string theory to the real financial market. The models are constructed with an idea of prediction models based on the string invariants (PMBSI). The performance of PMBSI is compared to support vector machines (SVM) and artificial neural networks (ANN) on an artificial and a financial time series. A brief overview of the results and analysis is given. The first model is based on the correlation function as invariant and the second one is an application based on the deviations from the closed string/pattern form (PMBCS). We found the difference between these two approaches. The first model cannot predict the behavior of the forex market with good efficiency in comparison with the second one which is, in addition, able to make relevant profit per year. The presented string models could be useful for portfolio creation and financial risk management in the banking sector as well as for a nonlinear statistical approach to data optimization.

12. Tracer kinetic model-driven registration for dynamic contrast-enhanced MRI time-series data.

Science.gov (United States)

Buonaccorsi, Giovanni A; O'Connor, James P B; Caunce, Angela; Roberts, Caleb; Cheung, Sue; Watson, Yvonne; Davies, Karen; Hope, Lynn; Jackson, Alan; Jayson, Gordon C; Parker, Geoffrey J M

2007-11-01

Dynamic contrast-enhanced MRI (DCE-MRI) time series data are subject to unavoidable physiological motion during acquisition (e.g., due to breathing) and this motion causes significant errors when fitting tracer kinetic models to the data, particularly with voxel-by-voxel fitting approaches. Motion correction is problematic, as contrast enhancement introduces new features into postcontrast images and conventional registration similarity measures cannot fully account for the increased image information content. A methodology is presented for tracer kinetic model-driven registration that addresses these problems by explicitly including a model of contrast enhancement in the registration process. The iterative registration procedure is focused on a tumor volume of interest (VOI), employing a three-dimensional (3D) translational transformation that follows only tumor motion. The implementation accurately removes motion corruption in a DCE-MRI software phantom and it is able to reduce model fitting errors and improve localization in 3D parameter maps in patient data sets that were selected for significant motion problems. Sufficient improvement was observed in the modeling results to salvage clinical trial DCE-MRI data sets that would otherwise have to be rejected due to motion corruption. Copyright 2007 Wiley-Liss, Inc.

13. Optimizing the De-Noise Neural Network Model for GPS Time-Series Monitoring of Structures

Directory of Open Access Journals (Sweden)

Mosbeh R. Kaloop

2015-09-01

Full Text Available The Global Positioning System (GPS is recently used widely in structures and other applications. Notwithstanding, the GPS accuracy still suffers from the errors afflicting the measurements, particularly the short-period displacement of structural components. Previously, the multi filter method is utilized to remove the displacement errors. This paper aims at using a novel application for the neural network prediction models to improve the GPS monitoring time series data. Four prediction models for the learning algorithms are applied and used with neural network solutions: back-propagation, Cascade-forward back-propagation, adaptive filter and extended Kalman filter, to estimate which model can be recommended. The noise simulation and bridge’s short-period GPS of the monitoring displacement component of one Hz sampling frequency are used to validate the four models and the previous method. The results show that the Adaptive neural networks filter is suggested for de-noising the observations, specifically for the GPS displacement components of structures. Also, this model is expected to have significant influence on the design of structures in the low frequency responses and measurements’ contents.

14. Bayesian models of thermal and pluviometric time series in the Fucino plateau

Directory of Open Access Journals (Sweden)

2011-09-01

Full Text Available This work was developed within the Project Metodologie e sistemi integrati per la qualificazione di produzioni orticole del Fucino (Methodologies and integrated systems for the classification of horticultural products in the Fucino plateau, sponsored by the Italian Ministry of Education, University and Research, Strategic Projects, Law 448/97. Agro-system managing, especially if necessary to achieve high quality in speciality crops, requires knowledge of main features and intrinsic variability of climate. Statistical models may properly summarize the structure existing behind the observed variability, furthermore they may support the agronomic manager by providing the probability that meteorological events happen in a time window of interest. More than 30 years of daily values collected in four sites located on the Fucino plateau, Abruzzo region, Italy, were studied by fitting Bayesian generalized linear models to air temperature maximum /minimum and rainfall time series. Bayesian predictive distributions of climate variables supporting decision-making processes were calculated at different timescales, 5-days for temperatures and 10-days for rainfall, both to reduce computational efforts and to simplify statistical model assumptions. Technicians and field operators, even with limited statistical training, may exploit the model output by inspecting graphs and climatic profiles of the cultivated areas during decision-making processes. Realizations taken from predictive distributions may also be used as input for agro-ecological models (e.g. models of crop growth, water balance. Fitted models may be exploited to monitor climatic changes and to revise climatic profiles of interest areas, periodically updating the probability distributions of target climatic variables. For the sake of brevity, the description of results is limited to just one of the four sites, and results for all other sites are available as supplementary information.

15. Identification of two-phase flow regimes by time-series modeling

International Nuclear Information System (INIS)

King, C.H.; Ouyang, M.S.; Pei, B.S.

1987-01-01

The identification of two-phase flow patterns in pipes or ducts is important to the design and operation of thermal-hydraulic systems, especially in the nuclear reactor cores of boiling water reactors or in the steam generators of pressurized water reactors. Basically, two-phase flow shows some fluctuating characteristics even at steady-state conditions. These fluctuating characteristics can be analyzed by statistical methods for obtaining flow signatures. There have been a number of experimental studies conducted that are concerned with the statistical properties of void fraction or pressure pulsation in two-phase flow. In this study, the authors propose a new technique of identifying the patterns of air-water two-phase flow in a vertical pipe. This technique is based on analyzing the statistic characteristics of the pressure signals of the test loop by time-series modeling

16. Fast and Scalable Gaussian Process Modeling with Applications to Astronomical Time Series

Science.gov (United States)

Foreman-Mackey, Daniel; Agol, Eric; Ambikasaran, Sivaram; Angus, Ruth

2017-12-01

The growing field of large-scale time domain astronomy requires methods for probabilistic data analysis that are computationally tractable, even with large data sets. Gaussian processes (GPs) are a popular class of models used for this purpose, but since the computational cost scales, in general, as the cube of the number of data points, their application has been limited to small data sets. In this paper, we present a novel method for GPs modeling in one dimension where the computational requirements scale linearly with the size of the data set. We demonstrate the method by applying it to simulated and real astronomical time series data sets. These demonstrations are examples of probabilistic inference of stellar rotation periods, asteroseismic oscillation spectra, and transiting planet parameters. The method exploits structure in the problem when the covariance function is expressed as a mixture of complex exponentials, without requiring evenly spaced observations or uniform noise. This form of covariance arises naturally when the process is a mixture of stochastically driven damped harmonic oscillators—providing a physical motivation for and interpretation of this choice—but we also demonstrate that it can be a useful effective model in some other cases. We present a mathematical description of the method and compare it to existing scalable GP methods. The method is fast and interpretable, with a range of potential applications within astronomical data analysis and beyond. We provide well-tested and documented open-source implementations of this method in C++, Python, and Julia.

17. GPS Position Time Series @ JPL

Science.gov (United States)

Owen, Susan; Moore, Angelyn; Kedar, Sharon; Liu, Zhen; Webb, Frank; Heflin, Mike; Desai, Shailen

2013-01-01

Different flavors of GPS time series analysis at JPL - Use same GPS Precise Point Positioning Analysis raw time series - Variations in time series analysis/post-processing driven by different users. center dot JPL Global Time Series/Velocities - researchers studying reference frame, combining with VLBI/SLR/DORIS center dot JPL/SOPAC Combined Time Series/Velocities - crustal deformation for tectonic, volcanic, ground water studies center dot ARIA Time Series/Coseismic Data Products - Hazard monitoring and response focused center dot ARIA data system designed to integrate GPS and InSAR - GPS tropospheric delay used for correcting InSAR - Caltech's GIANT time series analysis uses GPS to correct orbital errors in InSAR - Zhen Liu's talking tomorrow on InSAR Time Series analysis

18. Harmonic analysis of dense time series of landsat imagery for modeling change in forest conditions

Science.gov (United States)

Barry Tyler. Wilson

2015-01-01

This study examined the utility of dense time series of Landsat imagery for small area estimation and mapping of change in forest conditions over time. The study area was a region in north central Wisconsin for which Landsat 7 ETM+ imagery and field measurements from the Forest Inventory and Analysis program are available for the decade of 2003 to 2012. For the periods...

19. Beyond Rating Curves: Time Series Models for in-Stream Turbidity Prediction

Science.gov (United States)

Wang, L.; Mukundan, R.; Zion, M.; Pierson, D. C.

2012-12-01

The New York City Department of Environmental Protection (DEP) manages New York City's water supply, which is comprised of over 20 reservoirs and supplies over 1 billion gallons of water per day to more than 9 million customers. DEP's "West of Hudson" reservoirs located in the Catskill Mountains are unfiltered per a renewable filtration avoidance determination granted by the EPA. While water quality is usually pristine, high volume storm events occasionally cause the reservoirs to become highly turbid. A logical strategy for turbidity control is to temporarily remove the turbid reservoirs from service. While effective in limiting delivery of turbid water and reducing the need for in-reservoir alum flocculation, this strategy runs the risk of negatively impacting water supply reliability. Thus, it is advantageous for DEP to understand how long a particular turbidity event will affect their system. In order to understand the duration, intensity and total load of a turbidity event, predictions of future in-stream turbidity values are important. Traditionally, turbidity predictions have been carried out by applying streamflow observations/forecasts to a flow-turbidity rating curve. However, predictions from rating curves are often inaccurate due to inter- and intra-event variability in flow-turbidity relationships. Predictions can be improved by applying an autoregressive moving average (ARMA) time series model in combination with a traditional rating curve. Since 2003, DEP and the Upstate Freshwater Institute have compiled a relatively consistent set of 15-minute turbidity observations at various locations on Esopus Creek above Ashokan Reservoir. Using daily averages of this data and streamflow observations at nearby USGS gauges, flow-turbidity rating curves were developed via linear regression. Time series analysis revealed that the linear regression residuals may be represented using an ARMA(1,2) process. Based on this information, flow-turbidity regressions with

20. A scalable database model for multiparametric time series: a volcano observatory case study

Science.gov (United States)

Montalto, Placido; Aliotta, Marco; Cassisi, Carmelo; Prestifilippo, Michele; Cannata, Andrea

2014-05-01

The variables collected by a sensor network constitute a heterogeneous data source that needs to be properly organized in order to be used in research and geophysical monitoring. With the time series term we refer to a set of observations of a given phenomenon acquired sequentially in time. When the time intervals are equally spaced one speaks of period or sampling frequency. Our work describes in detail a possible methodology for storage and management of time series using a specific data structure. We designed a framework, hereinafter called TSDSystem (Time Series Database System), in order to acquire time series from different data sources and standardize them within a relational database. The operation of standardization provides the ability to perform operations, such as query and visualization, of many measures synchronizing them using a common time scale. The proposed architecture follows a multiple layer paradigm (Loaders layer, Database layer and Business Logic layer). Each layer is specialized in performing particular operations for the reorganization and archiving of data from different sources such as ASCII, Excel, ODBC (Open DataBase Connectivity), file accessible from the Internet (web pages, XML). In particular, the loader layer performs a security check of the working status of each running software through an heartbeat system, in order to automate the discovery of acquisition issues and other warning conditions. Although our system has to manage huge amounts of data, performance is guaranteed by using a smart partitioning table strategy, that keeps balanced the percentage of data stored in each database table. TSDSystem also contains modules for the visualization of acquired data, that provide the possibility to query different time series on a specified time range, or follow the realtime signal acquisition, according to a data access policy from the users.

1. Assessment and prediction of road accident injuries trend using time-series models in Kurdistan.

Science.gov (United States)

Parvareh, Maryam; Karimi, Asrin; Rezaei, Satar; Woldemichael, Abraha; Nili, Sairan; Nouri, Bijan; Nasab, Nader Esmail

2018-01-01

Road traffic accidents are commonly encountered incidents that can cause high-intensity injuries to the victims and have direct impacts on the members of the society. Iran has one of the highest incident rates of road traffic accidents. The objective of this study was to model the patterns of road traffic accidents leading to injury in Kurdistan province, Iran. A time-series analysis was conducted to characterize and predict the frequency of road traffic accidents that lead to injury in Kurdistan province. The injuries were categorized into three separate groups which were related to the car occupants, motorcyclists and pedestrian road traffic accident injuries. The Box-Jenkins time-series analysis was used to model the injury observations applying autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) from March 2009 to February 2015 and to predict the accidents up to 24 months later (February 2017). The analysis was carried out using R-3.4.2 statistical software package. A total of 5199 pedestrians, 9015 motorcyclists, and 28,906 car occupants' accidents were observed. The mean (SD) number of car occupant, motorcyclist and pedestrian accident injuries observed were 401.01 (SD 32.78), 123.70 (SD 30.18) and 71.19 (SD 17.92) per year, respectively. The best models for the pattern of car occupant, motorcyclist, and pedestrian injuries were the ARIMA (1, 0, 0), SARIMA (1, 0, 2) (1, 0, 0) 12 , and SARIMA (1, 1, 1) (0, 0, 1) 12 , respectively. The motorcyclist and pedestrian injuries showed a seasonal pattern and the peak was during summer (August). The minimum frequency for the motorcyclist and pedestrian injuries were observed during the late autumn and early winter (December and January). Our findings revealed that the observed motorcyclist and pedestrian injuries had a seasonal pattern that was explained by air temperature changes overtime. These findings call the need for close monitoring of the

2. FOURIER SERIES MODELS THROUGH TRANSFORMATION

African Journals Online (AJOL)

DEPT

This study considers the application of Fourier series analysis (FSA) to seasonal time series data. The ultimate objective of the study is to construct an FSA model that can lead to reliable forecast. Specifically, the study evaluates data for the assumptions of time series analysis; applies the necessary transformation to the ...

3. Nonparametric model reconstruction for stochastic differential equations from discretely observed time-series data.

Science.gov (United States)

Ohkubo, Jun

2011-12-01

A scheme is developed for estimating state-dependent drift and diffusion coefficients in a stochastic differential equation from time-series data. The scheme does not require to specify parametric forms for the drift and diffusion coefficients in advance. In order to perform the nonparametric estimation, a maximum likelihood method is combined with a concept based on a kernel density estimation. In order to deal with discrete observation or sparsity of the time-series data, a local linearization method is employed, which enables a fast estimation.

4. A comparative study of shallow groundwater level simulation with three time series models in a coastal aquifer of South China

Science.gov (United States)

Yang, Q.; Wang, Y.; Zhang, J.; Delgado, J.

2017-05-01

Accurate and reliable groundwater level forecasting models can help ensure the sustainable use of a watershed's aquifers for urban and rural water supply. In this paper, three time series analysis methods, Holt-Winters (HW), integrated time series (ITS), and seasonal autoregressive integrated moving average (SARIMA), are explored to simulate the groundwater level in a coastal aquifer, China. The monthly groundwater table depth data collected in a long time series from 2000 to 2011 are simulated and compared with those three time series models. The error criteria are estimated using coefficient of determination ( R 2), Nash-Sutcliffe model efficiency coefficient ( E), and root-mean-squared error. The results indicate that three models are all accurate in reproducing the historical time series of groundwater levels. The comparisons of three models show that HW model is more accurate in predicting the groundwater levels than SARIMA and ITS models. It is recommended that additional studies explore this proposed method, which can be used in turn to facilitate the development and implementation of more effective and sustainable groundwater management strategies.

5. Financial Time Series Modelling with Hybrid Model Based on Customized RBF Neural Network Combined With Genetic Algorithm

Directory of Open Access Journals (Sweden)

Lukas Falat

2014-01-01

Full Text Available In this paper, authors apply feed-forward artificial neural network (ANN of RBF type into the process of modelling and forecasting the future value of USD/CAD time series. Authors test the customized version of the RBF and add the evolutionary approach into it. They also combine the standard algorithm for adapting weights in neural network with an unsupervised clustering algorithm called K-means. Finally, authors suggest the new hybrid model as a combination of a standard ANN and a moving average for error modeling that is used to enhance the outputs of the network using the error part of the original RBF. Using high-frequency data, they examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, authors perform the comparative out-of-sample analysis of the suggested hybrid model with statistical models and the standard neural network.

6. Linear time series modeling of GPS-derived TEC observations over the Indo-Thailand region

Science.gov (United States)

Suraj, Puram Sai; Kumar Dabbakuti, J. R. K.; Chowdhary, V. Rajesh; Tripathi, Nitin K.; Ratnam, D. Venkata

2017-12-01

This paper proposes a linear time series model to represent the climatology of the ionosphere and to investigate the characteristics of hourly averaged total electron content (TEC). The GPS-TEC observation data at the Bengaluru international global navigation satellite system (GNSS) service (IGS) station (geographic 13.02°N , 77.57°E ; geomagnetic latitude 4.4°N ) have been utilized for processing the TEC data during an extended period (2009-2016) in the 24{th} solar cycle. Solar flux F10.7p index, geomagnetic Ap index, and periodic oscillation factors have been considered to construct a linear TEC model. It is evident from the results that solar activity effect on TEC is high. It reaches the maximum value (˜ 40 TECU) during the high solar activity (HSA) year (2014) and minimum value (˜ 15 TECU) during the low solar activity (LSA) year (2009). The larger magnitudes of semiannual variations are observed during the HSA periods. The geomagnetic effect on TEC is relatively low, with the highest being ˜ 4 TECU (March 2015). The magnitude of periodic variations can be seen more significantly during HSA periods (2013-2015) and less during LSA periods (2009-2011). The correlation coefficient of 0.89 between the observations and model-based estimations has been found. The RMSE between the observed TEC and model TEC values is 4.0 TECU (linear model) and 4.21 TECU (IRI2016 Model). Further, the linear TEC model has been validated at different latitudes over the northern low-latitude region. The solar component (F10.7p index) value decreases with an increase in latitude. The magnitudes of the periodic component become less significant with the increase in latitude. The influence of geomagnetic component becomes less significant at Lucknow GNSS station (26.76°N, 80.88°E) when compared to other GNSS stations. The hourly averaged TEC values have been considered and ionospheric features are well recovered with linear TEC model.

7. Linear genetic programming for time-series modelling of daily flow rate

two versions of Neural Networks (NNs) are used in predicting time-series of daily flow rates at a station on Schuylkill River at Berne, PA, .... function estimate directly from the training data. (Cigizoglu and Alp 2006). ..... Note: Qmean – mean observed discharge, Sx – standard deviation, Qmin – mini- mum observed discharge ...

8. SAX-VSM: Interpretable Time Series Classification Using SAX and Vector Space Model

Science.gov (United States)

2013-01-01

satisfactory partitioning on short synthetic data sets. Further, we evaluated our technique on the long time series from PhysioNet archive [40]. We...PhysioBank, PhysioToolkit, and PhysioNet : Circulation. Discovery 101(23), 1(3), 215–220 (1997) [41] Gavrilov, M., Anguelov, D., Indyk, P., Motwani, R

9. Development of a real-time prediction model of driver behavior at intersections using kinematic time series data.

Science.gov (United States)

Tan, Yaoyuan V; Elliott, Michael R; Flannagan, Carol A C

2017-09-01

As connected autonomous vehicles (CAVs) enter the fleet, there will be a long period when these vehicles will have to interact with human drivers. One of the challenges for CAVs is that human drivers do not communicate their decisions well. Fortunately, the kinematic behavior of a human-driven vehicle may be a good predictor of driver intent within a short time frame. We analyzed the kinematic time series data (e.g., speed) for a set of drivers making left turns at intersections to predict whether the driver would stop before executing the turn. We used principal components analysis (PCA) to generate independent dimensions that explain the variation in vehicle speed before a turn. These dimensions remained relatively consistent throughout the maneuver, allowing us to compute independent scores on these dimensions for different time windows throughout the approach to the intersection. We then linked these PCA scores to whether a driver would stop before executing a left turn using the random intercept Bayesian additive regression trees. Five more road and observable vehicle characteristics were included to enhance prediction. Our model achieved an area under the receiver operating characteristic curve (AUC) of 0.84 at 94m away from the center of an intersection and steadily increased to 0.90 by 46m away from the center of an intersection. Copyright © 2017 Elsevier Ltd. All rights reserved.

10. A COMPARATIVE STUDY OF SIMULATION AND TIME SERIES MODEL IN QUANTIFYING BULLWHIP EFFECT IN SUPPLY CHAIN

Directory of Open Access Journals (Sweden)

T. V. O. Fabson

2011-11-01

Full Text Available Bullwhip (or whiplash effect is an observed phenomenon in forecast driven distribution channeland careful management of these effects is of great importance to managers of supply chain.Bullwhip effect refers to situations where orders to the suppliers tend to have larger variance thansales to the buyer (demand distortion and the distortion increases as we move up the supply chain.Due to the fact that demand of customer for product is unstable, business managers must forecast inorder to properly position inventory and other resources. Forecasts are statistically based and in mostcases, are not very accurate. The existence of forecast errors made it necessary for organizations tooften carry an inventory buffer called “safety stock”. Moving up the supply chain from the end userscustomers to raw materials supplier there is a lot of variation in demand that can be observed, whichcall for greater need for safety stock.This study compares the efficacy of simulation and Time Series model in quantifying the bullwhipeffects in supply chain management.

11. Time Series Analysis and Forecasting by Example

CERN Document Server

Bisgaard, Soren

2011-01-01

An intuition-based approach enables you to master time series analysis with ease Time Series Analysis and Forecasting by Example provides the fundamental techniques in time series analysis using various examples. By introducing necessary theory through examples that showcase the discussed topics, the authors successfully help readers develop an intuitive understanding of seemingly complicated time series models and their implications. The book presents methodologies for time series analysis in a simplified, example-based approach. Using graphics, the authors discuss each presented example in

12. An Algorithm for Modified Times Series Analysis Method for Modeling and Prognosis of the River Water Quality

Directory of Open Access Journals (Sweden)

Petrov M.

2007-12-01

Full Text Available An algorithm and programs for modeling, analysis, and prognosis of river quality has been developed, which is a modified method of the time series analysis (TSA. The algorithm and program are used for modeling and prognosis of the river quality of Bulgarian river ecosystems.

13. Detecting geothermal anomalies and evaluating LST geothermal component by combining thermal remote sensing time series and land surface model data

NARCIS (Netherlands)

Romaguera, M.; Vaughan, R. G.; Ettema, J.; Izquierdo-Verdiguier, E.; Hecker, C. A.; van der Meer, F. D.

2017-01-01

This paper explores for the first time the possibilities to use two land surface temperature (LST) time series of different origins (geostationary Meteosat Second Generation satellite data and Noah land surface modelling, LSM), to detect geothermal anomalies and extract the geothermal component of

14. Spatiotemporal Characterization of Aquifers Using InSAR Time Series and Time-dependent Poroelastic Modeling in Phoenix, Arizona

Science.gov (United States)

Miller, M. M.; Shirzaei, M.

2014-12-01

Alluvial basins in Phoenix experience surface deformation due to large volumes of fluid withdrawn and added to aquifers. The spatiotemporal pattern of deformation is controlled by pumping and recharge rates, hydraulic boundaries, and properties such as diffusivity, transmissivity, and hydraulic conductivity. Land subsidence can cause damages to structures, earth fissures, and a permanent loss of aquifer storage; effects are often apparent after the onset of sustained events. Improving our understanding of the source and mechanisms of deformation is important for risk management and future planning. Monitoring subsidence and uplift using InSAR allows for detailed, dense spatial coverage with less than one cm measurement precision. Envisat data acquired from 2003-11 includes 38 ascending and 53 descending SAR images forming 239 and 423 coherent interferograms respectively. Displacement is separated into vertical and horizontal components by accounting for the satellite look angle and combining ascending and descending line of sight (LOS) data. Vertical velocity from Envisat reveals subsidence reaching -1.84 cm/yr and 0.60 cm/yr uplift. ERS 1&2 satellites delivered useful data from 1992-97, comprised of 6 ascending and 12 descending SAR images. Ascending images form 7 interferograms with LOS velocity from -1.23 to 1.65 cm/yr; descending images produce 25 interferograms with LOS velocity rates from -1.40 to 0.75 cm/yr. InSAR time series are compared with hydraulic head levels from 33 observation wells. Wavelet decomposition is used to separate the long-term, inelastic components from cyclic, elastic signals in InSAR and well level data. The specific storage coefficient, a parameter used in poroelastic models, is estimated as the ratio of cyclic vertical deformation to the equivalent component of the well level time series. Poroelastic theory assumes that pore pressure and fluid mass within the aquifer change during fluid withdrawal, while the relatively impermeable

15. Approaches in highly parameterized inversion: TSPROC, a general time-series processor to assist in model calibration and result summarization

Science.gov (United States)

Westenbroek, Stephen M.; Doherty, John; Walker, John F.; Kelson, Victor A.; Hunt, Randall J.; Cera, Timothy B.

2012-01-01

The TSPROC (Time Series PROCessor) computer software uses a simple scripting language to process and analyze time series. It was developed primarily to assist in the calibration of environmental models. The software is designed to perform calculations on time-series data commonly associated with surface-water models, including calculation of flow volumes, transformation by means of basic arithmetic operations, and generation of seasonal and annual statistics and hydrologic indices. TSPROC can also be used to generate some of the key input files required to perform parameter optimization by means of the PEST (Parameter ESTimation) computer software. Through the use of TSPROC, the objective function for use in the model-calibration process can be focused on specific components of a hydrograph.

16. Effective Feature Preprocessing for Time Series Forecasting

DEFF Research Database (Denmark)

Zhao, Junhua; Dong, Zhaoyang; Xu, Zhao

2006-01-01

Time series forecasting is an important area in data mining research. Feature preprocessing techniques have significant influence on forecasting accuracy, therefore are essential in a forecasting model. Although several feature preprocessing techniques have been applied in time series forecasting...... performance in time series forecasting. It is demonstrated in our experiment that, effective feature preprocessing can significantly enhance forecasting accuracy. This research can be a useful guidance for researchers on effectively selecting feature preprocessing techniques and integrating them with time...

17. Preliminary Study of Soil Available Nutrient Simulation Using a Modified WOFOST Model and Time-Series Remote Sensing Observations

OpenAIRE

Zhiqiang Cheng; Jihua Meng; Yanyou Qiao; Yiming Wang; Wenquan Dong; Yanxin Han

2018-01-01

The approach of using multispectral remote sensing (RS) to estimate soil available nutrients (SANs) has been recently developed and shows promising results. This method overcomes the limitations of commonly used methods by building a statistical model that connects RS-based crop growth and nutrient content. However, the stability and accuracy of this model require improvement. In this article, we replaced the statistical model by integrating the World Food Studies (WOFOST) model and time seri...

18. Time Series Analysis of a Principal-Agent Model to Assess Risk Shifting and Bargaining Power in Commodity Marketing Channels

NARCIS (Netherlands)

Kuiper, W.E.; Kuwornu, J.K.M.; Pennings, J.M.E.

2003-01-01

We apply the classic agency model to investigate risk shifting in an agricultural marketing channel, using time series analysis. We show that if the principal is risk-neutral and the agent is risk-averse instead of risk-neutral, then a linear contract can still be optimal if the fixed payment is

19. THE EFFECT OF DECOMPOSITION METHOD AS DATA PREPROCESSING ON NEURAL NETWORKS MODEL FOR FORECASTING TREND AND SEASONAL TIME SERIES

Directory of Open Access Journals (Sweden)

Subanar Subanar

2006-01-01

Full Text Available Recently, one of the central topics for the neural networks (NN community is the issue of data preprocessing on the use of NN. In this paper, we will investigate this topic particularly on the effect of Decomposition method as data processing and the use of NN for modeling effectively time series with both trend and seasonal patterns. Limited empirical studies on seasonal time series forecasting with neural networks show that some find neural networks are able to model seasonality directly and prior deseasonalization is not necessary, and others conclude just the opposite. In this research, we study particularly on the effectiveness of data preprocessing, including detrending and deseasonalization by applying Decomposition method on NN modeling and forecasting performance. We use two kinds of data, simulation and real data. Simulation data are examined on multiplicative of trend and seasonality patterns. The results are compared to those obtained from the classical time series model. Our result shows that a combination of detrending and deseasonalization by applying Decomposition method is the effective data preprocessing on the use of NN for forecasting trend and seasonal time series.

20. Time Series Analysis of Non-Gaussian Observations Based on State Space Models from Both Classical and Bayesian Perspectives

NARCIS (Netherlands)

Durbin, J.; Koopman, S.J.M.

1998-01-01

The analysis of non-Gaussian time series using state space models is considered from both classical and Bayesian perspectives. The treatment in both cases is based on simulation using importance sampling and antithetic variables; Monte Carlo Markov chain methods are not employed. Non-Gaussian

1. Fuzzy time series forecasting model with natural partitioning length approach for predicting the unemployment rate under different degree of confidence

Science.gov (United States)

Ramli, Nazirah; Mutalib, Siti Musleha Ab; Mohamad, Daud

2017-08-01

Fuzzy time series forecasting model has been proposed since 1993 to cater for data in linguistic values. Many improvement and modification have been made to the model such as enhancement on the length of interval and types of fuzzy logical relation. However, most of the improvement models represent the linguistic term in the form of discrete fuzzy sets. In this paper, fuzzy time series model with data in the form of trapezoidal fuzzy numbers and natural partitioning length approach is introduced for predicting the unemployment rate. Two types of fuzzy relations are used in this study which are first order and second order fuzzy relation. This proposed model can produce the forecasted values under different degree of confidence.

2. The projection of world geothermal energy consumption from time series and regression model

Science.gov (United States)

Simanullang, Elwin Y.; Supriatna, Agus; Supriatna, Asep K.

2015-12-01

World population growth has many impacts on human live activities and other related aspects. One among the aspects is the increase of the use of energy to support human daily activities, covering industrial aspect, transportation, domestic activities, etc. It is plausible that the higher the population size in a country the higher the needs for energy to support all aspects of human activities in the country. Considering the depletion of petroleum and other fossil-based energy, recently there is a tendency to use geothermal as other source of energy. In this paper we will discuss the prediction of the world consumption of geothermal energy by two different methods, i.e. via the time series of the geothermal usage and via the time series of the geothermal usage combined with the prediction of the world total population. For the first case, we use the simple exponential smoothing method while for the second case we use the simple regression method. The result shows that taking into account the prediction of the world population size giving a better prediction to forecast a short term of the geothermal energy consumption.

3. Time series with tailored nonlinearities

Science.gov (United States)

Räth, C.; Laut, I.

2015-10-01

It is demonstrated how to generate time series with tailored nonlinearities by inducing well-defined constraints on the Fourier phases. Correlations between the phase information of adjacent phases and (static and dynamic) measures of nonlinearities are established and their origin is explained. By applying a set of simple constraints on the phases of an originally linear and uncorrelated Gaussian time series, the observed scaling behavior of the intensity distribution of empirical time series can be reproduced. The power law character of the intensity distributions being typical for, e.g., turbulence and financial data can thus be explained in terms of phase correlations.

4. Do on/off time series models reproduce emerging stock market comovements?

OpenAIRE

Mohamed el hédi Arouri; Fredj Jawadi

2011-01-01

Using nonlinear modeling tools, this study investigates the comovements between the Mexican and the world stock markets over the last three decades. While the previous works only highlight some evidence of comovements, our paper aims to specify the different time-varying links and mechanisms characterizing the Mexican stock market through the comparison of two nonlinear error correction models (NECMs). Our findings point out strong evidence of time-varying and nonlinear mean-reversion and lin...

5. International Work-Conference on Time Series

CERN Document Server

Pomares, Héctor

2016-01-01

This volume presents selected peer-reviewed contributions from The International Work-Conference on Time Series, ITISE 2015, held in Granada, Spain, July 1-3, 2015. It discusses topics in time series analysis and forecasting, advanced methods and online learning in time series, high-dimensional and complex/big data time series as well as forecasting in real problems. The International Work-Conferences on Time Series (ITISE) provide a forum for scientists, engineers, educators and students to discuss the latest ideas and implementations in the foundations, theory, models and applications in the field of time series analysis and forecasting. It focuses on interdisciplinary and multidisciplinary research encompassing the disciplines of computer science, mathematics, statistics and econometrics.

6. Optimal Subset Selection of Time-Series MODIS Images and Sample Data Transfer with Random Forests for Supervised Classification Modelling.

Science.gov (United States)

Zhou, Fuqun; Zhang, Aining

2016-10-25

Nowadays, various time-series Earth Observation data with multiple bands are freely available, such as Moderate Resolution Imaging Spectroradiometer (MODIS) datasets including 8-day composites from NASA, and 10-day composites from the Canada Centre for Remote Sensing (CCRS). It is challenging to efficiently use these time-series MODIS datasets for long-term environmental monitoring due to their vast volume and information redundancy. This challenge will be greater when Sentinel 2-3 data become available. Another challenge that researchers face is the lack of in-situ data for supervised modelling, especially for time-series data analysis. In this study, we attempt to tackle the two important issues with a case study of land cover mapping using CCRS 10-day MODIS composites with the help of Random Forests' features: variable importance, outlier identification. The variable importance feature is used to analyze and select optimal subsets of time-series MODIS imagery for efficient land cover mapping, and the outlier identification feature is utilized for transferring sample data available from one year to an adjacent year for supervised classification modelling. The results of the case study of agricultural land cover classification at a regional scale show that using only about a half of the variables we can achieve land cover classification accuracy close to that generated using the full dataset. The proposed simple but effective solution of sample transferring could make supervised modelling possible for applications lacking sample data.

7. Multimodality Prediction of Chaotic Time Series with Sparse Hard-Cut EM Learning of the Gaussian Process Mixture Model

Science.gov (United States)

Zhou, Ya-Tong; Fan, Yu; Chen, Zi-Yi; Sun, Jian-Cheng

2017-05-01

The contribution of this work is twofold: (1) a multimodality prediction method of chaotic time series with the Gaussian process mixture (GPM) model is proposed, which employs a divide and conquer strategy. It automatically divides the chaotic time series into multiple modalities with different extrinsic patterns and intrinsic characteristics, and thus can more precisely fit the chaotic time series. (2) An effective sparse hard-cut expectation maximization (SHC-EM) learning algorithm for the GPM model is proposed to improve the prediction performance. SHC-EM replaces a large learning sample set with fewer pseudo inputs, accelerating model learning based on these pseudo inputs. Experiments on Lorenz and Chua time series demonstrate that the proposed method yields not only accurate multimodality prediction, but also the prediction confidence interval. SHC-EM outperforms the traditional variational learning in terms of both prediction accuracy and speed. In addition, SHC-EM is more robust and insusceptible to noise than variational learning. Supported by the National Natural Science Foundation of China under Grant No 60972106, the China Postdoctoral Science Foundation under Grant No 2014M561053, the Humanity and Social Science Foundation of Ministry of Education of China under Grant No 15YJA630108, and the Hebei Province Natural Science Foundation under Grant No E2016202341.

8. Time series analysis time series analysis methods and applications

CERN Document Server

Rao, Tata Subba; Rao, C R

2012-01-01

The field of statistics not only affects all areas of scientific activity, but also many other matters such as public policy. It is branching rapidly into so many different subjects that a series of handbooks is the only way of comprehensively presenting the various aspects of statistical methodology, applications, and recent developments. The Handbook of Statistics is a series of self-contained reference books. Each volume is devoted to a particular topic in statistics, with Volume 30 dealing with time series. The series is addressed to the entire community of statisticians and scientists in various disciplines who use statistical methodology in their work. At the same time, special emphasis is placed on applications-oriented techniques, with the applied statistician in mind as the primary audience. Comprehensively presents the various aspects of statistical methodology Discusses a wide variety of diverse applications and recent developments Contributors are internationally renowened experts in their respect...

9. Boosting Nonlinear Additive Autoregressive Time Series

OpenAIRE

Shafik, Nivien; Tutz, Gerhard

2007-01-01

Within the last years several methods for the analysis of nonlinear autoregressive time series have been proposed. As in linear autoregressive models main problems are model identification, estimation and prediction. A boosting method is proposed that performs model identification and estimation simultaneously within the framework of nonlinear autoregressive time series. The method allows to select influential terms from a large numbers of potential lags and exogenous variables. The influence...

10. Nonlinear Time Series and Neural-Network Models of Exchange Rates between the US Dollar and Major Currencies

Directory of Open Access Journals (Sweden)

David E. Allen

2016-03-01

Full Text Available This paper features an analysis of major currency exchange rate movements in relation to the US dollar, as constituted in US dollar terms. Euro, British pound, Chinese yuan, and Japanese yen are modelled using a variety of non-linear models, including smooth transition regression models, logistic smooth transition regressions models, threshold autoregressive models, nonlinear autoregressive models, and additive nonlinear autoregressive models, plus Neural Network models. The models are evaluated on the basis of error metrics for twenty day out-of-sample forecasts using the mean average percentage errors (MAPE. The results suggest that there is no dominating class of time series models, and the different currency pairs relationships with the US dollar are captured best by neural net regression models, over the ten year sample of daily exchange rate returns data, from August 2005 to August 2015.

11. A COMPARATIVE STUDY OF FORECASTING MODELS FOR TREND AND SEASONAL TIME SERIES DOES COMPLEX MODEL ALWAYS YIELD BETTER FORECAST THAN SIMPLE MODELS

Directory of Open Access Journals (Sweden)

Suhartono Suhartono

2005-01-01

Full Text Available Many business and economic time series are non-stationary time series that contain trend and seasonal variations. Seasonality is a periodic and recurrent pattern caused by factors such as weather, holidays, or repeating promotions. A stochastic trend is often accompanied with the seasonal variations and can have a significant impact on various forecasting methods. In this paper, we will investigate and compare some forecasting methods for modeling time series with both trend and seasonal patterns. These methods are Winter's, Decomposition, Time Series Regression, ARIMA and Neural Networks models. In this empirical research, we study on the effectiveness of the forecasting performance, particularly to answer whether a complex method always give a better forecast than a simpler method. We use a real data, that is airline passenger data. The result shows that the more complex model does not always yield a better result than a simpler one. Additionally, we also find the possibility to do further research especially the use of hybrid model by combining some forecasting method to get better forecast, for example combination between decomposition (as data preprocessing and neural network model.

12. Underwater Noise Modeling and Direction-Finding Based on Heteroscedastic Time Series

Directory of Open Access Journals (Sweden)

Kamarei Mahmoud

2007-01-01

Full Text Available We propose a new method for practical non-Gaussian and nonstationary underwater noise modeling. This model is very useful for passive sonar in shallow waters. In this application, measurement of additive noise in natural environment and exhibits shows that noise can sometimes be significantly non-Gaussian and a time-varying feature especially in the variance. Therefore, signal processing algorithms such as direction-finding that is optimized for Gaussian noise may degrade significantly in this environment. Generalized autoregressive conditional heteroscedasticity (GARCH models are suitable for heavy tailed PDFs and time-varying variances of stochastic process. We use a more realistic GARCH-based noise model in the maximum-likelihood approach for the estimation of direction-of-arrivals (DOAs of impinging sources onto a linear array, and demonstrate using measured noise that this approach is feasible for the additive noise and direction finding in an underwater environment.

13. Dynamical modelling of measured time series from a Q-switched CO sub 2 laser

CERN Document Server

Horbelt, W; Bünner, M J; Meucci, R; Ciofini, M

2003-01-01

The transient dynamics of a Q-switched CO sub 2 laser is modelled quantitatively on the base of the four level model, a five dimensional nonlinear system of ordinary differential equations. Using the multiple shooting technique, internal parameters of the laser are estimated and the unobserved time courses of the population densities are constructed. For excitations barely above the laser threshold large pulse variations are identified as an effect of small variations of the pump parameter.

14. Downsizer - A Graphical User Interface-Based Application for Browsing, Acquiring, and Formatting Time-Series Data for Hydrologic Modeling

Science.gov (United States)

Ward-Garrison, Christian; Markstrom, Steven L.; Hay, Lauren E.

2009-01-01

The U.S. Geological Survey Downsizer is a computer application that selects, downloads, verifies, and formats station-based time-series data for environmental-resource models, particularly the Precipitation-Runoff Modeling System. Downsizer implements the client-server software architecture. The client presents a map-based, graphical user interface that is intuitive to modelers; the server provides streamflow and climate time-series data from over 40,000 measurement stations across the United States. This report is the Downsizer user's manual and provides (1) an overview of the software design, (2) installation instructions, (3) a description of the graphical user interface, (4) a description of selected output files, and (5) troubleshooting information.

15. Nonlinear detection of disordered voice productions from short time series based on a Volterra-Wiener-Korenberg model

International Nuclear Information System (INIS)

Zhang Yu; Sprecher, Alicia J.; Zhao Zongxi; Jiang, Jack J.

2011-01-01

Highlights: → The VWK method effectively detects the nonlinearity of a discrete map. → The method describes the chaotic time series of a biomechanical vocal fold model. → Nonlinearity in laryngeal pathology is detected from short and noisy time series. - Abstract: In this paper, we apply the Volterra-Wiener-Korenberg (VWK) model method to detect nonlinearity in disordered voice productions. The VWK method effectively describes the nonlinearity of a third-order nonlinear map. It allows for the analysis of short and noisy data sets. The extracted VWK model parameters show an agreement with the original nonlinear map parameters. Furthermore, the VWK mode method is applied to successfully assess the nonlinearity of a biomechanical voice production model simulating irregular vibratory dynamics of vocal folds with a unilateral vocal polyp. Finally, we show the clinical applicability of this nonlinear detection method to analyze the electroglottographic data generated by 14 patients with vocal nodules or polyps. The VWK model method shows potential in describing the nonlinearity inherent in disordered voice productions from short and noisy time series that are common in the clinical setting.

16. A comparative study of time series modeling methods for reactor noise analysis

International Nuclear Information System (INIS)

Kitamura, Masaharu; Shigeno, Kei; Sugiyama, Kazusuke

1978-01-01

Two modeling algorithms were developed to study at-power reactor noise as a multi-input, multi-output process. A class of linear, discrete time description named autoregressive-moving average model was used as a compact mathematical expression of the objective process. One of the model estimation (modeling) algorithms is based on the theory of Kalman filtering, and the other on a conjugate gradient method. By introducing some modifications in the formulation of the problem, realization of the practically usable algorithms was made feasible. Through the testing with several simulation models, reliability and effectiveness of these algorithms were confirmed. By applying these algorithms to experimental data obtained from a nuclear power plant, interesting knowledge about the at-power reactor noise was found out. (author)

17. Ecological modeling from time-series inference: insight into dynamics and stability of intestinal microbiota.

Directory of Open Access Journals (Sweden)

Richard R Stein

Full Text Available The intestinal microbiota is a microbial ecosystem of crucial importance to human health. Understanding how the microbiota confers resistance against enteric pathogens and how antibiotics disrupt that resistance is key to the prevention and cure of intestinal infections. We present a novel method to infer microbial community ecology directly from time-resolved metagenomics. This method extends generalized Lotka-Volterra dynamics to account for external perturbations. Data from recent experiments on antibiotic-mediated Clostridium difficile infection is analyzed to quantify microbial interactions, commensal-pathogen interactions, and the effect of the antibiotic on the community. Stability analysis reveals that the microbiota is intrinsically stable, explaining how antibiotic perturbations and C. difficile inoculation can produce catastrophic shifts that persist even after removal of the perturbations. Importantly, the analysis suggests a subnetwork of bacterial groups implicated in protection against C. difficile. Due to its generality, our method can be applied to any high-resolution ecological time-series data to infer community structure and response to external stimuli.

18. Forecasting ocean wave energy: A Comparison of the ECMWF wave model with time series methods

DEFF Research Database (Denmark)

Reikard, Gordon; Pinson, Pierre; Bidlot, Jean

2011-01-01

(ECMWF) Wave Model, and two statistical techniques, time-varying parameter regressions and neural networks. Thirteen data sets at locations in the Atlantic and Pacific Oceans and the Gulf of Mexico are tested. The quantities to be predicted are the significant wave height, the wave period, and the wave...

19. Time series analysis of malaria in Afghanistan: using ARIMA models to predict future trends in incidence.

Science.gov (United States)

Anwar, Mohammad Y; Lewnard, Joseph A; Parikh, Sunil; Pitzer, Virginia E

2016-11-22

Malaria remains endemic in Afghanistan. National control and prevention strategies would be greatly enhanced through a better ability to forecast future trends in disease incidence. It is, therefore, of interest to develop a predictive tool for malaria patterns based on the current passive and affordable surveillance system in this resource-limited region. This study employs data from Ministry of Public Health monthly reports from January 2005 to September 2015. Malaria incidence in Afghanistan was forecasted using autoregressive integrated moving average (ARIMA) models in order to build a predictive tool for malaria surveillance. Environmental and climate data were incorporated to assess whether they improve predictive power of models. Two models were identified, each appropriate for different time horizons. For near-term forecasts, malaria incidence can be predicted based on the number of cases in the four previous months and 12 months prior (Model 1); for longer-term prediction, malaria incidence can be predicted using the rates 1 and 12 months prior (Model 2). Next, climate and environmental variables were incorporated to assess whether the predictive power of proposed models could be improved. Enhanced vegetation index was found to have increased the predictive accuracy of longer-term forecasts. Results indicate ARIMA models can be applied to forecast malaria patterns in Afghanistan, complementing current surveillance systems. The models provide a means to better understand malaria dynamics in a resource-limited context with minimal data input, yielding forecasts that can be used for public health planning at the national level.

20. Forecasting of particulate matter time series using wavelet analysis and wavelet-ARMA/ARIMA model in Taiyuan, China.

Science.gov (United States)

Zhang, Hong; Zhang, Sheng; Wang, Ping; Qin, Yuzhe; Wang, Huifeng

2017-07-01

Particulate matter with aerodynamic diameter below 10 μm (PM 10 ) forecasting is difficult because of the uncertainties in describing the emission and meteorological fields. This paper proposed a wavelet-ARMA/ARIMA model to forecast the short-term series of the PM 10 concentrations. It was evaluated by experiments using a 10-year data set of daily PM 10 concentrations from 4 stations located in Taiyuan, China. The results indicated the following: (1) PM 10 concentrations of Taiyuan had a decreasing trend during 2005 to 2012 but increased in 2013. PM 10 concentrations had an obvious seasonal fluctuation related to coal-fired heating in winter and early spring. (2) Spatial differences among the four stations showed that the PM 10 concentrations in industrial and heavily trafficked areas were higher than those in residential and suburb areas. (3) Wavelet analysis revealed that the trend variation and the changes of the PM 10 concentration of Taiyuan were complicated. (4) The proposed wavelet-ARIMA model could be efficiently and successfully applied to the PM 10 forecasting field. Compared with the traditional ARMA/ARIMA methods, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multiple-time-scale prediction. Wavelet analysis can filter noisy signals and identify the variation trend and the fluctuation of the PM 10 time-series data. Wavelet decomposition and reconstruction reduce the nonstationarity of the PM 10 time-series data, and thus improve the accuracy of the prediction. This paper proposed a wavelet-ARMA/ARIMA model to forecast the PM 10 time series. Compared with the traditional ARMA/ARIMA method, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multiple-time-scale prediction. The proposed model could be efficiently and successfully applied to the PM 10 forecasting field.

1. Comparison of extended mean-reversion and time series models for electricity spot price simulation considering negative prices

International Nuclear Information System (INIS)

Keles, Dogan; Genoese, Massimo; Möst, Dominik; Fichtner, Wolf

2012-01-01

This paper evaluates different financial price and time series models, such as mean reversion, autoregressive moving average (ARMA), integrated ARMA (ARIMA) and general autoregressive conditional heteroscedasticity (GARCH) process, usually applied for electricity price simulations. However, as these models are developed to describe the stochastic behaviour of electricity prices, they are extended by a separate data treatment for the deterministic components (trend, daily, weekly and annual cycles) of electricity spot prices. Furthermore price jumps are considered and implemented within a regime-switching model. Since 2008 market design allows for negative prices at the European Energy Exchange, which also occurred for several hours in the last years. Up to now, only a few financial and time series approaches exist, which are able to capture negative prices. This paper presents a new approach incorporating negative prices. The evaluation of the different approaches presented points out that the mean reversion and the ARMA models deliver the lowest mean root square error between simulated and historical electricity spot prices gained from the European Energy Exchange. These models posses also lower mean average errors than GARCH models. Hence, they are more suitable to simulate well-fitting price paths. Furthermore it is shown that the daily structure of historical price curves is better captured applying ARMA or ARIMA processes instead of mean-reversion or GARCH models. Another important outcome of the paper is that the regime-switching approach and the consideration of negative prices via the new proposed approach lead to a significant improvement of the electricity price simulation. - Highlights: ► Considering negative prices improves the results of time-series and financial models for electricity prices. ► Regime-switching approach captures the jumps and base prices quite well. ► Removing and separate modelling of deterministic annual, weekly and daily

2. A Data-Driven Modeling Strategy for Smart Grid Power Quality Coupling Assessment Based on Time Series Pattern Matching

Directory of Open Access Journals (Sweden)

Hao Yu

2018-01-01

Full Text Available This study introduces a data-driven modeling strategy for smart grid power quality (PQ coupling assessment based on time series pattern matching to quantify the influence of single and integrated disturbance among nodes in different pollution patterns. Periodic and random PQ patterns are constructed by using multidimensional frequency-domain decomposition for all disturbances. A multidimensional piecewise linear representation based on local extreme points is proposed to extract the patterns features of single and integrated disturbance in consideration of disturbance variation trend and severity. A feature distance of pattern (FDP is developed to implement pattern matching on univariate PQ time series (UPQTS and multivariate PQ time series (MPQTS to quantify the influence of single and integrated disturbance among nodes in the pollution patterns. Case studies on a 14-bus distribution system are performed and analyzed; the accuracy and applicability of the FDP in the smart grid PQ coupling assessment are verified by comparing with other time series pattern matching methods.

3. Prostate cancer detection from model-free T1-weighted time series and diffusion imaging

Science.gov (United States)

Haq, Nandinee F.; Kozlowski, Piotr; Jones, Edward C.; Chang, Silvia D.; Goldenberg, S. Larry; Moradi, Mehdi

2015-03-01

The combination of Dynamic Contrast Enhanced (DCE) images with diffusion MRI has shown great potential in prostate cancer detection. The parameterization of DCE images to generate cancer markers is traditionally performed based on pharmacokinetic modeling. However, pharmacokinetic models make simplistic assumptions about the tissue perfusion process, require the knowledge of contrast agent concentration in a major artery, and the modeling process is sensitive to noise and fitting instabilities. We address this issue by extracting features directly from the DCE T1-weighted time course without modeling. In this work, we employed a set of data-driven features generated by mapping the DCE T1 time course to its principal component space, along with diffusion MRI features to detect prostate cancer. The optimal set of DCE features is extracted with sparse regularized regression through a Least Absolute Shrinkage and Selection Operator (LASSO) model. We show that when our proposed features are used within the multiparametric MRI protocol to replace the pharmacokinetic parameters, the area under ROC curve is 0.91 for peripheral zone classification and 0.87 for whole gland classification. We were able to correctly classify 32 out of 35 peripheral tumor areas identified in the data when the proposed features were used with support vector machine classification. The proposed feature set was used to generate cancer likelihood maps for the prostate gland.

4. Benchmarking of energy time series

Energy Technology Data Exchange (ETDEWEB)

Williamson, M.A.

1990-04-01

Benchmarking consists of the adjustment of time series data from one source in order to achieve agreement with similar data from a second source. The data from the latter source are referred to as the benchmark(s), and often differ in that they are observed at a lower frequency, represent a higher level of temporal aggregation, and/or are considered to be of greater accuracy. This report provides an extensive survey of benchmarking procedures which have appeared in the statistical literature, and reviews specific benchmarking procedures currently used by the Energy Information Administration (EIA). The literature survey includes a technical summary of the major benchmarking methods and their statistical properties. Factors influencing the choice and application of particular techniques are described and the impact of benchmark accuracy is discussed. EIA applications and procedures are reviewed and evaluated for residential natural gas deliveries series and coal production series. It is found that the current method of adjusting the natural gas series is consistent with the behavior of the series and the methods used in obtaining the initial data. As a result, no change is recommended. For the coal production series, a staged approach based on a first differencing technique is recommended over the current procedure. A comparison of the adjustments produced by the two methods is made for the 1987 Indiana coal production series. 32 refs., 5 figs., 1 tab.

5. Three-factor models versus time series models: quantifying time-dependencies of interactions between stimuli in cell biology and psychobiology for short longitudinal data.

Science.gov (United States)

Frank, Till D; Kiyatkin, Anatoly; Cheong, Alex; Kholodenko, Boris N

2017-06-01

Signal integration determines cell fate on the cellular level, affects cognitive processes and affective responses on the behavioural level, and is likely to be involved in psychoneurobiological processes underlying mood disorders. Interactions between stimuli may subjected to time effects. Time-dependencies of interactions between stimuli typically lead to complex cell responses and complex responses on the behavioural level. We show that both three-factor models and time series models can be used to uncover such time-dependencies. However, we argue that for short longitudinal data the three factor modelling approach is more suitable. In order to illustrate both approaches, we re-analysed previously published short longitudinal data sets. We found that in human embryonic kidney 293 cells cells the interaction effect in the regulation of extracellular signal-regulated kinase (ERK) 1 signalling activation by insulin and epidermal growth factor is subjected to a time effect and dramatically decays at peak values of ERK activation. In contrast, we found that the interaction effect induced by hypoxia and tumour necrosis factor-alpha for the transcriptional activity of the human cyclo-oxygenase-2 promoter in HEK293 cells is time invariant at least in the first 12-h time window after stimulation. Furthermore, we applied the three-factor model to previously reported animal studies. In these studies, memory storage was found to be subjected to an interaction effect of the beta-adrenoceptor agonist clenbuterol and certain antagonists acting on the alpha-1-adrenoceptor / glucocorticoid-receptor system. Our model-based analysis suggests that only if the antagonist drug is administer in a critical time window, then the interaction effect is relevant. © The authors 2016. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.

6. Simulating multivariate time series using flocking

OpenAIRE

Schruben, Lee W.; Singham, Dashi I.

2010-01-01

Refereed Conference Paper Notions from agent based modeling (ABM) can be used to simulate multivariate time series. An example is given using the ABM concept of flocking, which models the behaviors of birds (called boids) in a flock. A multivariate time series is mapped into the coordinates of a bounded orthotope. This represents the flight path of a boid. Other boids are generated that flock around this data boid. The coordinates of these new boids are mapped back to simulate replicates o...

7. Model design to predict forest fire risk in Navarra (Spain) using time series analysis

OpenAIRE

Huesca, M.; Litago, J.; Palacios-Orueta, A.; Merino de Miguel, Silvia

2008-01-01

Understand and predict how forest fire potential changes over time are essential for prioritizing forest management activities and reducing damage. Nowadays we lack the capacity to predict future forest fire trends in response to climate change. The main goal of this research is to build an empirical model to describe, estimate and forecast the forest fires dynamics using the improved Fire Potential Index (FPI) (Huesca et al., 2007) as indicator of fire.

8. Assessing the Relationship Between Meteorological Parameters, Air Pollution And Cardiovascular Mortality of Mashhad City Based on Time Series Model

OpenAIRE

2017-01-01

Epidemiological studies conducted in the past two decades indicate that air pollution causes increase in cardiovascular, breathing and chronic bronchitis disorders and even causes cardiovascular mortality. Therefore, the aim of this study was to investigate the relationship between meteorological parameters, air pollution and cardiovascular mortality in the city of Mashhad in 2014 by a time series model. Data on mortality from cardiovascular disease, meteorological parameters and air pollutio...

9. Multiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia.

Science.gov (United States)

Ng, Kar Yong; Awang, Norhashidah

2018-01-06

Frequent haze occurrences in Malaysia have made the management of PM 10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM 10 variation and good forecast of PM 10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM 10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants. Three different models were built. They were multiple linear regression (MLR) model with lagged predictor variables (MLR1), MLR model with lagged predictor variables and PM 10 concentrations (MLR2) and regression with time series error (RTSE) model. The findings revealed that humidity, temperature, wind speed, wind direction, carbon monoxide and ozone were the main factors explaining the PM 10 variation in Peninsular Malaysia. Comparison among the three models showed that MLR2 model was on a same level with RTSE model in terms of forecasting accuracy, while MLR1 model was the worst.

10. Finite-element time-domain modeling of electromagnetic data in general dispersive medium using adaptive Padé series

Science.gov (United States)

Cai, Hongzhu; Hu, Xiangyun; Xiong, Bin; Zhdanov, Michael S.

2017-12-01

The induced polarization (IP) method has been widely used in geophysical exploration to identify the chargeable targets such as mineral deposits. The inversion of the IP data requires modeling the IP response of 3D dispersive conductive structures. We have developed an edge-based finite-element time-domain (FETD) modeling method to simulate the electromagnetic (EM) fields in 3D dispersive medium. We solve the vector Helmholtz equation for total electric field using the edge-based finite-element method with an unstructured tetrahedral mesh. We adopt the backward propagation Euler method, which is unconditionally stable, with semi-adaptive time stepping for the time domain discretization. We use the direct solver based on a sparse LU decomposition to solve the system of equations. We consider the Cole-Cole model in order to take into account the frequency-dependent conductivity dispersion. The Cole-Cole conductivity model in frequency domain is expanded using a truncated Padé series with adaptive selection of the center frequency of the series for early and late time. This approach can significantly increase the accuracy of FETD modeling.

11. Developing a dengue early warning system using time series model: Case study in Tainan, Taiwan

Science.gov (United States)

Chen, Xiao-Wei; Jan, Chyan-Deng; Wang, Ji-Shang

2017-04-01

Dengue fever (DF) is a climate-sensitive disease that has been emerging in southern regions of Taiwan over the past few decades, causing a significant health burden to affected areas. This study aims to propose a predictive model to implement an early warning system so as to enhance dengue surveillance and control in Tainan, Taiwan. The Seasonal Autoregressive Integrated Moving Average (SARIMA) model was used herein to forecast dengue cases. Temporal correlation between dengue incidences and climate variables were examined by Pearson correlation analysis and Cross-correlation tests in order to identify key determinants to be included as predictors. The dengue surveillance data between 2000 and 2009, as well as their respective climate variables were then used as inputs for the model. We validated the model by forecasting the number of dengue cases expected to occur each week between January 1, 2010 and December 31, 2015. In addition, we analyzed historical dengue trends and found that 25 cases occurring in one week was a trigger point that often led to a dengue outbreak. This threshold point was combined with the season-based framework put forth by the World Health Organization to create a more accurate epidemic threshold for a Tainan-specific warning system. A Seasonal ARIMA model with the general form: (1,0,5)(1,1,1)52 is identified as the most appropriate model based on lowest AIC, and was proven significant in the prediction of observed dengue cases. Based on the correlation coefficient, Lag-11 maximum 1-hr rainfall (r=0.319, Pdengue surveillance and control in Tainan, Taiwan. We conclude that this timely dengue early warning system will enable public health services to allocate limited resources more effectively, and public health officials to adjust dengue emergency response plans to their maximum capabilities.

12. How to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modeling.

Science.gov (United States)

Onisko, Agnieszka; Druzdzel, Marek J; Austin, R Marshall

2016-01-01

Classical statistics is a well-established approach in the analysis of medical data. While the medical community seems to be familiar with the concept of a statistical analysis and its interpretation, the Bayesian approach, argued by many of its proponents to be superior to the classical frequentist approach, is still not well-recognized in the analysis of medical data. The goal of this study is to encourage data analysts to use the Bayesian approach, such as modeling with graphical probabilistic networks, as an insightful alternative to classical statistical analysis of medical data. This paper offers a comparison of two approaches to analysis of medical time series data: (1) classical statistical approach, such as the Kaplan-Meier estimator and the Cox proportional hazards regression model, and (2) dynamic Bayesian network modeling. Our comparison is based on time series cervical cancer screening data collected at Magee-Womens Hospital, University of Pittsburgh Medical Center over 10 years. The main outcomes of our comparison are cervical cancer risk assessments produced by the three approaches. However, our analysis discusses also several aspects of the comparison, such as modeling assumptions, model building, dealing with incomplete data, individualized risk assessment, results interpretation, and model validation. Our study shows that the Bayesian approach is (1) much more flexible in terms of modeling effort, and (2) it offers an individualized risk assessment, which is more cumbersome for classical statistical approaches.

13. A Procedure for Identification of Appropriate State Space and ARIMA Models Based on Time-Series Cross-Validation

Directory of Open Access Journals (Sweden)

Patrícia Ramos

2016-11-01

Full Text Available In this work, a cross-validation procedure is used to identify an appropriate Autoregressive Integrated Moving Average model and an appropriate state space model for a time series. A minimum size for the training set is specified. The procedure is based on one-step forecasts and uses different training sets, each containing one more observation than the previous one. All possible state space models and all ARIMA models where the orders are allowed to range reasonably are fitted considering raw data and log-transformed data with regular differencing (up to second order differences and, if the time series is seasonal, seasonal differencing (up to first order differences. The value of root mean squared error for each model is calculated averaging the one-step forecasts obtained. The model which has the lowest root mean squared error value and passes the Ljung–Box test using all of the available data with a reasonable significance level is selected among all the ARIMA and state space models considered. The procedure is exemplified in this paper with a case study of retail sales of different categories of women’s footwear from a Portuguese retailer, and its accuracy is compared with three reliable forecasting approaches. The results show that our procedure consistently forecasts more accurately than the other approaches and the improvements in the accuracy are significant.

14. Predicting the Water Level Fluctuation in an Alpine Lake Using Physically Based, Artificial Neural Network, and Time Series Forecasting Models

Directory of Open Access Journals (Sweden)

Chih-Chieh Young

2015-01-01

Full Text Available Accurate prediction of water level fluctuation is important in lake management due to its significant impacts in various aspects. This study utilizes four model approaches to predict water levels in the Yuan-Yang Lake (YYL in Taiwan: a three-dimensional hydrodynamic model, an artificial neural network (ANN model (back propagation neural network, BPNN, a time series forecasting (autoregressive moving average with exogenous inputs, ARMAX model, and a combined hydrodynamic and ANN model. Particularly, the black-box ANN model and physically based hydrodynamic model are coupled to more accurately predict water level fluctuation. Hourly water level data (a total of 7296 observations was collected for model calibration (training and validation. Three statistical indicators (mean absolute error, root mean square error, and coefficient of correlation were adopted to evaluate model performances. Overall, the results demonstrate that the hydrodynamic model can satisfactorily predict hourly water level changes during the calibration stage but not for the validation stage. The ANN and ARMAX models better predict the water level than the hydrodynamic model does. Meanwhile, the results from an ANN model are superior to those by the ARMAX model in both training and validation phases. The novel proposed concept using a three-dimensional hydrodynamic model in conjunction with an ANN model has clearly shown the improved prediction accuracy for the water level fluctuation.

15. Efficient Computation of Multiscale Entropy over Short Biomedical Time Series Based on Linear State-Space Models

Directory of Open Access Journals (Sweden)

Luca Faes

2017-01-01

Full Text Available The most common approach to assess the dynamical complexity of a time series across multiple temporal scales makes use of the multiscale entropy (MSE and refined MSE (RMSE measures. In spite of their popularity, MSE and RMSE lack an analytical framework allowing their calculation for known dynamic processes and cannot be reliably computed over short time series. To overcome these limitations, we propose a method to assess RMSE for autoregressive (AR stochastic processes. The method makes use of linear state-space (SS models to provide the multiscale parametric representation of an AR process observed at different time scales and exploits the SS parameters to quantify analytically the complexity of the process. The resulting linear MSE (LMSE measure is first tested in simulations, both theoretically to relate the multiscale complexity of AR processes to their dynamical properties and over short process realizations to assess its computational reliability in comparison with RMSE. Then, it is applied to the time series of heart period, arterial pressure, and respiration measured for healthy subjects monitored in resting conditions and during physiological stress. This application to short-term cardiovascular variability documents that LMSE can describe better than RMSE the activity of physiological mechanisms producing biological oscillations at different temporal scales.

16. Integrating a Linear Signal Model with Groundwater and Rainfall time-series on the Characteristic Identification of Groundwater Systems

Science.gov (United States)

Chen, Yu-Wen; Wang, Yetmen; Chang, Liang-Cheng

2017-04-01

Groundwater resources play a vital role on regional supply. To avoid irreversible environmental impact such as land subsidence, the characteristic identification of groundwater system is crucial before sustainable management of groundwater resource. This study proposes a signal process approach to identify the character of groundwater systems based on long-time hydrologic observations include groundwater level and rainfall. The study process contains two steps. First, a linear signal model (LSM) is constructed and calibrated to simulate the variation of underground hydrology based on the time series of groundwater levels and rainfall. The mass balance equation of the proposed LSM contains three major terms contain net rate of horizontal exchange, rate of rainfall recharge and rate of pumpage and four parameters are required to calibrate. Because reliable records of pumpage is rare, the time-variant groundwater amplitudes of daily frequency (P ) calculated by STFT are assumed as linear indicators of puamage instead of pumpage records. Time series obtained from 39 observation wells and 50 rainfall stations in and around the study area, Pintung Plain, are paired for model construction. Second, the well-calibrated parameters of the linear signal model can be used to interpret the characteristic of groundwater system. For example, the rainfall recharge coefficient (γ) means the transform ratio between rainfall intention and groundwater level raise. The area around the observation well with higher γ means that the saturated zone here is easily affected by rainfall events and the material of unsaturated zone might be gravel or coarse sand with high infiltration ratio. Considering the spatial distribution of γ, the values of γ decrease from the upstream to the downstream of major rivers and also are correlated to the spatial distribution of grain size of surface soil. Via the time-series of groundwater levels and rainfall, the well-calibrated parameters of LSM have

17. Dimensionality reduction for time series data

OpenAIRE

Vidaurre, Diego; Rezek, Iead; Harrison, Samuel L.; Smith, Stephen S.; Woolrich, Mark

2014-01-01

Despite the fact that they do not consider the temporal nature of data, classic dimensionality reduction techniques, such as PCA, are widely applied to time series data. In this paper, we introduce a factor decomposition specific for time series that builds upon the Bayesian multivariate autoregressive model and hence evades the assumption that data points are mutually independent. The key is to find a low-rank estimation of the autoregressive matrices. As in the probabilistic version of othe...

18. Understanding Coronal Heating through Time-Series Analysis and Nanoflare Modeling

Science.gov (United States)

Romich, Kristine; Viall, Nicholeen

2018-01-01

Periodic intensity fluctuations in coronal loops, a signature of temperature evolution, have been observed using the Atmospheric Imaging Assembly (AIA) aboard NASA’s Solar Dynamics Observatory (SDO) spacecraft. We examine the proposal that nanoflares, or impulsive bursts of energy release in the solar atmosphere, are responsible for the intensity fluctuations as well as the megakelvin-scale temperatures observed in the corona. Drawing on the work of Cargill (2014) and Bradshaw & Viall (2016), we develop a computer model of the energy released by a sequence of nanoflare events in a single magnetic flux tube. We then use EBTEL (Enthalpy-Based Thermal Evolution of Loops), a hydrodynamic model of plasma response to energy input, to simulate intensity as a function of time across the coronal AIA channels. We test the EBTEL output for periodicities using a spectral code based on Mann and Lees’ (1996) multitaper method and present preliminary results here. Our ultimate goal is to establish whether quasi-continuous or impulsive energy bursts better approximate the original SDO data.

19. Estimation of unemployment rates using small area estimation model by combining time series and cross-sectional data

Science.gov (United States)

Muchlisoh, Siti; Kurnia, Anang; Notodiputro, Khairil Anwar; Mangku, I. Wayan

2016-02-01

Labor force surveys conducted over time by the rotating panel design have been carried out in many countries, including Indonesia. Labor force survey in Indonesia is regularly conducted by Statistics Indonesia (Badan Pusat Statistik-BPS) and has been known as the National Labor Force Survey (Sakernas). The main purpose of Sakernas is to obtain information about unemployment rates and its changes over time. Sakernas is a quarterly survey. The quarterly survey is designed only for estimating the parameters at the provincial level. The quarterly unemployment rate published by BPS (official statistics) is calculated based on only cross-sectional methods, despite the fact that the data is collected under rotating panel design. The study purpose to estimate a quarterly unemployment rate at the district level used small area estimation (SAE) model by combining time series and cross-sectional data. The study focused on the application and comparison between the Rao-Yu model and dynamic model in context estimating the unemployment rate based on a rotating panel survey. The goodness of fit of both models was almost similar. Both models produced an almost similar estimation and better than direct estimation, but the dynamic model was more capable than the Rao-Yu model to capture a heterogeneity across area, although it was reduced over time.

20. The forecasting of menstruation based on a state-space modeling of basal body temperature time series.

Science.gov (United States)

Fukaya, Keiichi; Kawamori, Ai; Osada, Yutaka; Kitazawa, Masumi; Ishiguro, Makio

2017-09-20

Women's basal body temperature (BBT) shows a periodic pattern that associates with menstrual cycle. Although this fact suggests a possibility that daily BBT time series can be useful for estimating the underlying phase state as well as for predicting the length of current menstrual cycle, little attention has been paid to model BBT time series. In this study, we propose a state-space model that involves the menstrual phase as a latent state variable to explain the daily fluctuation of BBT and the menstruation cycle length. Conditional distributions of the phase are obtained by using sequential Bayesian filtering techniques. A predictive distribution of the next menstruation day can be derived based on this conditional distribution and the model, leading to a novel statistical framework that provides a sequentially updated prediction for upcoming menstruation day. We applied this framework to a real data set of women's BBT and menstruation days and compared prediction accuracy of the proposed method with that of previous methods, showing that the proposed method generally provides a better prediction. Because BBT can be obtained with relatively small cost and effort, the proposed method can be useful for women's health management. Potential extensions of this framework as the basis of modeling and predicting events that are associated with the menstrual cycles are discussed. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

1. ARIMA-Based Time Series Model of Stochastic Wind Power Generation

DEFF Research Database (Denmark)

Chen, Peiyuan; Pedersen, Troels; Bak-Jensen, Birgitte

2010-01-01

This paper proposes a stochastic wind power model based on an autoregressive integrated moving average (ARIMA) process. The model takes into account the nonstationarity and physical limits of stochastic wind power generation. The model is constructed based on wind power measurement of one year from...... the Nysted offshore wind farm in Denmark. The proposed limited-ARIMA (LARIMA) model introduces a limiter and characterizes the stochastic wind power generation by mean level, temporal correlation and driving noise. The model is validated against the measurement in terms of temporal correlation...... and probability distribution. The LARIMA model outperforms a first-order transition matrix based discrete Markov model in terms of temporal correlation, probability distribution and model parameter number. The proposed LARIMA model is further extended to include the monthly variation of the stochastic wind power...

2. Preliminary Study of Soil Available Nutrient Simulation Using a Modified WOFOST Model and Time-Series Remote Sensing Observations

Directory of Open Access Journals (Sweden)

Zhiqiang Cheng

2018-01-01

Full Text Available The approach of using multispectral remote sensing (RS to estimate soil available nutrients (SANs has been recently developed and shows promising results. This method overcomes the limitations of commonly used methods by building a statistical model that connects RS-based crop growth and nutrient content. However, the stability and accuracy of this model require improvement. In this article, we replaced the statistical model by integrating the World Food Studies (WOFOST model and time series of remote sensing (T-RS observations to ensure stability and accuracy. Time series of HJ-1 A/B data was assimilated into the WOFOST model to extrapolate crop growth simulations from a single point to a large area using a specific assimilation method. Because nutrient-limited growth within the growing season is required and the SAN parameters can only be used at the end of the growing season in the original model, the WOFOST model was modified. Notably, the calculation order was changed, and new soil nutrient uptake algorithms were implemented in the model for nutrient-limited growth estimation. Finally, experiments were conducted in the spring maize plots of Hongxing Farm to analyze the effects of nutrient stress on crop growth and the SAN simulation accuracy. The results confirm the differences in crop growth status caused by a lack of soil nutrients. The new approach can take advantage of these differences to provide better SAN estimates. In general, the new approach can overcome the limitations of existing methods and simulate the SAN status with reliable accuracy.

3. Gap-filling of dry weather flow rate and water quality measurements in urban catchments by a time series modelling approach

DEFF Research Database (Denmark)

Sandoval, Santiago; Vezzaro, Luca; Bertrand-Krajewski, Jean-Luc

2016-01-01

solids (TSS) online measurements (year 2007, 2 minutes time-step, combined system, Ecully, Lyon, France). Results show up the potential of the method to fill gaps longer than 0.5 days, especially between 0.5 days and 1 day (mean NSE > 0.6) in the flow rate time series. TSS results still perform very...... seeks to evaluate the potential of the Singular Spectrum Analysis (SSA), a time-series modelling/gap-filling method, to complete dry weather time series. The SSA method is tested by reconstructing 1000 artificial discontinuous time series, randomly generated from real flow rate and total suspended...

4. Time averaging, ageing and delay analysis of financial time series

Science.gov (United States)

Cherstvy, Andrey G.; Vinod, Deepak; Aghion, Erez; Chechkin, Aleksei V.; Metzler, Ralf

2017-06-01

We introduce three strategies for the analysis of financial time series based on time averaged observables. These comprise the time averaged mean squared displacement (MSD) as well as the ageing and delay time methods for varying fractions of the financial time series. We explore these concepts via statistical analysis of historic time series for several Dow Jones Industrial indices for the period from the 1960s to 2015. Remarkably, we discover a simple universal law for the delay time averaged MSD. The observed features of the financial time series dynamics agree well with our analytical results for the time averaged measurables for geometric Brownian motion, underlying the famed Black-Scholes-Merton model. The concepts we promote here are shown to be useful for financial data analysis and enable one to unveil new universal features of stock market dynamics.

5. The Biasing Effects of Unmodeled ARMA Time Series Processes on Latent Growth Curve Model Estimates

Science.gov (United States)

Sivo, Stephen; Fan, Xitao; Witta, Lea

2005-01-01

The purpose of this study was to evaluate the robustness of estimated growth curve models when there is stationary autocorrelation among manifest variable errors. The results suggest that when, in practice, growth curve models are fitted to longitudinal data, alternative rival hypotheses to consider would include growth models that also specify…

6. Initial and final estimates of the Bilinear seasonal time series model ...

African Journals Online (AJOL)

A particular class of non-linear models which has been found to be useful in many fields is the bilinear models. A special class of it is discussed in this paper. In getting the estimates of the parameters of this model special attention was paid to the problem of having good initial estimates as it is proposed that with good initial ...

7. A novel model for Time-Series Data Clustering Based on piecewise SVD and BIRCH for Stock Data Analysis on Hadoop Platform

Directory of Open Access Journals (Sweden)

Ibgtc Bowala

2017-06-01

Full Text Available With the rapid growth of financial markets, analyzers are paying more attention on predictions. Stock data are time series data, with huge amounts. Feasible solution for handling the increasing amount of data is to use a cluster for parallel processing, and Hadoop parallel computing platform is a typical representative. There are various statistical models for forecasting time series data, but accurate clusters are a pre-requirement. Clustering analysis for time series data is one of the main methods for mining time series data for many other analysis processes. However, general clustering algorithms cannot perform clustering for time series data because series data has a special structure and a high dimensionality has highly co-related values due to high noise level. A novel model for time series clustering is presented using BIRCH, based on piecewise SVD, leading to a novel dimension reduction approach. Highly co-related features are handled using SVD with a novel approach for dimensionality reduction in order to keep co-related behavior optimal and then use BIRCH for clustering. The algorithm is a novel model that can handle massive time series data. Finally, this new model is successfully applied to real stock time series data of Yahoo finance with satisfactory results.

8. A Tutorial on Nonlinear Time-Series Data Mining in Engineering Asset Health and Reliability Prediction: Concepts, Models, and Algorithms

Directory of Open Access Journals (Sweden)

Ming Dong

2010-01-01

Full Text Available The primary objective of engineering asset management is to optimize assets service delivery potential and to minimize the related risks and costs over their entire life through the development and application of asset health and usage management in which the health and reliability prediction plays an important role. In real-life situations where an engineering asset operates under dynamic operational and environmental conditions, the lifetime of an engineering asset is generally described as monitored nonlinear time-series data and subject to high levels of uncertainty and unpredictability. It has been proved that application of data mining techniques is very useful for extracting relevant features which can be used as parameters for assets diagnosis and prognosis. In this paper, a tutorial on nonlinear time-series data mining in engineering asset health and reliability prediction is given. Besides that an overview on health and reliability prediction techniques for engineering assets is covered, this tutorial will focus on concepts, models, algorithms, and applications of hidden Markov models (HMMs and hidden semi-Markov models (HSMMs in engineering asset health prognosis, which are representatives of recent engineering asset health prediction techniques.

9. A Study of Mexican Free-Tailed Bat Chirp Syllables: Bayesian Functional Mixed Models for Nonstationary Acoustic Time Series

KAUST Repository

Martinez, Josue G.

2013-06-01

We describe a new approach to analyze chirp syllables of free-tailed bats from two regions of Texas in which they are predominant: Austin and College Station. Our goal is to characterize any systematic regional differences in the mating chirps and assess whether individual bats have signature chirps. The data are analyzed by modeling spectrograms of the chirps as responses in a Bayesian functional mixed model. Given the variable chirp lengths, we compute the spectrograms on a relative time scale interpretable as the relative chirp position, using a variable window overlap based on chirp length. We use 2D wavelet transforms to capture correlation within the spectrogram in our modeling and obtain adaptive regularization of the estimates and inference for the regions-specific spectrograms. Our model includes random effect spectrograms at the bat level to account for correlation among chirps from the same bat, and to assess relative variability in chirp spectrograms within and between bats. The modeling of spectrograms using functional mixed models is a general approach for the analysis of replicated nonstationary time series, such as our acoustical signals, to relate aspects of the signals to various predictors, while accounting for between-signal structure. This can be done on raw spectrograms when all signals are of the same length, and can be done using spectrograms defined on a relative time scale for signals of variable length in settings where the idea of defining correspondence across signals based on relative position is sensible.

10. Modeling and Forecasting of Water Demand in Isfahan Using Underlying Trend Concept and Time Series

OpenAIRE

2016-01-01

Introduction: Accurate water demand modeling for the city is very important for forecasting and policies adoption related to water resources management. Thus, for future requirements of water estimation, forecasting and modeling, it is important to utilize models with little errors. Water has a special place among the basic human needs, because it not hampers human life. The importance of the issue of water management in the extraction and consumption, it is necessary as a basic need. Municip...

11. Forecasting Marine Corps Enlisted Manpower Inventory Levels With Univariate Time Series Models

National Research Council Canada - National Science Library

Feiring, Douglas I

2006-01-01

.... Models are developed for 44 representative population groups using Holt-Winters exponential smoothing, multiplicative decomposition, and Box-Jenkins autoregressive integrated moving average (ARIMA...

12. Application of time series analysis in modelling and forecasting emergency department visits in a medical centre in Southern Taiwan.

Science.gov (United States)

Juang, Wang-Chuan; Huang, Sin-Jhih; Huang, Fong-Dee; Cheng, Pei-Wen; Wann, Shue-Ren

2017-12-01

Emergency department (ED) overcrowding is acknowledged as an increasingly important issue worldwide. Hospital managers are increasingly paying attention to ED crowding in order to provide higher quality medical services to patients. One of the crucial elements for a good management strategy is demand forecasting. Our study sought to construct an adequate model and to forecast monthly ED visits. We retrospectively gathered monthly ED visits from January 2009 to December 2016 to carry out a time series autoregressive integrated moving average (ARIMA) analysis. Initial development of the model was based on past ED visits from 2009 to 2016. A best-fit model was further employed to forecast the monthly data of ED visits for the next year (2016). Finally, we evaluated the predicted accuracy of the identified model with the mean absolute percentage error (MAPE). The software packages SAS/ETS V.9.4 and Office Excel 2016 were used for all statistical analyses. A series of statistical tests showed that six models, including ARIMA (0, 0, 1), ARIMA (1, 0, 0), ARIMA (1, 0, 1), ARIMA (2, 0, 1), ARIMA (3, 0, 1) and ARIMA (5, 0, 1), were candidate models. The model that gave the minimum Akaike information criterion and Schwartz Bayesian criterion and followed the assumptions of residual independence was selected as the adequate model. Finally, a suitable ARIMA (0, 0, 1) structure, yielding a MAPE of 8.91%, was identified and obtained as Visit t =7111.161+(a t +0.37462 a t -1). The ARIMA (0, 0, 1) model can be considered adequate for predicting future ED visits, and its forecast results can be used to aid decision-making processes. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

13. A Spectral Unmixing Model for the Integration of Multi-Sensor Imagery: A Tool to Generate Consistent Time Series Data

Directory of Open Access Journals (Sweden)

Georgia Doxani

2015-10-01

Full Text Available The Sentinel missions have been designed to support the operational services of the Copernicus program, ensuring long-term availability of data for a wide range of spectral, spatial and temporal resolutions. In particular, Sentinel-2 (S-2 data with improved high spatial resolution and higher revisit frequency (five days with the pair of satellites in operation will play a fundamental role in recording land cover types and monitoring land cover changes at regular intervals. Nevertheless, cloud coverage usually hinders the time series availability and consequently the continuous land surface monitoring. In an attempt to alleviate this limitation, the synergistic use of instruments with different features is investigated, aiming at the future synergy of the S-2 MultiSpectral Instrument (MSI and Sentinel-3 (S-3 Ocean and Land Colour Instrument (OLCI. To that end, an unmixing model is proposed with the intention of integrating the benefits of the two Sentinel missions, when both in orbit, in one composite image. The main goal is to fill the data gaps in the S-2 record, based on the more frequent information of the S-3 time series. The proposed fusion model has been applied on MODIS (MOD09GA L2G and SPOT4 (Take 5 data and the experimental results have demonstrated that the approach has high potential. However, the different acquisition characteristics of the sensors, i.e. illumination and viewing geometry, should be taken into consideration and bidirectional effects correction has to be performed in order to reduce noise in the reflectance time series.

14. A Conditionally Beta Distributed Time-Series Model With Application to Monthly US Corporate Default Rates

DEFF Research Database (Denmark)

Nielsen, Thor Pajhede

2017-01-01

We consider an observation driven, conditionally Beta distributed model for variables restricted to the unit interval. The model includes both explanatory variables and autoregressive dependence in the mean and precision parameters using the mean-precision parametrization of the beta distribution...

15. Detecting exoplanets: jointly modeling radial velocity and stellar activity time series

Science.gov (United States)

Jones, David Edward; Stenning, David; Ford, Eric B.; Wolpert, Robert L.; Loredo, Thomas J.

2017-06-01

The radial velocity method is one of the most successful techniques for detecting exoplanets, but stellar activity often corrupts the radial velocity signal. This corruption can make it difficult to detect low mass planets and planets orbiting more active stars. A principled approach to recovering planet radial velocity signals in the presence of stellar activity was proposed by Rajpaul et al. (2015) and involves the use of dependent Gaussian processes to jointly model the corrupted radial velocity signal and multiple proxies for stellar activity. We build on this work in two ways: (i) we propose using dimension reduction techniques to construct more informative stellar activity proxies; (ii) we extend the Rajpaul et al. (2015) model to a larger class of models and use a model comparison procedure to select the best model for the particular stellar activity proxies at hand. Our framework enables us to compare the performance of various proxies in terms of the resulting statistical power for planet detection.

16. A Time Series Forecasting Method

Directory of Open Access Journals (Sweden)

Wang Zhao-Yu

2017-01-01

Full Text Available This paper proposes a novel time series forecasting method based on a weighted self-constructing clustering technique. The weighted self-constructing clustering processes all the data patterns incrementally. If a data pattern is not similar enough to an existing cluster, it forms a new cluster of its own. However, if a data pattern is similar enough to an existing cluster, it is removed from the cluster it currently belongs to and added to the most similar cluster. During the clustering process, weights are learned for each cluster. Given a series of time-stamped data up to time t, we divide it into a set of training patterns. By using the weighted self-constructing clustering, the training patterns are grouped into a set of clusters. To estimate the value at time t + 1, we find the k nearest neighbors of the input pattern and use these k neighbors to decide the estimation. Experimental results are shown to demonstrate the effectiveness of the proposed approach.

17. Modeling and forecasting of the under-five mortality rate in Kermanshah province in Iran: a time series analysis.

Science.gov (United States)

Rostami, Mehran; Jalilian, Abdollah; Hamzeh, Behrooz; Laghaei, Zahra

2015-01-01

The target of the Fourth Millennium Development Goal (MDG-4) is to reduce the rate of under-five mortality by two-thirds between 1990 and 2015. Despite substantial progress towards achieving the target of the MDG-4 in Iran at the national level, differences at the sub-national levels should be taken into consideration. The under-five mortality data available from the Deputy of Public Health, Kermanshah University of Medical Sciences, was used in order to perform a time series analysis of the monthly under-five mortality rate (U5MR) from 2005 to 2012 in Kermanshah province in the west of Iran. After primary analysis, a seasonal auto-regressive integrated moving average model was chosen as the best fitting model based on model selection criteria. The model was assessed and proved to be adequate in describing variations in the data. However, the unexpected presence of a stochastic increasing trend and a seasonal component with a periodicity of six months in the fitted model are very likely to be consequences of poor quality of data collection and reporting systems. The present work is the first attempt at time series modeling of the U5MR in Iran, and reveals that improvement of under-five mortality data collection in health facilities and their corresponding systems is a major challenge to fully achieving the MGD-4 in Iran. Studies similar to the present work can enhance the understanding of the invisible patterns in U5MR, monitor progress towards the MGD-4, and predict the impact of future variations on the U5MR.

18. Forecasting of ozone level in time series using MLP model with a novel hybrid training algorithm

Science.gov (United States)

Wang, Dong; Lu, Wei-Zhen

As far as the impact of tropospheric ozone (O 3) on human heath and plant life are concerned, forecasting its daily maximum level is of great importance in Hong Kong as well as other metropolises in the world. This paper proposed a multi-layer perceptron (MLP) model with a novel hybrid training method to perform the forecasting task. The training method synergistically couples a stochastic particle swarm optimization (PSO) algorithm and a deterministic Levenberg-Marquardt (LM) algorithm, which aims at exploiting the advantage of both. The performance of such a hybrid model is further compared with ones obtained by the MLP model trained individually by these two training methods mentioned above. Based on original data collected from two typical monitoring sites with different O 3 formation and transportation mechanism, the simulation results show that the hybrid model is more robust and efficient than the other two models by not only producing good results during non-episodes but also providing better consistency with the original data during episodes.

19. Discretization of time series data.

Science.gov (United States)

Dimitrova, Elena S; Licona, M Paola Vera; McGee, John; Laubenbacher, Reinhard

2010-06-01

An increasing number of algorithms for biochemical network inference from experimental data require discrete data as input. For example, dynamic Bayesian network methods and methods that use the framework of finite dynamical systems, such as Boolean networks, all take discrete input. Experimental data, however, are typically continuous and represented by computer floating point numbers. The translation from continuous to discrete data is crucial in preserving the variable dependencies and thus has a significant impact on the performance of the network inference algorithms. We compare the performance of two such algorithms that use discrete data using several different discretization algorithms. One of the inference methods uses a dynamic Bayesian network framework, the other-a time-and state-discrete dynamical system framework. The discretization algorithms are quantile, interval discretization, and a new algorithm introduced in this article, SSD. SSD is especially designed for short time series data and is capable of determining the optimal number of discretization states. The experiments show that both inference methods perform better with SSD than with the other methods. In addition, SSD is demonstrated to preserve the dynamic features of the time series, as well as to be robust to noise in the experimental data. A C++ implementation of SSD is available from the authors at http://polymath.vbi.vt.edu/discretization .

20. Passenger Flow Forecasting Research for Airport Terminal Based on SARIMA Time Series Model

Science.gov (United States)

Li, Ziyu; Bi, Jun; Li, Zhiyin

2017-12-01

Based on the data of practical operating of Kunming Changshui International Airport during2016, this paper proposes Seasonal Autoregressive Integrated Moving Average (SARIMA) model to predict the passenger flow. This article not only considers the non-stationary and autocorrelation of the sequence, but also considers the daily periodicity of the sequence. The prediction results can accurately describe the change trend of airport passenger flow and provide scientific decision support for the optimal allocation of airport resources and optimization of departure process. The result shows that this model is applicable to the short-term prediction of airport terminal departure passenger traffic and the average error ranges from 1% to 3%. The difference between the predicted and the true values of passenger traffic flow is quite small, which indicates that the model has fairly good passenger traffic flow prediction ability.

1. Time series forecasting using ERNN and QR based on Bayesian model averaging

Science.gov (United States)

Pwasong, Augustine; Sathasivam, Saratha

2017-08-01

The Bayesian model averaging technique is a multi-model combination technique. The technique was employed to amalgamate the Elman recurrent neural network (ERNN) technique with the quadratic regression (QR) technique. The amalgamation produced a hybrid technique known as the hybrid ERNN-QR technique. The potentials of forecasting with the hybrid technique are compared with the forecasting capabilities of individual techniques of ERNN and QR. The outcome revealed that the hybrid technique is superior to the individual techniques in the mean square error sense.

2. The role of initial values in nonstationary fractional time series models

DEFF Research Database (Denmark)

Johansen, Søren; Nielsen, Morten Ørregaard

We consider the nonstationary fractional model $\\Delta^{d}X_{t}=\\varepsilon _{t}$ with $\\varepsilon_{t}$ i.i.d.$(0,\\sigma^{2})$ and $d>1/2$. We derive an analytical expression for the main term of the asymptotic bias of the maximum likelihood estimator of $d$ conditional on initial values, and we...

3. Testing for a Common Volatility Process and Information Spillovers in Bivariate Financial Time Series Models

NARCIS (Netherlands)

J. Chen (Jinghui); M. Kobayashi (Masahito); M.J. McAleer (Michael)

2016-01-01

textabstractThe paper considers the problem as to whether financial returns have a common volatility process in the framework of stochastic volatility models that were suggested by Harvey et al. (1994). We propose a stochastic volatility version of the ARCH test proposed by Engle and Susmel (1993),

4. Physics, Nonlinear Time Series Analysis, Data Assimilation and Hyperfast Modeling of Nonlinear Ocean Waves

Science.gov (United States)

2010-09-30

Hyperfast Modeling of Nonlinear Ocean Waves A. R. Osborne Dipartimento di Fisica Generale, Università di Torino Via Pietro Giuria 1, 10125...PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Universit?i Torino,Dipartimento di Fisica Generale,Via Pietro Giuria 1,10125 Torino, Italy, 8. PERFORMING

5. Introduction to time series analysis and forecasting

CERN Document Server

Montgomery, Douglas C; Kulahci, Murat

2015-01-01

Praise for the First Edition ""…[t]he book is great for readers who need to apply the methods and models presented but have little background in mathematics and statistics."" -MAA Reviews Thoroughly updated throughout, Introduction to Time Series Analysis and Forecasting, Second Edition presents the underlying theories of time series analysis that are needed to analyze time-oriented data and construct real-world short- to medium-term statistical forecasts.    Authored by highly-experienced academics and professionals in engineering statistics, the Second Edition features discussions on both

6. A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series

Science.gov (United States)

Patel, Ameera X.; Kundu, Prantik; Rubinov, Mikail; Jones, P. Simon; Vértes, Petra E.; Ersche, Karen D.; Suckling, John; Bullmore, Edward T.

2014-01-01

7. Univariate time series modeling and an application to future claims amount in SOCSO's invalidity pension scheme

Science.gov (United States)

Chek, Mohd Zaki Awang; Ahmad, Abu Bakar; Ridzwan, Ahmad Nur Azam Ahmad; Jelas, Imran Md.; Jamal, Nur Faezah; Ismail, Isma Liana; Zulkifli, Faiz; Noor, Syamsul Ikram Mohd

2012-09-01

The main objective of this study is to forecast the future claims amount of Invalidity Pension Scheme (IPS). All data were derived from SOCSO annual reports from year 1972 - 2010. These claims consist of all claims amount from 7 benefits offered by SOCSO such as Invalidity Pension, Invalidity Grant, Survivors Pension, Constant Attendance Allowance, Rehabilitation, Funeral and Education. Prediction of future claims of Invalidity Pension Scheme will be made using Univariate Forecasting Models to predict the future claims among workforce in Malaysia.

8. Development of satellite green vegetation fraction time series for use in mesoscale modeling: application to the European heat wave 2006

DEFF Research Database (Denmark)

Nielsen, Joakim Refslund; Dellwik, Ebba; Hahmann, Andrea N.

2014-01-01

A method is presented for development of satellite green vegetation fraction (GVF) time series for use in the Weather Research and Forecasting (WRF) model. The GVF data is in the WRF model used to describe the temporal evolution of many land surface parameters, in addition to the evolution...... of vegetation. Several high-resolution GVF products, derived from high-quality satellite retrievals from Moderate Resolution Imaging Spectroradiometer images, were produced and their performance was evaluated in long-term WRF simulations. The atmospheric conditions during the 2006 heat wave year over Europe...... were simulated since significant interannual variability in vegetation seasonality was found. Such interannual variability is expected to increase in the coming decades due to climatic changes. The simulation using a quadratic normalized difference vegetation index to GVF relationship resulted...

9. A hybrid predictive model for acoustic noise in urban areas based on time series analysis and artificial neural network

Science.gov (United States)

Guarnaccia, Claudio; Quartieri, Joseph; Tepedino, Carmine

2017-06-01

The dangerous effect of noise on human health is well known. Both the auditory and non-auditory effects are largely documented in literature, and represent an important hazard in human activities. Particular care is devoted to road traffic noise, since it is growing according to the growth of residential, industrial and commercial areas. For these reasons, it is important to develop effective models able to predict the noise in a certain area. In this paper, a hybrid predictive model is presented. The model is based on the mixing of two different approach: the Time Series Analysis (TSA) and the Artificial Neural Network (ANN). The TSA model is based on the evaluation of trend and seasonality in the data, while the ANN model is based on the capacity of the network to "learn" the behavior of the data. The mixed approach will consist in the evaluation of noise levels by means of TSA and, once the differences (residuals) between TSA estimations and observed data have been calculated, in the training of a ANN on the residuals. This hybrid model will exploit interesting features and results, with a significant variation related to the number of steps forward in the prediction. It will be shown that the best results, in terms of prediction, are achieved predicting one step ahead in the future. Anyway, a 7 days prediction can be performed, with a slightly greater error, but offering a larger range of prediction, with respect to the single day ahead predictive model.

10. Comparative Time Series Analysis of Aerosol Optical Depth over Sites in United States and China Using ARIMA Modeling

Science.gov (United States)

Li, X.; Zhang, C.; Li, W.

2017-12-01

Long-term spatiotemporal analysis and modeling of aerosol optical depth (AOD) distribution is of paramount importance to study radiative forcing, climate change, and human health. This study is focused on the trends and variations of AOD over six stations located in United States and China during 2003 to 2015, using satellite-retrieved Moderate Resolution Imaging Spectrometer (MODIS) Collection 6 retrievals and ground measurements derived from Aerosol Robotic NETwork (AERONET). An autoregressive integrated moving average (ARIMA) model is applied to simulate and predict AOD values. The R2, adjusted R2, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Bayesian Information Criterion (BIC) are used as indices to select the best fitted model. Results show that there is a persistent decreasing trend in AOD for both MODIS data and AERONET data over three stations. Monthly and seasonal AOD variations reveal consistent aerosol patterns over stations along mid-latitudes. Regional differences impacted by climatology and land cover types are observed for the selected stations. Statistical validation of time series models indicates that the non-seasonal ARIMA model performs better for AERONET AOD data than for MODIS AOD data over most stations, suggesting the method works better for data with higher quality. By contrast, the seasonal ARIMA model reproduces the seasonal variations of MODIS AOD data much more precisely. Overall, the reasonably predicted results indicate the applicability and feasibility of the stochastic ARIMA modeling technique to forecast future and missing AOD values.

11. Application of Time-series Model to Predict Groundwater Quality Parameters for Agriculture: (Plain Mehran Case Study)

Science.gov (United States)

2018-03-01

Underground water is regarded as considerable water source which is mainly available in arid and semi arid with deficient surface water source. Forecasting of hydrological variables are suitable tools in water resources management. On the other hand, time series concepts is considered efficient means in forecasting process of water management. In this study the data including qualitative parameters (electrical conductivity and sodium adsorption ratio) of 17 underground water wells in Mehran Plain has been used to model the trend of parameters change over time. Using determined model, the qualitative parameters of groundwater is predicted for the next seven years. Data from 2003 to 2016 has been collected and were fitted by AR, MA, ARMA, ARIMA and SARIMA models. Afterward, the best model is determined using information criterion or Akaike (AIC) and correlation coefficient. After modeling parameters, the map of agricultural land use in 2016 and 2023 were generated and the changes between these years were studied. Based on the results, the average of predicted SAR (Sodium Adsorption Rate) in all wells in the year 2023 will increase compared to 2016. EC (Electrical Conductivity) average in the ninth and fifteenth holes and decreases in other wells will be increased. The results indicate that the quality of groundwater for Agriculture Plain Mehran will decline in seven years.

12. Performance Evaluation of Linear (ARMA and Threshold Nonlinear (TAR Time Series Models in Daily River Flow Modeling (Case Study: Upstream Basin Rivers of Zarrineh Roud Dam

Directory of Open Access Journals (Sweden)

2017-01-01

Full Text Available Introduction: Time series models are generally categorized as a data-driven method or mathematically-based method. These models are known as one of the most important tools in modeling and forecasting of hydrological processes, which are used to design and scientific management of water resources projects. On the other hand, a better understanding of the river flow process is vital for appropriate streamflow modeling and forecasting. One of the main concerns of hydrological time series modeling is whether the hydrologic variable is governed by the linear or nonlinear models through time. Although the linear time series models have been widely applied in hydrology research, there has been some recent increasing interest in the application of nonlinear time series approaches. The threshold autoregressive (TAR method is frequently applied in modeling the mean (first order moment of financial and economic time series. Thise type of the model has not received considerable attention yet from the hydrological community. The main purposes of this paper are to analyze and to discuss stochastic modeling of daily river flow time series of the study area using linear (such as ARMA: autoregressive integrated moving average and non-linear (such as two- and three- regime TAR models. Material and Methods: The study area has constituted itself of four sub-basins namely, Saghez Chai, Jighato Chai, Khorkhoreh Chai and Sarogh Chai from west to east, respectively, which discharge water into the Zarrineh Roud dam reservoir. River flow time series of 6 hydro-gauge stations located on upstream basin rivers of Zarrineh Roud dam (located in the southern part of Urmia Lake basin were considered to model purposes. All the data series used here to start from January 1, 1997, and ends until December 31, 2011. In this study, the daily river flow data from January 01 1997 to December 31 2009 (13 years were chosen for calibration and data for January 01 2010 to December 31 2011

13. From time series analysis to a biomechanical multibody model of the human eye

International Nuclear Information System (INIS)

Pascolo, P.; Carniel, R.

2009-01-01

A mechanical model of the human eye is presented aimed at estimating the level of muscular activation. The applicability of the model in the biomedical field is discussed. Human eye movements studied in the laboratory are compared with the ones produced by a virtual eye described in kinematical terms and subject to the dynamics of six actuators, as many as the muscular systems devoted to the eye motion control. The definition of an error function between the experimental and the numerical response and the application of a suitable law that links activation and muscular force are at the base of the proposed methodology. The aim is the definition of a simple conceptual tool that could help the specialist in the diagnosis of potential physiological disturbances of saccadic and nystagmic movements but can also be extended in a second phase when more sophisticated data become available. The work is part of a collaboration between the Functional Mechanics Laboratory of the University and the Neurophysiopatology Laboratory of the 'S. Maria della Misericordia' Hospital in Udine, Italy.

14. Model Pemesanan Bahan Baku menggunakan Peramalan Time Series dengan CB Predictor

Directory of Open Access Journals (Sweden)

2014-12-01

Full Text Available A company that manufactures finished goods often faces a shortage of raw materials, due to the determination of the quantity of raw material ordering improper because it is done by intuition and the lack of raw material inventory reserves. This resulted in costs because inefficient production processes are inhibited or had to perform an emergency procurement of raw materials to meet customer orders. The company seeks to use the method in determining the order quantity of raw material, comprising the steps of (1 collecting historical data of raw material use, step (2 forecasting needs raw materials, step (3 calculating the order quantity forecasting based on the data by comparing the deterministic method and probabilistic methods. Calculating safety stock for each raw material is done so as to cope with the situation outside of normal conditions, such a surge in orders. In its design, the system will be developed using the Unified Modeling Language modeling language (UML on the basis of the concept of object-oriented analysis and design (Object Oriented Analysis and Design. With the proposed implementation of the information system, the company can estimate the need for raw materials more quickly and accurately, and can determine the order quantity that is tailored to the needs. So that the costs associated with ordering and storage of raw materials can be minimized.

15. A new wind speed forecasting strategy based on the chaotic time series modelling technique and the Apriori algorithm

International Nuclear Information System (INIS)

Guo, Zhenhai; Chi, Dezhong; Wu, Jie; Zhang, Wenyu

2014-01-01

Highlights: • Impact of meteorological factors on wind speed forecasting is taken into account. • Forecasted wind speed results are corrected by the associated rules. • Forecasting accuracy is improved by the new wind speed forecasting strategy. • Robust of the proposed model is validated by data sampled from different sites. - Abstract: Wind energy has been the fastest growing renewable energy resource in recent years. Because of the intermittent nature of wind, wind power is a fluctuating source of electrical energy. Therefore, to minimize the impact of wind power on the electrical grid, accurate and reliable wind power forecasting is mandatory. In this paper, a new wind speed forecasting approach based on based on the chaotic time series modelling technique and the Apriori algorithm has been developed. The new approach consists of four procedures: (I) Clustering by using the k-means clustering approach; (II) Employing the Apriori algorithm to discover the association rules; (III) Forecasting the wind speed according to the chaotic time series forecasting model; and (IV) Correcting the forecasted wind speed data using the associated rules discovered previously. This procedure has been verified by 31-day-ahead daily average wind speed forecasting case studies, which employed the wind speed and other meteorological data collected from four meteorological stations located in the Hexi Corridor area of China. The results of these case studies reveal that the chaotic forecasting model can efficiently improve the accuracy of the wind speed forecasting, and the Apriori algorithm can effectively discover the association rules between the wind speed and other meteorological factors. In addition, the correction results demonstrate that the association rules discovered by the Apriori algorithm have powerful capacities in handling the forecasted wind speed values correction when the forecasted values do not match the classification discovered by the association rules

16. Improved vertical displacements induced by a refined thermal expansion model and its quantitative analysis in GPS height time series

Science.gov (United States)

Wang, Kaihua; Chen, Hua; Jiang, Weiping; Li, Zhao; Ma, Yifang; Deng, Liansheng

2018-04-01

There are apparent seasonal variations in GPS height time series, and thermal expansion is considered to be one of the potential geophysical contributors. The displacements introduced by thermal expansion are usually derived without considering the annex height and underground part of the monument (e.g. located on roof or top of the buildings), which may bias the geophysical explanation of the seasonal oscillation. In this paper, the improved vertical displacements are derived by a refined thermal expansion model where the annex height and underground depth of the monument are taken into account, and then 560 IGS stations are adopted to validate the modeled thermal expansion (MTE) displacements. In order to evaluate the impact of thermal expansion on GPS heights, the MTE displacements of 80 IGS stations with less data discontinuities are selected to compare with their observed GPS vertical (OGV) displacements with the modeled surface loading (MSL) displacements removed in advance. Quantitative analysis results show the maximum annual and semiannual amplitudes of the MTE are 6.65 mm (NOVJ) and 0.51 mm (IISC), respectively, and the maximum peak-to-peak oscillation of the MTE displacements can be 19.4 mm. The average annual amplitude reductions are 0.75 mm and 1.05 mm respectively after removing the MTE and MSL displacements from the OGV, indicating the seasonal oscillation induced by thermal expansion is equivalent to >75% of the impact of surface loadings. However, there are rarely significant reductions for the semiannual amplitude. Given the result in this study that thermal expansion can explain 17.3% of the annual amplitude in GPS heights on average, it must be precisely modeled both in GPS precise data processing and GPS time series analysis, especially for those stations located in the middle and high latitudes with larger annual temperature oscillation, or stations with higher monument.

17. A bootstrap method to avoid the effect of concurvity in generalised additive models in time series studies of air pollution.

Science.gov (United States)

2005-10-01

In recent years a great number of studies have applied generalised additive models (GAMs) to time series data to estimate the short term health effects of air pollution. Lately, however, it has been found that concurvity--the non-parametric analogue of multicollinearity--might lead to underestimation of standard errors of the effects of independent variables. Underestimation of standard errors means that for concurvity levels commonly present in the data, the risk of committing type I error rises by over threefold. This study developed a conditional bootstrap methology that consists of assuming that the outcome in any observation is conditional upon the values of the set of independent variables used. It then tested this procedure by means of a simulation study using a Poisson additive model. The response variable of this model is a function of an unobserved confounding variable (that introduces trend and seasonality), real black smoke data, and temperature. Scenarios were created with different coefficients and degrees of concurvity. Conditional bootstrap provides confidence intervals with coverages close to nominal (95%), irrespective of the degree of concurvity, number of variables in the model or magnitude of the coefficient to be estimated (for example, for a concurvity of 0.85, bootstrap confidence interval coverage is 95% compared with 71% in the case of the asymptotic interval obtained directly with S-plus gam function). The bootstrap method avoids the problem of concurvity in time series studies of air pollution, and is easily generalised to non-linear dose-risk effects. All bootstrap calculations described in this paper can be performed using S-Plus gam.boot software.

18. Seasonal trend analysis and ARIMA modeling of relative humidity and wind speed time series around Yamula Dam

Science.gov (United States)

Eymen, Abdurrahman; Köylü, Ümran

2018-02-01

Local climate change is determined by analysis of long-term recorded meteorological data. In the statistical analysis of the meteorological data, the Mann-Kendall rank test, which is one of the non-parametrical tests, has been used; on the other hand, for determining the power of the trend, Theil-Sen method has been used on the data obtained from 16 meteorological stations. The stations cover the provinces of Kayseri, Sivas, Yozgat, and Nevşehir in the Central Anatolia region of Turkey. Changes in land-use affect local climate. Dams are structures that cause major changes on the land. Yamula Dam is located 25 km northwest of Kayseri. The dam has huge water body which is approximately 85 km2. The mentioned tests have been used for detecting the presence of any positive or negative trend in meteorological data. The meteorological data in relation to the seasonal average, maximum, and minimum values of the relative humidity and seasonal average wind speed have been organized as time series and the tests have been conducted accordingly. As a result of these tests, the following have been identified: increase was observed in minimum relative humidity values in the spring, summer, and autumn seasons. As for the seasonal average wind speed, decrease was detected for nine stations in all seasons, whereas increase was observed in four stations. After the trend analysis, pre-dam mean relative humidity time series were modeled with Autoregressive Integrated Moving Averages (ARIMA) model which is statistical modeling tool. Post-dam relative humidity values were predicted by ARIMA models.

19. Unravelling earth flow dynamics with 3-D time series derived from UAV-SfM models

Directory of Open Access Journals (Sweden)

F. Clapuyt

2017-12-01

Full Text Available Accurately assessing geo-hazards and quantifying landslide risks in mountainous environments are gaining importance in the context of the ongoing global warming. For an in-depth understanding of slope failure mechanisms, accurate monitoring of the mass movement topography at high spatial and temporal resolutions remains essential. The choice of the acquisition framework for high-resolution topographic reconstructions will mainly result from the trade-off between the spatial resolution needed and the extent of the study area. Recent advances in the development of unmanned aerial vehicle (UAV-based image acquisition combined with the structure-from-motion (SfM algorithm for three-dimensional (3-D reconstruction make the UAV-SfM framework a competitive alternative to other high-resolution topographic techniques. In this study, we aim at gaining in-depth knowledge of the Schimbrig earthflow located in the foothills of the Central Swiss Alps by monitoring ground surface displacements at very high spatial and temporal resolution using the efficiency of the UAV-SfM framework. We produced distinct topographic datasets for three acquisition dates between 2013 and 2015 in order to conduct a comprehensive 3-D analysis of the landslide. Therefore, we computed (1 the sediment budget of the hillslope, and (2 the horizontal and (3 the three-dimensional surface displacements. The multitemporal UAV-SfM based topographic reconstructions allowed us to quantify rates of sediment redistribution and surface movements. Our data show that the Schimbrig earthflow is very active, with mean annual horizontal displacement ranging between 6 and 9 m. Combination and careful interpretation of high-resolution topographic analyses reveal the internal mechanisms of the earthflow and its complex rotational structure. In addition to variation in horizontal surface movements through time, we interestingly showed that the configuration of nested rotational units changes through

20. Unravelling earth flow dynamics with 3-D time series derived from UAV-SfM models

Science.gov (United States)

Clapuyt, François; Vanacker, Veerle; Schlunegger, Fritz; Van Oost, Kristof

2017-12-01

Accurately assessing geo-hazards and quantifying landslide risks in mountainous environments are gaining importance in the context of the ongoing global warming. For an in-depth understanding of slope failure mechanisms, accurate monitoring of the mass movement topography at high spatial and temporal resolutions remains essential. The choice of the acquisition framework for high-resolution topographic reconstructions will mainly result from the trade-off between the spatial resolution needed and the extent of the study area. Recent advances in the development of unmanned aerial vehicle (UAV)-based image acquisition combined with the structure-from-motion (SfM) algorithm for three-dimensional (3-D) reconstruction make the UAV-SfM framework a competitive alternative to other high-resolution topographic techniques. In this study, we aim at gaining in-depth knowledge of the Schimbrig earthflow located in the foothills of the Central Swiss Alps by monitoring ground surface displacements at very high spatial and temporal resolution using the efficiency of the UAV-SfM framework. We produced distinct topographic datasets for three acquisition dates between 2013 and 2015 in order to conduct a comprehensive 3-D analysis of the landslide. Therefore, we computed (1) the sediment budget of the hillslope, and (2) the horizontal and (3) the three-dimensional surface displacements. The multitemporal UAV-SfM based topographic reconstructions allowed us to quantify rates of sediment redistribution and surface movements. Our data show that the Schimbrig earthflow is very active, with mean annual horizontal displacement ranging between 6 and 9 m. Combination and careful interpretation of high-resolution topographic analyses reveal the internal mechanisms of the earthflow and its complex rotational structure. In addition to variation in horizontal surface movements through time, we interestingly showed that the configuration of nested rotational units changes through time. Although

1. Multispectral Photoacoustic Imaging Artifact Removal and Denoising Using Time Series Model-Based Spectral Noise Estimation.

Science.gov (United States)

Kazakeviciute, Agne; Ho, Chris Jun Hui; Olivo, Malini

2016-09-01

The aim of this study is to solve a problem of denoising and artifact removal from in vivo multispectral photoacoustic imaging when the level of noise is not known a priori. The study analyzes Wiener filtering in Fourier domain when a family of anisotropic shape filters is considered. The unknown noise and signal power spectral densities are estimated using spectral information of images and the autoregressive of the power 1 ( AR(1)) model. Edge preservation is achieved by detecting image edges in the original and the denoised image and superimposing a weighted contribution of the two edge images to the resulting denoised image. The method is tested on multispectral photoacoustic images from simulations, a tissue-mimicking phantom, as well as in vivo imaging of the mouse, with its performance compared against that of the standard Wiener filtering in Fourier domain. The results reveal better denoising and fine details preservation capabilities of the proposed method when compared to that of the standard Wiener filtering in Fourier domain, suggesting that this could be a useful denoising technique for other multispectral photoacoustic studies.

2. A Bayesian method for characterizing distributed micro-releases: II. inference under model uncertainty with short time-series data.

Energy Technology Data Exchange (ETDEWEB)

Marzouk, Youssef; Fast P. (Lawrence Livermore National Laboratory, Livermore, CA); Kraus, M. (Peterson AFB, CO); Ray, J. P.

2006-01-01

Terrorist attacks using an aerosolized pathogen preparation have gained credibility as a national security concern after the anthrax attacks of 2001. The ability to characterize such attacks, i.e., to estimate the number of people infected, the time of infection, and the average dose received, is important when planning a medical response. We address this question of characterization by formulating a Bayesian inverse problem predicated on a short time-series of diagnosed patients exhibiting symptoms. To be of relevance to response planning, we limit ourselves to 3-5 days of data. In tests performed with anthrax as the pathogen, we find that these data are usually sufficient, especially if the model of the outbreak used in the inverse problem is an accurate one. In some cases the scarcity of data may initially support outbreak characterizations at odds with the true one, but with sufficient data the correct inferences are recovered; in other words, the inverse problem posed and its solution methodology are consistent. We also explore the effect of model error-situations for which the model used in the inverse problem is only a partially accurate representation of the outbreak; here, the model predictions and the observations differ by more than a random noise. We find that while there is a consistent discrepancy between the inferred and the true characterizations, they are also close enough to be of relevance when planning a response.

3. Lake Area Analysis Using Exponential Smoothing Model and Long Time-Series Landsat Images in Wuhan, China

Directory of Open Access Journals (Sweden)

Gonghao Duan

2018-01-01

Full Text Available The loss of lake area significantly influences the climate change in a region, and this loss represents a serious and unavoidable challenge to maintaining ecological sustainability under the circumstances of lakes that are being filled. Therefore, mapping and forecasting changes in the lake is critical for protecting the environment and mitigating ecological problems in the urban district. We created an accessible map displaying area changes for 82 lakes in the Wuhan city using remote sensing data in conjunction with visual interpretation by combining field data with Landsat 2/5/7/8 Thematic Mapper (TM time-series images for the period 1987–2013. In addition, we applied a quadratic exponential smoothing model to forecast lake area changes in Wuhan city. The map provides, for the first time, estimates of lake development in Wuhan using data required for local-scale studies. The model predicted a lake area reduction of 18.494 km2 in 2015. The average error reached 0.23 with a correlation coefficient of 0.98, indicating that the model is reliable. The paper provided a numerical analysis and forecasting method to provide a better understanding of lake area changes. The modeling and mapping results can help assess aquatic habitat suitability and property planning for Wuhan lakes.

4. Performance evaluation of linear time-series ionospheric Total Electron Content model over low latitude Indian GPS stations

Science.gov (United States)

Dabbakuti, J. R. K. Kumar; Venkata Ratnam, D.

2017-10-01

Precise modeling of the ionospheric Total Electron Content (TEC) is a critical aspect of Positioning, Navigation, and Timing (PNT) services intended for the Global Navigation Satellite Systems (GNSS) applications as well as Earth Observation System (EOS), satellite communication, and space weather forecasting applications. In this paper, linear time series modeling has been carried out on ionospheric TEC at two different locations at Koneru Lakshmaiah University (KLU), Guntur (geographic 16.44° N, 80.62° E; geomagnetic 7.55° N) and Bangalore (geographic 12.97° N, 77.59° E; geomagnetic 4.53° N) at the northern low-latitude region, for the year 2013 in the 24th solar cycle. The impact of the solar and geomagnetic activity on periodic oscillations of TEC has been investigated. Results confirm that the correlation coefficient of the estimated TEC from the linear model TEC and the observed GPS-TEC is around 93%. Solar activity is the key component that influences ionospheric daily averaged TEC while periodic component reveals the seasonal dependency of TEC. Furthermore, it is observed that the influence of geomagnetic activity component on TEC is different at both the latitudes. The accuracy of the model has been assessed by comparing the International Reference Ionosphere (IRI) 2012 model TEC and TEC measurements. Moreover, the absence of winter anomaly is remarkable, as determined by the Root Mean Square Error (RMSE) between the linear model TEC and GPS-TEC. On the contrary, the IRI2012 model TEC evidently failed to predict the absence of winter anomaly in the Equatorial Ionization Anomaly (EIA) crest region. The outcome of this work will be useful for improving the ionospheric now-casting models under various geophysical conditions.

5. Markov Trends in Macroeconomic Time Series

NARCIS (Netherlands)

R. Paap (Richard)

1997-01-01

textabstractMany macroeconomic time series are characterised by long periods of positive growth, expansion periods, and short periods of negative growth, recessions. A popular model to describe this phenomenon is the Markov trend, which is a stochastic segmented trend where the slope depends on the

6. Estimating Sub-Pixel Soybean Fraction from Time-Series MODIS Data Using an Optimized Geographically Weighted Regression Model

Directory of Open Access Journals (Sweden)

Qiong Hu

2018-03-01

Full Text Available Soybean cultivation in China has significantly decreased due to the rising import of genetically modified soybeans from other countries. Understanding soybean’s extent and change information is of great value for national agricultural policy implications and global food security. Some previous studies have explored the quantitative relationships between crop area and spectral variables derived from remote sensing data. However, both those linear or non-linear relationships were expressed by global regression models, which ignored the spatial non-stationarity of crop spectral signature and may limit the prediction accuracy. This study presented a geographically weighted regression model (GWR to estimate fractional soybean at 250 m spatial resolution in Heilongjiang Province, one of the most important food production regions in China, using time-series MODIS data and high-quality calibration information derived from Landsat data. A forward stepwise optimization strategy was embedded with the GWR model to select the optimal subset of independent variables for soybeans. Normalized Difference Vegetation Index (NDVI of Julian day 233 to 257 when soybeans are filling seed was found to be the most important temporal period for sub-pixel soybean area estimation. Our MODIS-based soybean area compared well with Landsat-based results at pixel-level. Also, there was a good agreement between the MODIS-based result and census data at county level, with the coefficient of determination (R2 of 0.80 and the root mean square error (RMSE was 340.21 km2. Additionally, F-test results showed GWR model had better model goodness-of-fit and higher prediction accuracy than the traditional ordinary least squares (OLS model. These promising results suggest crop spectral variations both at temporal and spatial scales should be considered when exploring its relationship with pixel-level crop acreage. The optimized GWR model by combining an automated feature selection

7. Detecting geothermal anomalies and evaluating LST geothermal component by combining thermal remote sensing time series and land surface model data

Science.gov (United States)

Romaguera, Mireia; Vaughan, R. Greg; Ettema, J.; Izquierdo-Verdiguier, E.; Hecker, C. A.; van der Meer, F.D.

2018-01-01

This paper explores for the first time the possibilities to use two land surface temperature (LST) time series of different origins (geostationary Meteosat Second Generation satellite data and Noah land surface modelling, LSM), to detect geothermal anomalies and extract the geothermal component of LST, the LSTgt. We hypothesize that in geothermal areas the LSM time series will underestimate the LST as compared to the remote sensing data, since the former does not account for the geothermal component in its model.In order to extract LSTgt, two approaches of different nature (physical based and data mining) were developed and tested in an area of about 560 × 560 km2 centered at the Kenyan Rift. Pre-dawn data in the study area during the first 45 days of 2012 were analyzed.The results show consistent spatial and temporal LSTgt patterns between the two approaches, and systematic differences of about 2 K. A geothermal area map from surface studies was used to assess LSTgt inside and outside the geothermal boundaries. Spatial means were found to be higher inside the geothermal limits, as well as the relative frequency of occurrence of high LSTgt. Results further show that areas with strong topography can result in anomalously high LSTgt values (false positives), which suggests the need for a slope and aspect correction in the inputs to achieve realistic results in those areas. The uncertainty analysis indicates that large uncertainties of the input parameters may limit detection of LSTgt anomalies. To validate the approaches, higher spatial resolution images from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data over the Olkaria geothermal field were used. An established method to estimate radiant geothermal flux was applied providing values between 9 and 24 W/m2 in the geothermal area, which coincides with the LSTgt flux rates obtained with the proposed approaches.The proposed approaches are a first step in estimating LSTgt

8. Parameter estimation methods for gene circuit modeling from time-series mRNA data: a comparative study

KAUST Repository

Fan, M.

2015-03-29

Parameter estimation is a challenging computational problemin the reverse engineering of biological systems. Because advances in biotechnology have facilitated wide availability of time-series gene expression data, systematic parameter esti- mation of gene circuitmodels fromsuch time-series mRNA data has become an importantmethod for quantitatively dissecting the regulation of gene expression. By focusing on themodeling of gene circuits, we examine here the perform- ance of three types of state-of-the-art parameter estimation methods: population-basedmethods, onlinemethods and model-decomposition-basedmethods. Our results show that certain population-basedmethods are able to generate high- quality parameter solutions. The performance of thesemethods, however, is heavily dependent on the size of the param- eter search space, and their computational requirements substantially increase as the size of the search space increases. In comparison, onlinemethods andmodel decomposition-basedmethods are computationally faster alternatives and are less dependent on the size of the search space. Among other things, our results show that a hybrid approach that augments computationally fastmethods with local search as a subsequent refinement procedure can substantially increase the qual- ity of their parameter estimates to the level on par with the best solution obtained fromthe population-basedmethods whilemaintaining high computational speed. These suggest that such hybridmethods can be a promising alternative to themore commonly used population-basedmethods for parameter estimation of gene circuit models when limited prior knowledge about the underlying regulatorymechanismsmakes the size of the parameter search space vastly large. © The Author 2015. Published by Oxford University Press.

9. CLUSTERnGO: a user-defined modelling platform for two-stage clustering of time-series data.

Science.gov (United States)

Fidaner, Işık Barış; Cankorur-Cetinkaya, Ayca; Dikicioglu, Duygu; Kirdar, Betul; Cemgil, Ali Taylan; Oliver, Stephen G

2016-02-01

Simple bioinformatic tools are frequently used to analyse time-series datasets regardless of their ability to deal with transient phenomena, limiting the meaningful information that may be extracted from them. This situation requires the development and exploitation of tailor-made, easy-to-use and flexible tools designed specifically for the analysis of time-series datasets. We present a novel statistical application called CLUSTERnGO, which uses a model-based clustering algorithm that fulfils this need. This algorithm involves two components of operation. Component 1 constructs a Bayesian non-parametric model (Infinite Mixture of Piecewise Linear Sequences) and Component 2, which applies a novel clustering methodology (Two-Stage Clustering). The software can also assign biological meaning to the identified clusters using an appropriate ontology. It applies multiple hypothesis testing to report the significance of these enrichments. The algorithm has a four-phase pipeline. The application can be executed using either command-line tools or a user-friendly Graphical User Interface. The latter has been developed to address the needs of both specialist and non-specialist users. We use three diverse test cases to demonstrate the flexibility of the proposed strategy. In all cases, CLUSTERnGO not only outperformed existing algorithms in assigning unique GO term enrichments to the identified clusters, but also revealed novel insights regarding the biological systems examined, which were not uncovered in the original publications. The C++ and QT source codes, the GUI applications for Windows, OS X and Linux operating systems and user manual are freely available for download under the GNU GPL v3 license at http://www.cmpe.boun.edu.tr/content/CnG. sgo24@cam.ac.uk Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

10. Assessing the Relationship Between Meteorological Parameters, Air Pollution And Cardiovascular Mortality of Mashhad City Based on Time Series Model

Directory of Open Access Journals (Sweden)

Morteza Hatami

2017-10-01

Full Text Available Epidemiological studies conducted in the past two decades indicate that air pollution causes increase in cardiovascular, breathing and chronic bronchitis disorders and even causes cardiovascular mortality. Therefore, the aim of this study was to investigate the relationship between meteorological parameters, air pollution and cardiovascular mortality in the city of Mashhad in 2014 by a time series model. Data on mortality from cardiovascular disease, meteorological parameters and air pollution in 2014 were gathered from Paradises organization, meteorology organization and pollutant monitoring center, respectively. Then the relationship between these parameters was analyzed using correlation coefficient, generalized linear regression, time series models and comparison of means. The results of the study showed that the highest rate of cardiovascular mortality related to Sulfur dioxide, nitrogen dioxide and then PM2.5. So that each unit increase in SO2, NO2 and PM2.5 pollutants adds to the rate of cardiovascular mortality by 22.5, 2.9 and 0.69, respectively. Pressure, wind speed and rainfall have a significant association with mortality. So that each unit decrease in pressure and wind speed, increases the rate of cardiovascular mortality by 2.79 and 15.77, respectively. It was also found that in the case of one-unit increase in rainfall, the possibility of mortality from the mentioned disease goes up by 3.8 units. It was also found that one-year increase of the age increases the mortality caused by these diseases up to 0.57 percent. Furthermore, the highest rate of cardiovascular mortality related to cold periods of the year. Therefore, considering the growing trend of air pollution and its health effects on human health, performing actions and effective solutions is important in the field of controlling and reducing air pollution in Iranian metropolis including Mashhad.

11. Long-term prediction of solar and geomagnetic activity daily time series using singular spectrum analysis and fuzzy descriptor models

Science.gov (United States)

Mirmomeni, M.; Kamaliha, E.; Shafiee, M.; Lucas, C.

2009-09-01

Of the various conditions that affect space weather, Sun-driven phenomena are the most dominant. Cyclic solar activity has a significant effect on the Earth, its climate, satellites, and space missions. In recent years, space weather hazards have become a major area of investigation, especially due to the advent of satellite technology. As such, the design of reliable alerting and warning systems is of utmost importance, and international collaboration is needed to develop accurate short-term and long-term prediction methodologies. Several methods have been proposed and implemented for the prediction of solar and geomagnetic activity indices, but problems in predicting the exact time and magnitude of such catastrophic events still remain. There are, however, descriptor systems that describe a wider class of systems, including physical models and non-dynamic constraints. It is well known that the descriptor system is much tighter than the state-space expression for representing real independent parametric perturbations. In addition, the fuzzy descriptor models as a generalization of the locally linear neurofuzzy models are general forms that can be trained by constructive intuitive learning algorithms. Here, we propose a combined model based on fuzzy descriptor models and singular spectrum analysis (SSA) (FD/SSA) to forecast a number of geomagnetic activity indices in a manner that optimizes a fuzzy descriptor model for each of the principal components obtained from singular spectrum analysis and recombines the predicted values so as to transform the geomagnetic activity time series into natural chaotic phenomena. The method has been applied to predict two solar and geomagnetic activity indices: geomagnetic aa and solar wind speed (SWS) of the solar wind index. The results demonstrate the higher power of the proposed method-- compared to other methods -- for predicting solar activity.

12. Fourier series models through transformation | Omekara | Global ...

African Journals Online (AJOL)

This study considers the application of Fourier series analysis (FSA) to seasonal time series data. The ultimate objective of the study is to construct an FSA model that can lead to reliable forecast. Specifically, the study evaluates data for the assumptions of time series analysis; applies the necessary transformation to the ...

13. Trends in Average Living Children at the Time of Terminal Contraception: A Time Series Analysis Over 27 Years Using ARIMA (p, d, q) Nonseasonal Model.

Science.gov (United States)

Mumbare, Sachin S; Gosavi, Shriram; Almale, Balaji; Patil, Aruna; Dhakane, Supriya; Kadu, Aniruddha

2014-10-01

India's National Family Welfare Programme is dominated by sterilization, particularly tubectomy. Sterilization, being a terminal method of contraception, decides the final number of children for that couple. Many studies have shown the declining trend in the average number of living children at the time of sterilization over a short period of time. So this study was planned to do time series analysis of the average children at the time of terminal contraception, to do forecasting till 2020 for the same and to compare the rates of change in various subgroups of the population. Data was preprocessed in MS Access 2007 by creating and running SQL queries. After testing stationarity of every series with augmented Dickey-Fuller test, time series analysis and forecasting was done using best-fit Box-Jenkins ARIMA (p, d, q) nonseasonal model. To compare the rates of change of average children in various subgroups, at sterilization, analysis of covariance (ANCOVA) was applied. Forecasting showed that the replacement level of 2.1 total fertility rate (TFR) will be achieved in 2018 for couples opting for sterilization. The same will be achieved in 2020, 2016, 2018, and 2019 for rural area, urban area, Hindu couples, and Buddhist couples, respectively. It will not be achieved till 2020 in Muslim couples. Every stratum of population showed the declining trend. The decline for male children and in rural area was significantly faster than the decline for female children and in urban area, respectively. The decline was not significantly different in Hindu, Muslim, and Buddhist couples.

14. Trends in average living children at the time of terminal contraception: A time series analysis over 27 years using ARIMA (p, d, q nonseasonal model

Directory of Open Access Journals (Sweden)

Sachin S Mumbare

2014-01-01

Full Text Available Background: India′s National Family Welfare Programme is dominated by sterilization, particularly tubectomy. Sterilization, being a terminal method of contraception, decides the final number of children for that couple. Many studies have shown the declining trend in the average number of living children at the time of sterilization over a short period of time. So this study was planned to do time series analysis of the average children at the time of terminal contraception, to do forecasting till 2020 for the same and to compare the rates of change in various subgroups of the population. Materials and Methods: Data was preprocessed in MS Access 2007 by creating and running SQL queries. After testing stationarity of every series with augmented Dickey-Fuller test, time series analysis and forecasting was done using best-fit Box-Jenkins ARIMA (p, d, q nonseasonal model. To compare the rates of change of average children in various subgroups, at sterilization, analysis of covariance (ANCOVA was applied. Results: Forecasting showed that the replacement level of 2.1 total fertility rate (TFR will be achieved in 2018 for couples opting for sterilization. The same will be achieved in 2020, 2016, 2018, and 2019 for rural area, urban area, Hindu couples, and Buddhist couples, respectively. It will not be achieved till 2020 in Muslim couples. Conclusion: Every stratum of population showed the declining trend. The decline for male children and in rural area was significantly faster than the decline for female children and in urban area, respectively. The decline was not significantly different in Hindu, Muslim, and Buddhist couples.

15. Spatiotemporal distribution of strain field and hydraulic conductivity at the Phoenix valley basins, constrained using InSAR time series and time-dependent models

Science.gov (United States)

Miller, M. M.; Shirzaei, M.

2015-12-01

Poroelastic theory suggests that long-term aquifer deformation is linearly proportional to changes in pore pressure. Land subsidence is the surface expression of deformation occurring at depth that is observed with dense, detailed, and high precision interferometric SAR data. In earlier work, Miller & Shirzaei [2015] identified zones of subsidence and uplift across the Phoenix valley caused by pumping and artificial recharge operations. we combined ascending and descending Envisat InSAR datasets to estimate vertical and horizontal displacement time series from 2003-2010. Next, wavelet decomposition was used to extract and compare the elastic components of vertical deformation and hydraulic head data to estimate aquifer storage coefficients. In the following, we present the results from elastic aquifer modeling using a 3D array of triangular dislocations, extending from depth of 0.5 to 3.5 km. We employ a time-dependent modeling scheme to invert the InSAR displacement time series, solving for the spatiotemporal distribution of the aquifer-aquitard compaction. Such models are used to calculate strain and stress fields and forecast the location of extensional cracks and earth fissures, useful for urban planning and management. Later, applying the framework suggested by Burbey [1999], the optimum compaction model is used to estimate the 3D distribution of hydraulic conductivities as a function of time. These estimates are verified using in-situ and laboratory observations and provide unique evidence to investigate the stress-dependence of the hydraulic conductivity and its variations due to pumping, recharge, and injection. The estimates will also be used in groundwater flow models, enhancing water management in the valley and elsewhere. References Burby, T. J. (1999), Effects of horizontal strain in estimating specific storage and compaction in confined and leaky aquifer systems, Hydrogeology Journal, 7(6), 521-532, doi:10.1007/s100400050225. Miller, M. M., and M

16. Investigating and Modelling Effects of Climatically and Hydrologically Indicators on the Urmia Lake Coastline Changes Using Time Series Analysis

Science.gov (United States)

2017-09-01

Study of hydrological parameters of lakes and examine the variation of water level to operate management on water resources are important. The purpose of this study is to investigate and model the Urmia Lake water level changes due to changes in climatically and hydrological indicators that affects in the process of level variation and area of this lake. For this purpose, Landsat satellite images, hydrological data, the daily precipitation, the daily surface evaporation and the daily discharge in total of the lake basin during the period of 2010-2016 have been used. Based on time-series analysis that is conducted on individual data independently with same procedure, to model variation of Urmia Lake level, we used polynomial regression technique and combined polynomial with periodic behavior. In the first scenario, we fit a multivariate linear polynomial to our datasets and determining RMSE, NRSME and R² value. We found that fourth degree polynomial can better fit to our datasets with lowest RMSE value about 9 cm. In the second scenario, we combine polynomial with periodic behavior for modeling. The second scenario has superiority comparing to the first one, by RMSE value about 3 cm.

17. Modeling GPP in the Nordic forest landscape with MODIS time series data—Comparison with the MODIS GPP product

DEFF Research Database (Denmark)

Schubert, Per; Lagergren, Fredrik; Aurela, Mika

2012-01-01

. The main objective of this study was to investigate if MODIS 500m reflectance data can be used to drive empirical models for regional estimations of GPP in Nordic forests. The performance of the proposed models was compared with the MODIS 1km GPP product. Linear regression analyses were made on 8-day...... averages of eddy covariance GPP from three deciduous and ten coniferous sites in relation to MODIS 8-day composite data and 8-day averages of modeled incoming PPFD (photosynthetic photon flux density). Time series of EVI2 (two-band enhanced vegetation index) were calculated from MODIS 500m reflectance data...... and smoothed by a curve fitting procedure. For most sites, GPP was fairly strongly to strongly related to the product of EVI2 and PPFD (Deciduous: R2=0.45–0.86, Coniferous: R2=0.49–0.90). Similar strengths were found between GPP and the product of EVI2 and MODIS 1km daytime LST (land surface temperature) (R2...

18. Global Population Density Grid Time Series Estimates

Data.gov (United States)

National Aeronautics and Space Administration — Global Population Density Grid Time Series Estimates provide a back-cast time series of population density grids based on the year 2000 population grid from SEDAC's...

19. Global Population Count Grid Time Series Estimates

Data.gov (United States)

National Aeronautics and Space Administration — Global Population Count Grid Time Series Estimates provide a back-cast time series of population grids based on the year 2000 population grid from SEDAC's Global...

20. Introduction to time series and forecasting

CERN Document Server

Brockwell, Peter J

2016-01-01

This book is aimed at the reader who wishes to gain a working knowledge of time series and forecasting methods as applied to economics, engineering and the natural and social sciences. It assumes knowledge only of basic calculus, matrix algebra and elementary statistics. This third edition contains detailed instructions for the use of the professional version of the Windows-based computer package ITSM2000, now available as a free download from the Springer Extras website. The logic and tools of time series model-building are developed in detail. Numerous exercises are included and the software can be used to analyze and forecast data sets of the user's own choosing. The book can also be used in conjunction with other time series packages such as those included in R. The programs in ITSM2000 however are menu-driven and can be used with minimal investment of time in the computational details. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space mod...

1. Forecasting autoregressive time series under changing persistence

DEFF Research Database (Denmark)

Kruse, Robinson

Changing persistence in time series models means that a structural change from nonstationarity to stationarity or vice versa occurs over time. Such a change has important implications for forecasting, as negligence may lead to inaccurate model predictions. This paper derives generally applicable...... recommendations, no matter whether a change in persistence occurs or not. Seven different forecasting strategies based on a biasedcorrected estimator are compared by means of a large-scale Monte Carlo study. The results for decreasing and increasing persistence are highly asymmetric and new to the literature. Its...

2. Forecasting of time series with trend and seasonal cycle using the airline model and artiﬁcial neural networks Pronóstico de series de tiempo con tendencia y ciclo estacional usando el modelo airline y redes neuronales artificiales

Directory of Open Access Journals (Sweden)

J D Velásquez

2012-06-01

Full Text Available Many time series with trend and seasonal pattern are successfully modeled and forecasted by the airline model of Box and Jenkins; however, this model neglects the presence of nonlinearity on data. In this paper, we propose a new nonlinear version of the airline model; for this, we replace the moving average linear component by a multilayer perceptron neural network. The proposedmodel is used for forecasting two benchmark time series; we found that theproposed model is able to forecast the time series with more accuracy that other traditional approaches.Muchas series de tiempo con tendencia y ciclos estacionales son exitosamente modeladas y pronosticadas usando el modelo airline de Box y Jenkins; sin embargo, la presencia de no linealidades en los datos son despreciadas por este modelo. En este artículo, se propone una nueva versión no lineal del modelo airline; para esto, se reemplaza la componente lineal de promedios móviles por un perceptrón multicapa. El modelo propuesto es usado para pronosticar dos series de tiempo benchmark; se encontró que el modelo propuesto es capaz de pronosticar las series de tiempo con mayor precisión que otras aproximaciones tradicionales.

3. Markov Trends in Macroeconomic Time Series

OpenAIRE

Paap, Richard

1997-01-01

textabstractMany macroeconomic time series are characterised by long periods of positive growth, expansion periods, and short periods of negative growth, recessions. A popular model to describe this phenomenon is the Markov trend, which is a stochastic segmented trend where the slope depends on the value of an unobserved two-state first-order Markov process. The two slopes of the Markov trend describe the growth rates in the two phases of the business cycle. This thesis deals with a Bayesian ...

4. TIME SERIES CHARACTERISTIC ANALYSIS OF RAINFALL, LAND USE AND FLOOD DISCHARGE BASED ON ARIMA BOX-JENKINS MODEL

Directory of Open Access Journals (Sweden)

Abror Abror

2014-01-01

Full Text Available Indonesia located in tropic area consists of wet season and dry season. However, in last few years, in river discharge in dry season is very little, but in contrary, in wet season, frequency of flood increases with sharp peak and increasingly great water elevation. The increased flood discharge may occur due to change in land use or change in rainfall characteristic. Both matters should get clarity. Therefore, a research should be done to analyze rainfall characteristic, land use and flood discharge in some watershed area (DAS quantitatively from time series data. The research was conducted in DAS Gintung in Parakankidang, DAS Gung in Danawarih, DAS Rambut in Cipero, DAS Kemiri in Sidapurna and DAS Comal in Nambo, located in Tegal Regency and Pemalang Regency in Central Java Province. This research activity consisted of three main steps: input, DAS system and output. Input is DAS determination and selection and searching secondary data. DAS system is early secondary data processing consisting of rainfall analysis, HSS GAMA I parameter, land type analysis and DAS land use. Output is final processing step that consisting of calculation of Tadashi Tanimoto, USSCS effective rainfall, flood discharge, ARIMA analysis, result analysis and conclusion. Analytical calculation of ARIMA Box-Jenkins time series used software Number Cruncher Statistical Systems and Power Analysis Sample Size (NCSS-PASS version 2000, which result in time series characteristic in form of time series pattern, mean square errors (MSE, root mean square ( RMS, autocorrelation of residual and trend. Result of this research indicates that composite CN and flood discharge is proportional that means when composite CN trend increase then flood discharge trend also increase and vice versa. Meanwhile, decrease of rainfall trend is not always followed with decrease in flood discharge trend. The main cause of flood discharge characteristic is DAS management characteristic, not change in

5. A review of subsequence time series clustering.

Science.gov (United States)

Zolhavarieh, Seyedjamal; Aghabozorgi, Saeed; Teh, Ying Wah

2014-01-01

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.

6. Developing a local least-squares support vector machines-based neuro-fuzzy model for nonlinear and chaotic time series prediction.

Science.gov (United States)

2013-02-01

Local modeling approaches, owing to their ability to model different operating regimes of nonlinear systems and processes by independent local models, seem appealing for modeling, identification, and prediction applications. In this paper, we propose a local neuro-fuzzy (LNF) approach based on the least-squares support vector machines (LSSVMs). The proposed LNF approach employs LSSVMs, which are powerful in modeling and predicting time series, as local models and uses hierarchical binary tree (HBT) learning algorithm for fast and efficient estimation of its parameters. The HBT algorithm heuristically partitions the input space into smaller subdomains by axis-orthogonal splits. In each partitioning, the validity functions automatically form a unity partition and therefore normalization side effects, e.g., reactivation, are prevented. Integration of LSSVMs into the LNF network as local models, along with the HBT learning algorithm, yield a high-performance approach for modeling and prediction of complex nonlinear time series. The proposed approach is applied to modeling and predictions of different nonlinear and chaotic real-world and hand-designed systems and time series. Analysis of the prediction results and comparisons with recent and old studies demonstrate the promising performance of the proposed LNF approach with the HBT learning algorithm for modeling and prediction of nonlinear and chaotic systems and time series.

7. Modeling malaria control intervention effect in KwaZulu-Natal, South Africa using intervention time series analysis.

Science.gov (United States)

Ebhuoma, Osadolor; Gebreslasie, Michael; Magubane, Lethumusa

The change of the malaria control intervention policy in South Africa (SA), re-introduction of dichlorodiphenyltrichloroethane (DDT), may be responsible for the low and sustained malaria transmission in KwaZulu-Natal (KZN). We evaluated the effect of the re-introduction of DDT on malaria in KZN and suggested practical ways the province can strengthen her already existing malaria control and elimination efforts, to achieve zero malaria transmission. We obtained confirmed monthly malaria cases in KZN from the malaria control program of KZN from 1998 to 2014. The seasonal autoregressive integrated moving average (SARIMA) intervention time series analysis (ITSA) was employed to model the effect of the re-introduction of DDT on confirmed monthly malaria cases. The result is an abrupt and permanent decline of monthly malaria cases (w 0 =-1174.781, p-value=0.003) following the implementation of the intervention policy. The sustained low malaria cases observed over a long period suggests that the continued usage of DDT did not result in insecticide resistance as earlier anticipated. It may be due to exophagic malaria vectors, which renders the indoor residual spraying not totally effective. Therefore, the feasibility of reducing malaria transmission to zero in KZN requires other reliable and complementary intervention resources to optimize the existing ones. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

8. Research on Healthy Anomaly Detection Model Based on Deep Learning from Multiple Time-Series Physiological Signals

Directory of Open Access Journals (Sweden)

Kai Wang

2016-01-01

Full Text Available Health is vital to every human being. To further improve its already respectable medical technology, the medical community is transitioning towards a proactive approach which anticipates and mitigates risks before getting ill. This approach requires measuring the physiological signals of human and analyzes these data at regular intervals. In this paper, we present a novel approach to apply deep learning in physiological signals analysis that allows doctor to identify latent risks. However, extracting high level information from physiological time-series data is a hard problem faced by the machine learning communities. Therefore, in this approach, we apply model based on convolutional neural network that can automatically learn features from raw physiological signals in an unsupervised manner and then based on the learned features use multivariate Gauss distribution anomaly detection method to detect anomaly data. Our experiment is shown to have a significant performance in physiological signals anomaly detection. So it is a promising tool for doctor to identify early signs of illness even if the criteria are unknown a priori.

9. Nonlinear time series analysis with R

CERN Document Server

Huffaker, Ray; Rosa, Rodolfo

2017-01-01

In the process of data analysis, the investigator is often facing highly-volatile and random-appearing observed data. A vast body of literature shows that the assumption of underlying stochastic processes was not necessarily representing the nature of the processes under investigation and, when other tools were used, deterministic features emerged. Non Linear Time Series Analysis (NLTS) allows researchers to test whether observed volatility conceals systematic non linear behavior, and to rigorously characterize governing dynamics. Behavioral patterns detected by non linear time series analysis, along with scientific principles and other expert information, guide the specification of mechanistic models that serve to explain real-world behavior rather than merely reproducing it. Often there is a misconception regarding the complexity of the level of mathematics needed to understand and utilize the tools of NLTS (for instance Chaos theory). However, mathematics used in NLTS is much simpler than many other subjec...

10. Visibility graphlet approach to chaotic time series

Energy Technology Data Exchange (ETDEWEB)

Mutua, Stephen [Business School, University of Shanghai for Science and Technology, Shanghai 200093 (China); Computer Science Department, Masinde Muliro University of Science and Technology, P.O. Box 190-50100, Kakamega (Kenya); Gu, Changgui, E-mail: gu-changgui@163.com, E-mail: hjyang@ustc.edu.cn; Yang, Huijie, E-mail: gu-changgui@163.com, E-mail: hjyang@ustc.edu.cn [Business School, University of Shanghai for Science and Technology, Shanghai 200093 (China)

2016-05-15

Many novel methods have been proposed for mapping time series into complex networks. Although some dynamical behaviors can be effectively captured by existing approaches, the preservation and tracking of the temporal behaviors of a chaotic system remains an open problem. In this work, we extended the visibility graphlet approach to investigate both discrete and continuous chaotic time series. We applied visibility graphlets to capture the reconstructed local states, so that each is treated as a node and tracked downstream to create a temporal chain link. Our empirical findings show that the approach accurately captures the dynamical properties of chaotic systems. Networks constructed from periodic dynamic phases all converge to regular networks and to unique network structures for each model in the chaotic zones. Furthermore, our results show that the characterization of chaotic and non-chaotic zones in the Lorenz system corresponds to the maximal Lyapunov exponent, thus providing a simple and straightforward way to analyze chaotic systems.

11. The Simulation of Daily Temperature Time Series from GCM Output. Part I: Comparison of Model Data with Observations.

Science.gov (United States)

Palutikof, J. P.; Winkler, J. A.; Goodess, C. M.; Andresen, J. A.

1997-10-01

For climate change impact analyses, local scenarios of surface variables at the daily scales are frequently required. Empirical transfer functions are a widely used technique to generate scenarios from GCM data at these scales. For successful downscaling, the impact analyst should take into account certain considerations. First, it must be demonstrated that the GCM simulations of the required variable are unrealistic and therefore that downscaling is required. Second, it must be shown that the GCM simulations of the selected predictor variables are realistic. Where errors occur, attempts must be made to compensate for their effect on the transfer function-generated predictions or, where this is not possible, the effect on the transfer function-generated climate series must be understood. Third, the changes in the predictors between the control and perturbed simulation must be examined in the light of the implications for the change in the predicted variable. Finally, the effect of decisions made during the development of the transfer functions on the final result should be explored. This study, presented in two parts, addresses these considerations with respect to the development of local scenarios for daily maximum (TMAX) and minimum (TMIN) temperature for two sites, one in North America (Eau Claire, Michigan) and one in Europe (Alcantarilla, Spain).Part I confirms for a selected GCM that simulations of daily TMAX and TMIN, whether taken from the nearest land grid point, or obtained by interpolation to the site location, are inadequate. Differences between the GCM 1 × CO2 and observed temperature series arise because of a 0°C threshold in the model data. At both sites, variability is suppressed during periods affected by the threshold. The thresholds persist into the perturbed simulation, affecting not only GCM-predicted 2 × CO2 temperatures but also, because the duration and timing of the threshold effect changes in the perturbed simulation, the magnitude and

12. Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches

Science.gov (United States)

Scott L. Powell; Warren B. Cohen; Sean P. Healey; Robert E. Kennedy; Gretchen G. Moisen; Kenneth B. Pierce; Janet L. Ohmann

2010-01-01

Spatially and temporally explicit knowledge of biomass dynamics at broad scales is critical to understanding how forest disturbance and regrowth processes influence carbon dynamics. We modeled live, aboveground tree biomass using Forest Inventory and Analysis (FIA) field data and applied the models to 20+ year time-series of Landsat satellite imagery to...

13. Advantage of make-to-stock strategy based on linear mixed-effect model: a comparison with regression, autoregressive, times series, and exponential smoothing models

Directory of Open Access Journals (Sweden)

Yu-Pin Liao

2017-11-01

Full Text Available In the past few decades, demand forecasting has become relatively difficult due to rapid changes in the global environment. This research illustrates the use of the make-to-stock (MTS production strategy in order to explain how forecasting plays an essential role in business management. The linear mixed-effect (LME model has been extensively developed and is widely applied in various fields. However, no study has used the LME model for business forecasting. We suggest that the LME model be used as a tool for prediction and to overcome environment complexity. The data analysis is based on real data in an international display company, where the company needs accurate demand forecasting before adopting a MTS strategy. The forecasting result from the LME model is compared to the commonly used approaches, including the regression model, autoregressive model, times series model, and exponential smoothing model, with the results revealing that prediction performance provided by the LME model is more stable than using the other methods. Furthermore, product types in the data are regarded as a random effect in the LME model, hence demands of all types can be predicted simultaneously using a single LME model. However, some approaches require splitting the data into different type categories, and then predicting the type demand by establishing a model for each type. This feature also demonstrates the practicability of the LME model in real business operations.

14. Investigation of Relationship Between Hydrologic Processes of Precipitation, Evaporation and Stream Flow Using Linear Time Series Models (Case study: Western Basins of Lake Urmia

Directory of Open Access Journals (Sweden)

M. Moravej

2016-02-01

Full Text Available Introduction: Studying the hydrological cycle, especially in large scales such as water catchments, is difficult and complicated despite the fact that the numbers of hydrological components are limited. This complexity rises from complex interactions between hydrological components and environment. Recognition, determination and modeling of all interactive processes are needed to address this issue, but it's not feasible for dealing with practical engineering problems. So, it is more convenient to consider hydrological components as stochastic phenomenon, and use stochastic models for modeling them. Stochastic simulation of time series models related to water resources, particularly hydrologic time series, have been widely used in recent decades in order to solve issues pertaining planning and management of water resource systems. In this study time series models fitted to the precipitation, evaporation and stream flow series separately and the relationships between stream flow and precipitation processes are investigated. In fact, the three mentioned processes should be modeled in parallel to each other in order to acquire a comprehensive vision of hydrological conditions in the region. Moreover, the relationship between the hydrologic processes has been mostly studied with respect to their trends. It is desirable to investigate the relationship between trends of hydrological processes and climate change, while the relationship of the models has not been taken into consideration. The main objective of this study is to investigate the relationship between hydrological processes and their effects on each other and the selected models. Material and Method: In the current study, the four sub-basins of Lake Urmia Basin namely Zolachay (A, Nazloochay (B, Shahrchay (C and Barandoozchay (D were considered. Precipitation, evaporation and stream flow time series were modeled by linear time series. Fundamental assumptions of time series analysis namely

15. Modelling effects of forest disturbance history on carbon balance: a deep learning approach using Landsat-time series.

Science.gov (United States)

Besnard, S.; Carvalhais, N.; Clevers, J.; Dutrieux, L.; Gans, F.; Herold, M.; Reichstein, M.; Jung, M.

2017-12-01

Forests play a crucial role in the global carbon (C) cycle, covering about 30% of the planet's terrestrial surface, accounting for 50% of plant productivity, and storing 45% of all terrestrial C. As such, forest disturbances affect the balance of terrestrial C dioxide (CO 2 ) exchange, with the potential of releasing large amounts of C into the atmosphere. Understanding and quantifying the effect of forest disturbance on terrestrial C metabolism is critical for improving forest C balance estimates and predictions. Here we combine remote sensing, climate, and eddy-covariance (EC) data to study forest land surface-atmosphere C fluxes at more than 180 sites globally. We aim to enhance understanding of C balance in forest ecosystems by capturing the ecological carry-over effect of disturbance historyon C fluxes. Our objectives are to (1) characterize forest disturbance history through the full temporal depth of the Landsat time series (LTS); and (2) to investigate lag and carry-over effects of forest dynamics and climate on ecosystem C fluxes using a data-driven recurrent neural network(RNN). The resulting data-driven model integrates carry-over effects of the system, using LTS, ecosystem productivity, and several abiotic factors. In this study, we show that our RNN algorithm is able to effectively calculate realistic seasonal, interannual, and across-site C flux variabilities based on EC, LTS, and climate data. In addition, our results demonstrate that a deep learning approach with embedded dynamic memory effects offorest dynamics is able to better capture lag and carry-over effects due to soil-vegetation feedback compared to a classic approach considering only the current condition of the ecosystem. Our study paves the way to produce accurate, high resolution carbon fluxes maps, providing morecomprehensive monitoring, mapping, and reporting of the carbon consequences of forest change globally.

16. Stochastic time series analysis of hydrology data for water resources

Science.gov (United States)

Sathish, S.; Khadar Babu, S. K.

2017-11-01

The prediction to current publication of stochastic time series analysis in hydrology and seasonal stage. The different statistical tests for predicting the hydrology time series on Thomas-Fiering model. The hydrology time series of flood flow have accept a great deal of consideration worldwide. The concentration of stochastic process areas of time series analysis method are expanding with develop concerns about seasonal periods and global warming. The recent trend by the researchers for testing seasonal periods in the hydrologic flowseries using stochastic process on Thomas-Fiering model. The present article proposed to predict the seasonal periods in hydrology using Thomas-Fiering model.

17. Capturing Structure Implicitly from Time-Series having Limited Data

OpenAIRE

Emaasit, Daniel; Johnson, Matthew

2018-01-01

Scientific fields such as insider-threat detection and highway-safety planning often lack sufficient amounts of time-series data to estimate statistical models for the purpose of scientific discovery. Moreover, the available limited data are quite noisy. This presents a major challenge when estimating time-series models that are robust to overfitting and have well-calibrated uncertainty estimates. Most of the current literature in these fields involve visualizing the time-series for noticeabl...

18. Trend Filtering Techniques for Time Series Analysis

OpenAIRE

López Arias, Daniel

2016-01-01

Time series can be found almost everywhere in our lives and because of this being capable of analysing them is an important task. Most of the time series we can think of are quite noisy, being this one of the main problems to extract information from them. In this work we use Trend Filtering techniques to try to remove this noise from a series and understand the underlying trend of the series, that gives us information about the behaviour of the series aside from the particular...

19. Stochastic simulation of time-series models combined with geostatistics to predict water-table scenarios in a Guarani Aquifer System outcrop area, Brazil

Science.gov (United States)

Manzione, Rodrigo L.; Wendland, Edson; Tanikawa, Diego H.

2012-11-01

Stochastic methods based on time-series modeling combined with geostatistics can be useful tools to describe the variability of water-table levels in time and space and to account for uncertainty. Monitoring water-level networks can give information about the dynamic of the aquifer domain in both dimensions. Time-series modeling is an elegant way to treat monitoring data without the complexity of physical mechanistic models. Time-series model predictions can be interpolated spatially, with the spatial differences in water-table dynamics determined by the spatial variation in the system properties and the temporal variation driven by the dynamics of the inputs into the system. An integration of stochastic methods is presented, based on time-series modeling and geostatistics as a framework to predict water levels for decision making in groundwater management and land-use planning. The methodology is applied in a case study in a Guarani Aquifer System (GAS) outcrop area located in the southeastern part of Brazil. Communication of results in a clear and understandable form, via simulated scenarios, is discussed as an alternative, when translating scientific knowledge into applications of stochastic hydrogeology in large aquifers with limited monitoring network coverage like the GAS.

20. Modelling conditional heteroscedasticity in nonstationary series

NARCIS (Netherlands)

Cizek, P.; Cizek, P.; Härdle, W.K.; Weron, R.

2011-01-01

A vast amount of econometrical and statistical research deals with modeling financial time series and their volatility, which measures the dispersion of a series at a point in time (i.e., conditional variance). Although financial markets have been experiencing many shorter and longer periods of

1. Application of semi parametric modelling to times series forecasting: case of the electricity consumption; Modeles semi-parametriques appliques a la prevision des series temporelles. Cas de la consommation d'electricite

Energy Technology Data Exchange (ETDEWEB)

Lefieux, V

2007-10-15

Reseau de Transport d'Electricite (RTE), in charge of operating the French electric transportation grid, needs an accurate forecast of the power consumption in order to operate it correctly. The forecasts used everyday result from a model combining a nonlinear parametric regression and a SARIMA model. In order to obtain an adaptive forecasting model, nonparametric forecasting methods have already been tested without real success. In particular, it is known that a nonparametric predictor behaves badly with a great number of explanatory variables, what is commonly called the curse of dimensionality. Recently, semi parametric methods which improve the pure nonparametric approach have been proposed to estimate a regression function. Based on the concept of 'dimension reduction', one those methods (called MAVE : Moving Average -conditional- Variance Estimate) can apply to time series. We study empirically its effectiveness to predict the future values of an autoregressive time series. We then adapt this method, from a practical point of view, to forecast power consumption. We propose a partially linear semi parametric model, based on the MAVE method, which allows to take into account simultaneously the autoregressive aspect of the problem and the exogenous variables. The proposed estimation procedure is practically efficient. (author)

2. Multi variate regression model of the water level and production rate time series of the geothermal reservoir Waiwera (New Zealand)

Science.gov (United States)

Kühn, Michael; Schöne, Tim

2017-04-01

Water management tools are essential to ensure the conservation of natural resources. The geothermal hot water reservoir below the village of Waiwera, on the Northern Island of New Zealand is used commercially since 1863. The continuous production of 50 °C hot geothermal water, to supply hotels and spas, has a negative impact on the reservoir. Until the year 1969 from all wells drilled the warm water flow was artesian. Due to overproduction the water needs to be pumped up nowadays. Further, within the years 1975 to 1976 the warm water seeps on the beach of Waiwera ran dry. In order to protect the reservoir and the historical and tourist site in the early 1980s a water management plan was deployed. The "Auckland Council" established guidelines to enable a sustainable management of the resource [1]. The management plan demands that the water level in the official and appropriate observation well of the council is 0.5 m above sea level throughout the year in average. Almost four decades of data (since 1978 until today) are now available [2]. For a sustainable water management, it is necessary to be able to forecast the water level as a function of the production rates in the production wells. The best predictions are provided by a multivariate regression model of the water level and production rate time series, which takes into account the production rates of individual wells. It is based on the inversely proportional relationship between the independent variable (production rate) and the dependent variable (measured water level). In production scenarios, a maximum total production rate of approx. 1,100 m3 / day is determined in order to comply with the guidelines of the "Auckland Council". [1] Kühn M., Stöfen H. (2005) A reactive flow model of the geothermal reservoir Waiwera, New Zealand. Hydrogeology Journal 13, 606-626, doi: 10.1007/s10040-004-0377-6 [2] Kühn M., Altmannsberger C. (2016) Assessment of data driven and process based water management tools for

3. A model and diagnostic measures for response time series on tests of concentration: Historical background, conceptual framework, and some applications

NARCIS (Netherlands)

Breukelen, G.J.P. van; Roskam, E.E.C.I.; Eling, P.A.T.M.; Jansen, R.W.T.L.; Souren, D.A.P.B.; Ickenroth, J.G.M.

1995-01-01

Based upon classical hypotheses about accumulating mental fatigue and distraction and its effect on response times, put forward in late 19th and early 20th century papers, a mathematical model is proposed for response times on tests of speed and concentration. The model assumes the random occurrence

4. Analysis of Heavy-Tailed Time Series

DEFF Research Database (Denmark)

Xie, Xiaolei

and expressed in terms of the parameters of the dependence structure, among others. Furthermore, we study an importance sampling method for estimating rare-event probabilities of multivariate heavy-tailed time series generated by matrix recursion. We show that the proposed algorithm is efficient in the sense......This thesis is about analysis of heavy-tailed time series. We discuss tail properties of real-world equity return series and investigate the possibility that a single tail index is shared by all return series of actively traded equities in a market. Conditions for this hypothesis to be true...... are identified. We study the eigenvalues and eigenvectors of sample covariance and sample auto-covariance matrices of multivariate heavy-tailed time series, and particularly for time series with very high dimensions. Asymptotic approximations of the eigenvalues and eigenvectors of such matrices are found...

5. Inferring causality from noisy time series data

DEFF Research Database (Denmark)

Mønster, Dan; Fusaroli, Riccardo; Tylén, Kristian

2016-01-01

Convergent Cross-Mapping (CCM) has shown high potential to perform causal inference in the absence of models. We assess the strengths and weaknesses of the method by varying coupling strength and noise levels in coupled logistic maps. We find that CCM fails to infer accurate coupling strength...... and even causality direction in synchronized time-series and in the presence of intermediate coupling. We find that the presence of noise deterministically reduces the level of cross-mapping fidelity, while the convergence rate exhibits higher levels of robustness. Finally, we propose that controlled noise...

6. Time series prediction of apple scab using meteorological ...

African Journals Online (AJOL)

A new prediction model for the early warning of apple scab is proposed in this study. The method is based on artificial intelligence and time series prediction. The infection period of apple scab was evaluated as the time series prediction model instead of summation of wetness duration. Also, the relations of different ...

7. Anomaly on Superspace of Time Series Data

Science.gov (United States)

Capozziello, Salvatore; Pincak, Richard; Kanjamapornkul, Kabin

2017-11-01

We apply the G-theory and anomaly of ghost and antighost fields in the theory of supersymmetry to study a superspace over time series data for the detection of hidden general supply and demand equilibrium in the financial market. We provide proof of the existence of a general equilibrium point over 14 extradimensions of the new G-theory compared with the M-theory of the 11 dimensions model of Edward Witten. We found that the process of coupling between nonequilibrium and equilibrium spinor fields of expectation ghost fields in the superspace of time series data induces an infinitely long exact sequence of cohomology from a short exact sequence of moduli state space model. If we assume that the financial market is separated into two topological spaces of supply and demand as the D-brane and anti-D-brane model, then we can use a cohomology group to compute the stability of the market as a stable point of the general equilibrium of the interaction between D-branes of the market. We obtain the result that the general equilibrium will exist if and only if the 14th Batalin-Vilkovisky cohomology group with the negative dimensions underlying 14 major hidden factors influencing the market is zero.

8. The foundations of modern time series analysis

CERN Document Server

Mills, Terence C

2011-01-01

This book develops the analysis of Time Series from its formal beginnings in the 1890s through to the publication of Box and Jenkins' watershed publication in 1970, showing how these methods laid the foundations for the modern techniques of Time Series analysis that are in use today.

9. A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models.

Science.gov (United States)

BackgroundExposure measurement error in copollutant epidemiologic models has the potential to introduce bias in relative risk (RR) estimates. A simulation study was conducted using empirical data to quantify the impact of correlated measurement errors in time-series analyses of a...

10. Beyond long memory in heart rate variability: An approach based on fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity

Science.gov (United States)

Leite, Argentina; Paula Rocha, Ana; Eduarda Silva, Maria

2013-06-01

Heart Rate Variability (HRV) series exhibit long memory and time-varying conditional variance. This work considers the Fractionally Integrated AutoRegressive Moving Average (ARFIMA) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors. ARFIMA-GARCH models may be used to capture and remove long memory and estimate the conditional volatility in 24 h HRV recordings. The ARFIMA-GARCH approach is applied to fifteen long term HRV series available at Physionet, leading to the discrimination among normal individuals, heart failure patients, and patients with atrial fibrillation.

11. Analysis of JET ELMy time series

International Nuclear Information System (INIS)

Zvejnieks, G.; Kuzovkov, V.N.

2005-01-01

Full text: Achievement of the planned operational regime in the next generation tokamaks (such as ITER) still faces principal problems. One of the main challenges is obtaining the control of edge localized modes (ELMs), which should lead to both long plasma pulse times and reasonable divertor life time. In order to control ELMs the hypothesis was proposed by Degeling [1] that ELMs exhibit features of chaotic dynamics and thus a standard chaos control methods might be applicable. However, our findings which are based on the nonlinear autoregressive (NAR) model contradict this hypothesis for JET ELMy time-series. In turn, it means that ELM behavior is of a relaxation or random type. These conclusions coincide with our previous results obtained for ASDEX Upgrade time series [2]. [1] A.W. Degeling, Y.R. Martin, P.E. Bak, J. B.Lister, and X. Llobet, Plasma Phys. Control. Fusion 43, 1671 (2001). [2] G. Zvejnieks, V.N. Kuzovkov, O. Dumbrajs, A.W. Degeling, W. Suttrop, H. Urano, and H. Zohm, Physics of Plasmas 11, 5658 (2004)

12. Time-frequency analysis of econometric time series

Science.gov (United States)

Corinaldi, Sharif; Cohen, Leon

2007-06-01

We review the basic concepts of time-frequency analysis which are methods that indicate not only that which frequencies in a time series but also when they existed. A number of examples are given to illustrate the possible use of these methods to econometric series. The methods are applied to the Beveridge Wheat Price Series.

13. Model-based deconvolution of cell cycle time-series data reveals gene expression details at high resolution.

Directory of Open Access Journals (Sweden)

2009-08-01

Full Text Available In both prokaryotic and eukaryotic cells, gene expression is regulated across the cell cycle to ensure "just-in-time" assembly of select cellular structures and molecular machines. However, present in all time-series gene expression measurements is variability that arises from both systematic error in the cell synchrony process and variance in the timing of cell division at the level of the single cell. Thus, gene or protein expression data collected from a population of synchronized cells is an inaccurate measure of what occurs in the average single-cell across a cell cycle. Here, we present a general computational method to extract "single-cell"-like information from population-level time-series expression data. This method removes the effects of 1 variance in growth rate and 2 variance in the physiological and developmental state of the cell. Moreover, this method represents an advance in the deconvolution of molecular expression data in its flexibility, minimal assumptions, and the use of a cross-validation analysis to determine the appropriate level of regularization. Applying our deconvolution algorithm to cell cycle gene expression data from the dimorphic bacterium Caulobacter crescentus, we recovered critical features of cell cycle regulation in essential genes, including ctrA and ftsZ, that were obscured in population-based measurements. In doing so, we highlight the problem with using population data alone to decipher cellular regulatory mechanisms and demonstrate how our deconvolution algorithm can be applied to produce a more realistic picture of temporal regulation in a cell.

14. Entropic Analysis of Electromyography Time Series

Science.gov (United States)

Kaufman, Miron; Sung, Paul

2005-03-01

We are in the process of assessing the effectiveness of fractal and entropic measures for the diagnostic of low back pain from surface electromyography (EMG) time series. Surface electromyography (EMG) is used to assess patients with low back pain. In a typical EMG measurement, the voltage is measured every millisecond. We observed back muscle fatiguing during one minute, which results in a time series with 60,000 entries. We characterize the complexity of time series by computing the Shannon entropy time dependence. The analysis of the time series from different relevant muscles from healthy and low back pain (LBP) individuals provides evidence that the level of variability of back muscle activities is much larger for healthy individuals than for individuals with LBP. In general the time dependence of the entropy shows a crossover from a diffusive regime to a regime characterized by long time correlations (self organization) at about 0.01s.

15. Track Irregularity Time Series Analysis and Trend Forecasting

OpenAIRE

Jia Chaolong; Xu Weixiang; Wang Futian; Wang Hanning

2012-01-01

The combination of linear and nonlinear methods is widely used in the prediction of time series data. This paper analyzes track irregularity time series data by using gray incidence degree models and methods of data transformation, trying to find the connotative relationship between the time series data. In this paper, GM (1,1) is based on first-order, single variable linear differential equations; after an adaptive improvement and error correction, it is used to predict the long-term changin...

16. Time series analysis of temporal networks

Science.gov (United States)

Sikdar, Sandipan; Ganguly, Niloy; Mukherjee, Animesh

2016-01-01

A common but an important feature of all real-world networks is that they are temporal in nature, i.e., the network structure changes over time. Due to this dynamic nature, it becomes difficult to propose suitable growth models that can explain the various important characteristic properties of these networks. In fact, in many application oriented studies only knowing these properties is sufficient. For instance, if one wishes to launch a targeted attack on a network, this can be done even without the knowledge of the full network structure; rather an estimate of some of the properties is sufficient enough to launch the attack. We, in this paper show that even if the network structure at a future time point is not available one can still manage to estimate its properties. We propose a novel method to map a temporal network to a set of time series instances, analyze them and using a standard forecast model of time series, try to predict the properties of a temporal network at a later time instance. To our aim, we consider eight properties such as number of active nodes, average degree, clustering coefficient etc. and apply our prediction framework on them. We mainly focus on the temporal network of human face-to-face contacts and observe that it represents a stochastic process with memory that can be modeled as Auto-Regressive-Integrated-Moving-Average (ARIMA). We use cross validation techniques to find the percentage accuracy of our predictions. An important observation is that the frequency domain properties of the time series obtained from spectrogram analysis could be used to refine the prediction framework by identifying beforehand the cases where the error in prediction is likely to be high. This leads to an improvement of 7.96% (for error level ≤20%) in prediction accuracy on an average across all datasets. As an application we show how such prediction scheme can be used to launch targeted attacks on temporal networks. Contribution to the Topical Issue

17. Studies on time series applications in environmental sciences

CERN Document Server

Bărbulescu, Alina

2016-01-01

Time series analysis and modelling represent a large study field, implying the approach from the perspective of the time and frequency, with applications in different domains. Modelling hydro-meteorological time series is difficult due to the characteristics of these series, as long range dependence, spatial dependence, the correlation with other series. Continuous spatial data plays an important role in planning, risk assessment and decision making in environmental management. In this context, in this book we present various statistical tests and modelling techniques used for time series analysis, as well as applications to hydro-meteorological series from Dobrogea, a region situated in the south-eastern part of Romania, less studied till now. Part of the results are accompanied by their R code. .

18. Automated time series forecasting for biosurveillance.

Science.gov (United States)

Burkom, Howard S; Murphy, Sean Patrick; Shmueli, Galit

2007-09-30

For robust detection performance, traditional control chart monitoring for biosurveillance is based on input data free of trends, day-of-week effects, and other systematic behaviour. Time series forecasting methods may be used to remove this behaviour by subtracting forecasts from observations to form residuals for algorithmic input. We describe three forecast methods and compare their predictive accuracy on each of 16 authentic syndromic data streams. The methods are (1) a non-adaptive regression model using a long historical baseline, (2) an adaptive regression model with a shorter, sliding baseline, and (3) the Holt-Winters method for generalized exponential smoothing. Criteria for comparing the forecasts were the root-mean-square error, the median absolute per cent error (MedAPE), and the median absolute deviation. The median-based criteria showed best overall performance for the Holt-Winters method. The MedAPE measures over the 16 test series averaged 16.5, 11.6, and 9.7 for the non-adaptive regression, adaptive regression, and Holt-Winters methods, respectively. The non-adaptive regression forecasts were degraded by changes in the data behaviour in the fixed baseline period used to compute model coefficients. The mean-based criterion was less conclusive because of the effects of poor forecasts on a small number of calendar holidays. The Holt-Winters method was also most effective at removing serial autocorrelation, with most 1-day-lag autocorrelation coefficients below 0.15. The forecast methods were compared without tuning them to the behaviour of individual series. We achieved improved predictions with such tuning of the Holt-Winters method, but practical use of such improvements for routine surveillance will require reliable data classification methods.

19. Visibility Graph Based Time Series Analysis.

Science.gov (United States)

Stephen, Mutua; Gu, Changgui; Yang, Huijie

2015-01-01

Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it's microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq) and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks.

20. Methods comparison by time series analysis

International Nuclear Information System (INIS)

Giovino, J.

1986-01-01

One role of the U.S. Environmental Protection Agency (EPA) is that of monitor for laboratories under contract to perform chemical analyses. In general this program involves periodic analyses and reporting of unknown radionuclides in water. This radiochemistry data for the years 1980-1984, has been summarized. It represents several radionuclides and various methods used by numerous laboratories. Any series of measurements taken at successive time points is a time series, and is thus candidate for time series analysis. The purpose of such an analysis is to see what changes take place over time in the event being observed, to see if the performance is better or worse than it was expected to be, and to predict future behavior. To illustrate the step-by-step process of a time series analysis, the radionuclide /sup 226/Ra was selected. The available data were generated by two methods; total radium alpha and /sup 222/Rn emanation. The results of analysis are presented

1. Data Mining Smart Energy Time Series

Directory of Open Access Journals (Sweden)

Janina POPEANGA

2015-07-01

Full Text Available With the advent of smart metering technology the amount of energy data will increase significantly and utilities industry will have to face another big challenge - to find relationships within time-series data and even more - to analyze such huge numbers of time series to find useful patterns and trends with fast or even real-time response. This study makes a small review of the literature in the field, trying to demonstrate how essential is the application of data mining techniques in the time series to make the best use of this large quantity of data, despite all the difficulties. Also, the most important Time Series Data Mining techniques are presented, highlighting their applicability in the energy domain.

2. Time series prediction: statistical and neural techniques

Science.gov (United States)

Zahirniak, Daniel R.; DeSimio, Martin P.

1996-03-01

In this paper we compare the performance of nonlinear neural network techniques to those of linear filtering techniques in the prediction of time series. Specifically, we compare the results of using the nonlinear systems, known as multilayer perceptron and radial basis function neural networks, with the results obtained using the conventional linear Wiener filter, Kalman filter and Widrow-Hoff adaptive filter in predicting future values of stationary and non- stationary time series. Our results indicate the performance of each type of system is heavily dependent upon the form of the time series being predicted and the size of the system used. In particular, the linear filters perform adequately for linear or near linear processes while the nonlinear systems perform better for nonlinear processes. Since the linear systems take much less time to be developed, they should be tried prior to using the nonlinear systems when the linearity properties of the time series process are unknown.

3. Time series analysis in the social sciences the fundamentals

CERN Document Server

Shin, Youseop

2017-01-01

Times Series Analysis in the Social Sciences is a practical and highly readable introduction written exclusively for students and researchers whose mathematical background is limited to basic algebra. The book focuses on fundamental elements of time series analysis that social scientists need to understand so they can employ time series analysis for their research and practice. Through step-by-step explanations and using monthly violent crime rates as case studies, this book explains univariate time series from the preliminary visual analysis through the modeling of seasonality, trends, and re

4. Time series forecasting based on deep extreme learning machine

NARCIS (Netherlands)

Guo, Xuqi; Pang, Y.; Yan, Gaowei; Qiao, Tiezhu; Yang, Guang-Hong; Yang, Dan

2017-01-01

Multi-layer Artificial Neural Networks (ANN) has caught widespread attention as a new method for time series forecasting due to the ability of approximating any nonlinear function. In this paper, a new local time series prediction model is established with the nearest neighbor domain theory, in

5. Two-fractal overlap time series: Earthquakes and market crashes

We find prominent similarities in the features of the time series for the (model earthquakes or) overlap of two Cantor sets when one set moves with uniform relative velocity over the other and time series of stock prices. An anticipation method for some of the crashes have been proposed here, based on these observations.

6. Measuring multiscaling in financial time-series

International Nuclear Information System (INIS)

Buonocore, R.J.; Aste, T.; Di Matteo, T.

2016-01-01

We discuss the origin of multiscaling in financial time-series and investigate how to best quantify it. Our methodology consists in separating the different sources of measured multifractality by analyzing the multi/uni-scaling behavior of synthetic time-series with known properties. We use the results from the synthetic time-series to interpret the measure of multifractality of real log-returns time-series. The main finding is that the aggregation horizon of the returns can introduce a strong bias effect on the measure of multifractality. This effect can become especially important when returns distributions have power law tails with exponents in the range (2, 5). We discuss the right aggregation horizon to mitigate this bias.

7. Detecting nonlinear structure in time series

International Nuclear Information System (INIS)

Theiler, J.

1991-01-01

We describe an approach for evaluating the statistical significance of evidence for nonlinearity in a time series. The formal application of our method requires the careful statement of a null hypothesis which characterizes a candidate linear process, the generation of an ensemble of ''surrogate'' data sets which are similar to the original time series but consistent with the null hypothesis, and the computation of a discriminating statistic for the original and for each of the surrogate data sets. The idea is to test the original time series against the null hypothesis by checking whether the discriminating statistic computed for the original time series differs significantly from the statistics computed for each of the surrogate sets. While some data sets very cleanly exhibit low-dimensional chaos, there are many cases where the evidence is sketchy and difficult to evaluate. We hope to provide a framework within which such claims of nonlinearity can be evaluated. 5 refs., 4 figs

8. Applied time series analysis and innovative computing

CERN Document Server

Ao, Sio-Iong

2010-01-01

This text is a systematic, state-of-the-art introduction to the use of innovative computing paradigms as an investigative tool for applications in time series analysis. It includes frontier case studies based on recent research.

9. DROP: Dimensionality Reduction Optimization for Time Series

OpenAIRE

Suri, Sahaana; Bailis, Peter

2017-01-01

Dimensionality reduction is critical in analyzing increasingly high-volume, high-dimensional time series. In this paper, we revisit a now-classic study of time series dimensionality reduction operators and find that for a given quality constraint, Principal Component Analysis (PCA) uncovers representations that are over 2x smaller than those obtained via alternative techniques favored in the literature. However, as classically implemented via Singular Value Decomposition (SVD), PCA is incredi...

10. Introduction to time series analysis and forecasting

CERN Document Server

Montgomery, Douglas C; Kulahci, Murat

2008-01-01

An accessible introduction to the most current thinking in and practicality of forecasting techniques in the context of time-oriented data. Analyzing time-oriented data and forecasting are among the most important problems that analysts face across many fields, ranging from finance and economics to production operations and the natural sciences. As a result, there is a widespread need for large groups of people in a variety of fields to understand the basic concepts of time series analysis and forecasting. Introduction to Time Series Analysis and Forecasting presents the time series analysis branch of applied statistics as the underlying methodology for developing practical forecasts, and it also bridges the gap between theory and practice by equipping readers with the tools needed to analyze time-oriented data and construct useful, short- to medium-term, statistically based forecasts.

11. A comparison of monthly precipitation point estimates at 6 locations in Iran using integration of soft computing methods and GARCH time series model

Science.gov (United States)

2017-11-01

Precipitation plays an important role in determining the climate of a region. Precise estimation of precipitation is required to manage and plan water resources, as well as other related applications such as hydrology, climatology, meteorology and agriculture. Time series of hydrologic variables such as precipitation are composed of deterministic and stochastic parts. Despite this fact, the stochastic part of the precipitation data is not usually considered in modeling of precipitation process. As an innovation, the present study introduces three new hybrid models by integrating soft computing methods including multivariate adaptive regression splines (MARS), Bayesian networks (BN) and gene expression programming (GEP) with a time series model, namely generalized autoregressive conditional heteroscedasticity (GARCH) for modeling of the monthly precipitation. For this purpose, the deterministic (obtained by soft computing methods) and stochastic (obtained by GARCH time series model) parts are combined with each other. To carry out this research, monthly precipitation data of Babolsar, Bandar Anzali, Gorgan, Ramsar, Tehran and Urmia stations with different climates in Iran were used during the period of 1965-2014. Root mean square error (RMSE), relative root mean square error (RRMSE), mean absolute error (MAE) and determination coefficient (R2) were employed to evaluate the performance of conventional/single MARS, BN and GEP, as well as the proposed MARS-GARCH, BN-GARCH and GEP-GARCH hybrid models. It was found that the proposed novel models are more precise than single MARS, BN and GEP models. Overall, MARS-GARCH and BN-GARCH models yielded better accuracy than GEP-GARCH. The results of the present study confirmed the suitability of proposed methodology for precise modeling of precipitation.

12. Normalizing the causality between time series

Science.gov (United States)

Liang, X. San

2015-08-01

Recently, a rigorous yet concise formula was derived to evaluate information flow, and hence the causality in a quantitative sense, between time series. To assess the importance of a resulting causality, it needs to be normalized. The normalization is achieved through distinguishing a Lyapunov exponent-like, one-dimensional phase-space stretching rate and a noise-to-signal ratio from the rate of information flow in the balance of the marginal entropy evolution of the flow recipient. It is verified with autoregressive models and applied to a real financial analysis problem. An unusually strong one-way causality is identified from IBM (International Business Machines Corporation) to GE (General Electric Company) in their early era, revealing to us an old story, which has almost faded into oblivion, about "Seven Dwarfs" competing with a giant for the mainframe computer market.

13. [Modelling the effect of local climatic variability on dengue transmission in Medellin (Colombia) by means of time series analysis].

Science.gov (United States)

Rúa-Uribe, Guillermo L; Suárez-Acosta, Carolina; Chauca, José; Ventosilla, Palmira; Almanza, Rita

2013-09-01

Dengue fever is a major impact on public health vector-borne disease, and its transmission is influenced by entomological, sociocultural and economic factors. Additionally, climate variability plays an important role in the transmission dynamics. A large scientific consensus has indicated that the strong association between climatic variables and disease could be used to develop models to explain the incidence of the disease. To develop a model that provides a better understanding of dengue transmission dynamics in Medellin and predicts increases in the incidence of the disease. The incidence of dengue fever was used as dependent variable, and weekly climatic factors (maximum, mean and minimum temperature, relative humidity and precipitation) as independent variables. Expert Modeler was used to develop a model to better explain the behavior of the disease. Climatic variables with significant association to the dependent variable were selected through ARIMA models. The model explains 34% of observed variability. Precipitation was the climatic variable showing statistically significant association with the incidence of dengue fever, but with a 20 weeks delay. In Medellin, the transmission of dengue fever was influenced by climate variability, especially precipitation. The strong association dengue fever/precipitation allowed the construction of a model to help understand dengue transmission dynamics. This information will be useful to develop appropriate and timely strategies for dengue control.

14. Long-Term Retrospective Analysis of Mackerel Spawning in the North Sea: A New Time Series and Modeling Approach to CPR Data

OpenAIRE

Jansen, Teunis; Kristensen, Kasper; Payne, Mark; Edwards, Martin; Schrum, Corinna; Pitois, Sophie

2012-01-01

We present a unique view of mackerel (Scomber scombrus) in the North Sea based on a new time series of larvae caught by the Continuous Plankton Recorder (CPR) survey from 1948-2005, covering the period both before and after the collapse of the North Sea stock. Hydrographic backtrack modelling suggested that the effect of advection is very limited between spawning and larvae capture in the CPR survey. Using a statistical technique not previously applied to CPR data, we then generated a larval ...

15. Estimate of the degree of synchronization in the intermittent phase synchronization regime from a time series (model systems and neurophysiological data)

Science.gov (United States)

Moskalenko, O. I.; Koronovskii, A. A.; Hramov, A. E.; Zhuravlev, M. O.

2016-04-01

A method for determining the degree of synchronization of intermittent phase synchronization regime from a time series has been proposed on the basis of estimating the zero conditional Lyapunov exponent. The efficiency of the method has been tested on model systems near the boundary of the appearance of the synchronous regime. The method has been used to determine the degree of synchronization between various regions of the brain of rats of the WAG/Rij line having a genetic predisposition to epilepsy.

16. Perbandingan Berbagai Model Conditionally Heteroscedastic Time Series Dalam Analisis Risiko Investasi Saham Syariah Dengan Metode Value At Risk

Directory of Open Access Journals (Sweden)

2013-04-01

Full Text Available Value at Risk (VaR is one of the tools recommended Bank Indonesia to gauge the risk of an investment, the VaR approach tends to be more associated with the conventional assumption of a normal distribution, while contemporary empirical findings indicate the existence of patterns of abnormality in the nature of statistical data, especially on financial data. Up to this time shares in the Jakarta Islamic Index (JII is still heavily influenced by the dynamics of market volatility which one, so the necessary in-depth analysis to help investors make the right decisions in investing. This research addresses the issue of risk analysis model using the VaR approach using a variety of model Heterokedastic Timeseris Conditionals (CHT and find the best model. As for the data used is the daily closing stock index data-Sharia stocks (JII post-crisis global 2008 (January 2009 – June 2011 and the software used is E-Views 5.1 and Excel 2007. The results obtained are of 16 (sixteen model approach to VaR-CHT used, only 5 (five a valid model on a confidence level of 99%, i.e. Approach (2.2 GARCH, GARCH M standard deviation GARCH (1,1, M Log (Variansi (1,1, TARCH M Log (Variansi (1,1, EGARCH and M Log (Variansi (1,1. The VaR Model of the CHT are the best and recommended in analyzing the risks of stock investment is Shariah (JII is a model that gives the value of the VaR model, i.e. the smallest VaR GARCH-M standard deviation (1,1 that gives the value of VaR is equal to 3.2396%.

17. Using SAR satellite data time series for regional glacier mapping

Directory of Open Access Journals (Sweden)

S. H. Winsvold

2018-03-01

Full Text Available With dense SAR satellite data time series it is possible to map surface and subsurface glacier properties that vary in time. On Sentinel-1A and RADARSAT-2 backscatter time series images over mainland Norway and Svalbard, we outline how to map glaciers using descriptive methods. We present five application scenarios. The first shows potential for tracking transient snow lines with SAR backscatter time series and correlates with both optical satellite images (Sentinel-2A and Landsat 8 and equilibrium line altitudes derived from in situ surface mass balance data. In the second application scenario, time series representation of glacier facies corresponding to SAR glacier zones shows potential for a more accurate delineation of the zones and how they change in time. The third application scenario investigates the firn evolution using dense SAR backscatter time series together with a coupled energy balance and multilayer firn model. We find strong correlation between backscatter signals with both the modeled firn air content and modeled wetness in the firn. In the fourth application scenario, we highlight how winter rain events can be detected in SAR time series, revealing important information about the area extent of internal accumulation. In the last application scenario, averaged summer SAR images were found to have potential in assisting the process of mapping glaciers outlines, especially in the presence of seasonal snow. Altogether we present examples of how to map glaciers and to further understand glaciological processes using the existing and future massive amount of multi-sensor time series data.

18. Using SAR satellite data time series for regional glacier mapping

Science.gov (United States)

Winsvold, Solveig H.; Kääb, Andreas; Nuth, Christopher; Andreassen, Liss M.; van Pelt, Ward J. J.; Schellenberger, Thomas

2018-03-01

With dense SAR satellite data time series it is possible to map surface and subsurface glacier properties that vary in time. On Sentinel-1A and RADARSAT-2 backscatter time series images over mainland Norway and Svalbard, we outline how to map glaciers using descriptive methods. We present five application scenarios. The first shows potential for tracking transient snow lines with SAR backscatter time series and correlates with both optical satellite images (Sentinel-2A and Landsat 8) and equilibrium line altitudes derived from in situ surface mass balance data. In the second application scenario, time series representation of glacier facies corresponding to SAR glacier zones shows potential for a more accurate delineation of the zones and how they change in time. The third application scenario investigates the firn evolution using dense SAR backscatter time series together with a coupled energy balance and multilayer firn model. We find strong correlation between backscatter signals with both the modeled firn air content and modeled wetness in the firn. In the fourth application scenario, we highlight how winter rain events can be detected in SAR time series, revealing important information about the area extent of internal accumulation. In the last application scenario, averaged summer SAR images were found to have potential in assisting the process of mapping glaciers outlines, especially in the presence of seasonal snow. Altogether we present examples of how to map glaciers and to further understand glaciological processes using the existing and future massive amount of multi-sensor time series data.

19. Efficient Approximate OLAP Querying Over Time Series

DEFF Research Database (Denmark)

Perera, Kasun Baruhupolage Don Kasun Sanjeewa; Hahmann, Martin; Lehner, Wolfgang

2016-01-01

The ongoing trend for data gathering not only produces larger volumes of data, but also increases the variety of recorded data types. Out of these, especially time series, e.g. various sensor readings, have attracted attention in the domains of business intelligence and decision making. As OLAP...... queries play a major role in these domains, it is desirable to also execute them on time series data. While this is not a problem on the conceptual level, it can become a bottleneck with regards to query run-time. In general, processing OLAP queries gets more computationally intensive as the volume...... are either costly or require continuous maintenance. In this paper we propose an approach for approximate OLAP querying of time series that offers constant latency and is maintenance-free. To achieve this, we identify similarities between aggregation cuboids and propose algorithms that eliminate...

20. Application of Genetic Programing to Develop a Modular Model for the Simulation of Stream Flow Time Series

Science.gov (United States)

Meshgi, A.; Babovic, V.; Chui, T. F. M.; Schmitter, P.

2014-12-01

Developing reliable methods to estimate stream flow has been a subject of interest due to its importance in planning, design and management of water resources within a basin. Machine learning tools such as Artificial Neural Network (ANN) and Genetic Programming (GP) have been widely applied for rainfall-runoff modeling as they require less computational time as compared to physically-based models. As GP is able to generate a function with understandable structure, it may offer advantages over other data driven techniques and therefore has been used in different studies to generate rainfall-runoff functions. However, to date, proposed formulations only contain rainfall and/or streamflow data and consequently are local and cannot be generalized and adopted in other catchments which have different physical characteristics. This study investigated the capability of GP in developing a physically interpretable model with understandable structure to simulate stream flow based on hydrological parameters (e.g. precipitation) and catchment conditions (e.g., initial groundwater table elevation and area of the catchment) by following a modular approach. The modular model resulted in two sub-models where the baseflow was first predicted and the direct runoff was then estimated for a semi-urban catchment in Singapore. The simulated results matched very well with observed data in both the training and the testing of data sets, giving NSEs of 0.97 and 0.96 respectively demonstrated the successful estimation of stream flow using the modular model derived in this study. The results of this study indicate that GP is an effective tool in developing a physically interpretable model with understandable structure to simulate stream flow that can be transferred to other catchments.

1. Relevance of long term time - Series of atmospheric parameters at a mountain observatory to models for climate change

Science.gov (United States)

Kancírová, M.; Kudela, K.; Erlykin, A. D.; Wolfendale, A. W.

2016-10-01

A detailed analysis has been made based on annual meteorological and cosmic ray data from the Lomnicky stit mountain observatory (LS, 2634 masl; 49.40°N, 20.22°E; vertical cut-off rigidity 3.85 GV), from the standpoint of looking for possible solar cycle (including cosmic ray) manifestations. A comparison of the mountain data with the Global average for the cloud cover in general shows no correlation but there is a possible small correlation for low clouds (LCC in the Global satellite data). However, whereas it cannot be claimed that cloud cover observed at Lomnicky stit (LSCC) can be used directly as a proxy for the Global LCC, its examination has value because it is an independent estimate of cloud cover and one that has a different altitude weighting to that adopted in the satellite-derived LCC. This statement is derived from satellite data (http://isccp.giss.nasa.gov/climanal7.html) which shows the time series for the period 1983-2010 for 9 cloud regimes. There is a significant correlation only between cosmic ray (CR) intensity (and sunspot number (SSN)) and the cloud cover of the types cirrus and stratus. This effect is mainly confined to the CR intensity minimum during the epoch around 1990, when the SSN was at its maximum. This fact, together with the present study of the correlation of LSCC with our measured CR intensity, shows that there is no firm evidence for a significant contribution of CR induced ionization to the local (or, indeed, Global) cloud cover. Pressure effects are the preferred cause of the cloud cover changes. A consequence is that there is no evidence favouring a contribution of CR to the Global Warming problem. Our analysis shows that the LS data are consistent with the Gas Laws for a stable mass of atmosphere.

2. Multivariate time series analysis with R and financial applications

CERN Document Server

Tsay, Ruey S

2013-01-01

Since the publication of his first book, Analysis of Financial Time Series, Ruey Tsay has become one of the most influential and prominent experts on the topic of time series. Different from the traditional and oftentimes complex approach to multivariate (MV) time series, this sequel book emphasizes structural specification, which results in simplified parsimonious VARMA modeling and, hence, eases comprehension. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-worl

3. Stacked Heterogeneous Neural Networks for Time Series Forecasting

Directory of Open Access Journals (Sweden)

Florin Leon

2010-01-01

Full Text Available A hybrid model for time series forecasting is proposed. It is a stacked neural network, containing one normal multilayer perceptron with bipolar sigmoid activation functions, and the other with an exponential activation function in the output layer. As shown by the case studies, the proposed stacked hybrid neural model performs well on a variety of benchmark time series. The combination of weights of the two stack components that leads to optimal performance is also studied.

4. Layered Ensemble Architecture for Time Series Forecasting.

Science.gov (United States)

Rahman, Md Mustafizur; Islam, Md Monirul; Murase, Kazuyuki; Yao, Xin

2016-01-01

Time series forecasting (TSF) has been widely used in many application areas such as science, engineering, and finance. The phenomena generating time series are usually unknown and information available for forecasting is only limited to the past values of the series. It is, therefore, necessary to use an appropriate number of past values, termed lag, for forecasting. This paper proposes a layered ensemble architecture (LEA) for TSF problems. Our LEA consists of two layers, each of which uses an ensemble of multilayer perceptron (MLP) networks. While the first ensemble layer tries to find an appropriate lag, the second ensemble layer employs the obtained lag for forecasting. Unlike most previous work on TSF, the proposed architecture considers both accuracy and diversity of the individual networks in constructing an ensemble. LEA trains different networks in the ensemble by using different training sets with an aim of maintaining diversity among the networks. However, it uses the appropriate lag and combines the best trained networks to construct the ensemble. This indicates LEAs emphasis on accuracy of the networks. The proposed architecture has been tested extensively on time series data of neural network (NN)3 and NN5 competitions. It has also been tested on several standard benchmark time series data. In terms of forecasting accuracy, our experimental results have revealed clearly that LEA is better than other ensemble and nonensemble methods.

5. Time series clustering in large data sets

Directory of Open Access Journals (Sweden)

Jiří Fejfar

2011-01-01

Full Text Available The clustering of time series is a widely researched area. There are many methods for dealing with this task. We are actually using the Self-organizing map (SOM with the unsupervised learning algorithm for clustering of time series. After the first experiment (Fejfar, Weinlichová, Šťastný, 2009 it seems that the whole concept of the clustering algorithm is correct but that we have to perform time series clustering on much larger dataset to obtain more accurate results and to find the correlation between configured parameters and results more precisely. The second requirement arose in a need for a well-defined evaluation of results. It seems useful to use sound recordings as instances of time series again. There are many recordings to use in digital libraries, many interesting features and patterns can be found in this area. We are searching for recordings with the similar development of information density in this experiment. It can be used for musical form investigation, cover songs detection and many others applications.The objective of the presented paper is to compare clustering results made with different parameters of feature vectors and the SOM itself. We are describing time series in a simplistic way evaluating standard deviations for separated parts of recordings. The resulting feature vectors are clustered with the SOM in batch training mode with different topologies varying from few neurons to large maps.There are other algorithms discussed, usable for finding similarities between time series and finally conclusions for further research are presented. We also present an overview of the related actual literature and projects.

6. Integrated approaches to miRNAs target definition: time-series analysis in an osteosarcoma differentiative model.

Science.gov (United States)

Grilli, A; Sciandra, M; Terracciano, M; Picci, P; Scotlandi, K

2015-06-30

microRNAs (miRs) are small non-coding RNAs involved in the fine regulation of several cellular processes by inhibiting their target genes at post-transcriptional level. Osteosarcoma (OS) is a tumor thought to be related to a molecular blockade of the normal process of osteoblast differentiation. The current paper explores temporal transcriptional modifications comparing an osteosarcoma cell line, Saos-2, and clones stably transfected with CD99, a molecule which was found to drive OS cells to terminally differentiate. Parental cell line and CD99 transfectants were cultured up to 14 days in differentiating medium. In this setting, OS cells were profiled by gene and miRNA expression arrays. Integration of gene and miRNA profiling was performed by both sequence complementarity and expression correlation. Further enrichment and network analyses were carried out to focus on the modulated pathways and on the interactions between transcriptome and miRNome. To track the temporal transcriptional modification, a PCA analysis with differentiated human MSC was performed. We identified a strong (about 80 %) gene down-modulation where reversion towards the osteoblast-like phenotype matches significant enrichment in TGFbeta signaling players like AKT1 and SMADs. In parallel, we observed the modulation of several cancer-related microRNAs like miR-34a, miR-26b or miR-378. To decipher their impact on the modified transcriptional program in CD99 cells, we correlated gene and microRNA time-series data miR-34a, in particular, was found to regulate a distinct subnetwork of genes with respect to the rest of the other differentially expressed miRs and it appeared to be the main mediator of several TGFbeta signaling genes at initial and middle phases of differentiation. Integration studies further highlighted the involvement of TGFbeta pathway in the differentiation of OS cells towards osteoblasts and its regulation by microRNAs. These data underline that the expression of miR-34a and down

7. Autoregressive Generalized Linear Mixed Effect Models with Crossed Random Effects: An Application to Intensive Binary Time Series Eye-Tracking Data.

Science.gov (United States)

Cho, Sun-Joo; Brown-Schmidt, Sarah; Lee, Woo-Yeol

2018-02-07

As a method to ascertain person and item effects in psycholinguistics, a generalized linear mixed effect model (GLMM) with crossed random effects has met limitations in handing serial dependence across persons and items. This paper presents an autoregressive GLMM with crossed random effects that accounts for variability in lag effects across persons and items. The model is shown to be applicable to intensive binary time series eye-tracking data when researchers are interested in detecting experimental condition effects while controlling for previous responses. In addition, a simulation study shows that ignoring lag effects can lead to biased estimates and underestimated standard errors for the experimental condition effects.

8. Vector Autoregressive Models and Granger Causality in Time Series Analysis in Nursing Research: Dynamic Changes Among Vital Signs Prior to Cardiorespiratory Instability Events as an Example.

Science.gov (United States)

Bose, Eliezer; Hravnak, Marilyn; Sereika, Susan M

Patients undergoing continuous vital sign monitoring (heart rate [HR], respiratory rate [RR], pulse oximetry [SpO2]) in real time display interrelated vital sign changes during situations of physiological stress. Patterns in this physiological cross-talk could portend impending cardiorespiratory instability (CRI). Vector autoregressive (VAR) modeling with Granger causality tests is one of the most flexible ways to elucidate underlying causal mechanisms in time series data. The purpose of this article is to illustrate the development of patient-specific VAR models using vital sign time series data in a sample of acutely ill, monitored, step-down unit patients and determine their Granger causal dynamics prior to onset of an incident CRI. CRI was defined as vital signs beyond stipulated normality thresholds (HR = 40-140/minute, RR = 8-36/minute, SpO2 change in RR caused change in HR (21%; i.e., RR changed before HR changed) more often than change in HR causing change in RR (15%). Similarly, changes in RR caused changes in SpO2 (15%) more often than changes in SpO2 caused changes in RR (9%). For HR and SpO2, changes in HR causing changes in SpO2 and changes in SpO2 causing changes in HR occurred with equal frequency (18%). Within this sample of acutely ill patients who experienced a CRI event, VAR modeling indicated that RR changes tend to occur before changes in HR and SpO2. These findings suggest that contextual assessment of RR changes as the earliest sign of CRI is warranted. Use of VAR modeling may be helpful in other nursing research applications based on time series data.

9. Complex time series analysis of PM10 and PM2.5 for a coastal site using artificial neural network modelling and k-means clustering

Science.gov (United States)

Elangasinghe, M. A.; Singhal, N.; Dirks, K. N.; Salmond, J. A.; Samarasinghe, S.

2014-09-01

This paper uses artificial neural networks (ANN), combined with k-means clustering, to understand the complex time series of PM10 and PM2.5 concentrations at a coastal location of New Zealand based on data from a single site. Out of available meteorological parameters from the network (wind speed, wind direction, solar radiation, temperature, relative humidity), key factors governing the pattern of the time series concentrations were identified through input sensitivity analysis performed on the trained neural network model. The transport pathways of particulate matter under these key meteorological parameters were further analysed through bivariate concentration polar plots and k-means clustering techniques. The analysis shows that the external sources such as marine aerosols and local sources such as traffic and biomass burning contribute equally to the particulate matter concentrations at the study site. These results are in agreement with the results of receptor modelling by the Auckland Council based on Positive Matrix Factorization (PMF). Our findings also show that contrasting concentration-wind speed relationships exist between marine aerosols and local traffic sources resulting in very noisy and seemingly large random PM10 concentrations. The inclusion of cluster rankings as an input parameter to the ANN model showed a statistically significant (p < 0.005) improvement in the performance of the ANN time series model and also showed better performance in picking up high concentrations. For the presented case study, the correlation coefficient between observed and predicted concentrations improved from 0.77 to 0.79 for PM2.5 and from 0.63 to 0.69 for PM10 and reduced the root mean squared error (RMSE) from 5.00 to 4.74 for PM2.5 and from 6.77 to 6.34 for PM10. The techniques presented here enable the user to obtain an understanding of potential sources and their transport characteristics prior to the implementation of costly chemical analysis techniques or

10. TimeSeer: Scagnostics for high-dimensional time series.

Science.gov (United States)

Dang, Tuan Nhon; Anand, Anushka; Wilkinson, Leland

2013-03-01

We introduce a method (Scagnostic time series) and an application (TimeSeer) for organizing multivariate time series and for guiding interactive exploration through high-dimensional data. The method is based on nine characterizations of the 2D distributions of orthogonal pairwise projections on a set of points in multidimensional euclidean space. These characterizations include measures, such as, density, skewness, shape, outliers, and texture. Working directly with these Scagnostic measures, we can locate anomalous or interesting subseries for further analysis. Our application is designed to handle the types of doubly multivariate data series that are often found in security, financial, social, and other sectors.

11. Developing a Time Series Predictive Model for Dengue in Zhongshan, China Based on Weather and Guangzhou Dengue Surveillance Data.

Directory of Open Access Journals (Sweden)

Yingtao Zhang

2016-02-01

Full Text Available Dengue is a re-emerging infectious disease of humans, rapidly growing from endemic areas to dengue-free regions due to favorable conditions. In recent decades, Guangzhou has again suffered from several big outbreaks of dengue; as have its neighboring cities. This study aims to examine the impact of dengue epidemics in Guangzhou, China, and to develop a predictive model for Zhongshan based on local weather conditions and Guangzhou dengue surveillance information.We obtained weekly dengue case data from 1st January, 2005 to 31st December, 2014 for Guangzhou and Zhongshan city from the Chinese National Disease Surveillance Reporting System. Meteorological data was collected from the Zhongshan Weather Bureau and demographic data was collected from the Zhongshan Statistical Bureau. A negative binomial regression model with a log link function was used to analyze the relationship between weekly dengue cases in Guangzhou and Zhongshan, controlling for meteorological factors. Cross-correlation functions were applied to identify the time lags of the effect of each weather factor on weekly dengue cases. Models were validated using receiver operating characteristic (ROC curves and k-fold cross-validation.Our results showed that weekly dengue cases in Zhongshan were significantly associated with dengue cases in Guangzhou after the treatment of a 5 weeks prior moving average (Relative Risk (RR = 2.016, 95% Confidence Interval (CI: 1.845-2.203, controlling for weather factors including minimum temperature, relative humidity, and rainfall. ROC curve analysis indicated our forecasting model performed well at different prediction thresholds, with 0.969 area under the receiver operating characteristic curve (AUC for a threshold of 3 cases per week, 0.957 AUC for a threshold of 2 cases per week, and 0.938 AUC for a threshold of 1 case per week. Models established during k-fold cross-validation also had considerable AUC (average 0.938-0.967. The sensitivity and

12. Complex dynamic in ecological time series

Science.gov (United States)

Peter Turchin; Andrew D. Taylor

1992-01-01

Although the possibility of complex dynamical behaviors-limit cycles, quasiperiodic oscillations, and aperiodic chaos-has been recognized theoretically, most ecologists are skeptical of their importance in nature. In this paper we develop a methodology for reconstructing endogenous (or deterministic) dynamics from ecological time series. Our method consists of fitting...

13. On clustering fMRI time series

DEFF Research Database (Denmark)

Goutte, Cyril; Toft, Peter Aundal; Rostrup, E.

1999-01-01

Analysis of fMRI time series is often performed by extracting one or more parameters for the individual voxels. Methods based, e.g., on various statistical tests are then used to yield parameters corresponding to probability of activation or activation strength. However, these methods do not indi...

14. Lecture notes for Advanced Time Series Analysis

DEFF Research Database (Denmark)

1997-01-01

A first version of this notes was used at the lectures in Grenoble, and they are now extended and improved (together with Jan Holst), and used in Ph.D. courses on Advanced Time Series Analysis at IMM and at the Department of Mathematical Statistics, University of Lund, 1994, 1997, ...

15. Inferring interdependencies from short time series

chance – a much weaker null hypothesis than when trying to ensure that the observed value of a test statis- .... for short time series and performs better than exist- ing methods. The details are discussed in the .... seen to perform well in a significant number of combi- nations, although without any discernible relation to the.

16. Argos: An Optimized Time-Series Photometer

2016-01-27

Jan 27, 2016 ... We designed a prime focus CCD photometer, Argos, optimized for high speed time-series measurements of blue variables (Nather & Mukadam 2004) for the 2.1 m telescope at McDonald Observatory. Lack of any intervening optics between the primary mirror and the CCD makes the instrument highly ...

17. Nonlinear Time Series Analysis via Neural Networks

Science.gov (United States)

Volná, Eva; Janošek, Michal; Kocian, Václav; Kotyrba, Martin

This article deals with a time series analysis based on neural networks in order to make an effective forex market [Moore and Roche, J. Int. Econ. 58, 387-411 (2002)] pattern recognition. Our goal is to find and recognize important patterns which repeatedly appear in the market history to adapt our trading system behaviour based on them.

18. Inferring interdependencies from short time series

underlying structural difference in their overall economies, as well as their agricultural sectors. Keywords. Interdependence; correlation; inner composition alignment; time series ..... ables – sharing common properties within a climate zone – and socio-economic indicators, where informa- tion is aggregated only on a ...

19. Recent Advances in Energy Time Series Forecasting

Directory of Open Access Journals (Sweden)

Francisco Martínez-Álvarez

2017-06-01

Full Text Available This editorial summarizes the performance of the special issue entitled Energy Time Series Forecasting, which was published in MDPI’s Energies journal. The special issue took place in 2016 and accepted a total of 21 papers from twelve different countries. Electrical, solar, or wind energy forecasting were the most analyzed topics, introducing brand new methods with very sound results.

20. Dangers and uses of cross-correlation in analyzing time series in perception, performance, movement, and neuroscience: The importance of constructing transfer function autoregressive models.

Science.gov (United States)

Dean, Roger T; Dunsmuir, William T M

2016-06-01

Many articles on perception, performance, psychophysiology, and neuroscience seek to relate pairs of time series through assessments of their cross-correlations. Most such series are individually autocorrelated: they do not comprise independent values. Given this situation, an unfounded reliance is often placed on cross-correlation as an indicator of relationships (e.g., referent vs. response, leading vs. following). Such cross-correlations can indicate spurious relationships, because of autocorrelation. Given these dangers, we here simulated how and why such spurious conclusions can arise, to provide an approach to resolving them. We show that when multiple pairs of series are aggregated in several different ways for a cross-correlation analysis, problems remain. Finally, even a genuine cross-correlation function does not answer key motivating questions, such as whether there are likely causal relationships between the series. Thus, we illustrate how to obtain a transfer function describing such relationships, informed by any genuine cross-correlations. We illustrate the confounds and the meaningful transfer functions by two concrete examples, one each in perception and performance, together with key elements of the R software code needed. The approach involves autocorrelation functions, the establishment of stationarity, prewhitening, the determination of cross-correlation functions, the assessment of Granger causality, and autoregressive model development. Autocorrelation also limits the interpretability of other measures of possible relationships between pairs of time series, such as mutual information. We emphasize that further complexity may be required as the appropriate analysis is pursued fully, and that causal intervention experiments will likely also be needed.

1. The Statistical Analysis of Time Series

CERN Document Server

Anderson, T W

2011-01-01

The Wiley Classics Library consists of selected books that have become recognized classics in their respective fields. With these new unabridged and inexpensive editions, Wiley hopes to extend the life of these important works by making them available to future generations of mathematicians and scientists. Currently available in the Series: T. W. Anderson Statistical Analysis of Time Series T. S. Arthanari & Yadolah Dodge Mathematical Programming in Statistics Emil Artin Geometric Algebra Norman T. J. Bailey The Elements of Stochastic Processes with Applications to the Natural Sciences George

2. Segmentation of Nonstationary Time Series with Geometric Clustering

DEFF Research Database (Denmark)

Bocharov, Alexei; Thiesson, Bo

2013-01-01

We introduce a non-parametric method for segmentation in regimeswitching time-series models. The approach is based on spectral clustering of target-regressor tuples and derives a switching regression tree, where regime switches are modeled by oblique splits. Such models can be learned efficiently...

3. An innovation approach to non-Gaussian time series analysis

OpenAIRE

Ozaki, Tohru; Iino, Mitsunori

2001-01-01

The paper shows that the use of both types of random noise, white noise and Poisson noise, can be justified when using an innovations approach. The historical background for this is sketched, and then several methods of whitening dependent time series are outlined, including a mixture of Gaussian white noise and a compound Poisson process: this appears as a natural extension of the Gaussian white noise model for the prediction errors of a non-Gaussian time series. A stati...

4. The error and covariance structures of the mean approach model of pooled cross-section and time series data

International Nuclear Information System (INIS)

Nuamah, N.N.N.N.

1991-01-01

This paper postulates the assumptions underlying the Mean Approach model and recasts the re-expressions of the normal equations of this model in partitioned matrices of covariances. These covariance structures have been analysed. (author). 16 refs

5. Elements of nonlinear time series analysis and forecasting

CERN Document Server

De Gooijer, Jan G

2017-01-01

This book provides an overview of the current state-of-the-art of nonlinear time series analysis, richly illustrated with examples, pseudocode algorithms and real-world applications. Avoiding a “theorem-proof” format, it shows concrete applications on a variety of empirical time series. The book can be used in graduate courses in nonlinear time series and at the same time also includes interesting material for more advanced readers. Though it is largely self-contained, readers require an understanding of basic linear time series concepts, Markov chains and Monte Carlo simulation methods. The book covers time-domain and frequency-domain methods for the analysis of both univariate and multivariate (vector) time series. It makes a clear distinction between parametric models on the one hand, and semi- and nonparametric models/methods on the other. This offers the reader the option of concentrating exclusively on one of these nonlinear time series analysis methods. To make the book as user friendly as possible...

6. Chow-Liu trees are sufficient predictive models for reproducing key features of functional networks of periictal EEG time-series.

Science.gov (United States)

Steimer, Andreas; Zubler, Frédéric; Schindler, Kaspar

2015-09-01

Seizure freedom in patients suffering from pharmacoresistant epilepsies is still not achieved in 20-30% of all cases. Hence, current therapies need to be improved, based on a more complete understanding of ictogenesis. In this respect, the analysis of functional networks derived from intracranial electroencephalographic (iEEG) data has recently become a standard tool. Functional networks however are purely descriptive models and thus are conceptually unable to predict fundamental features of iEEG time-series, e.g., in the context of therapeutical brain stimulation. In this paper we present some first steps towards overcoming the limitations of functional network analysis, by showing that its results are implied by a simple predictive model of time-sliced iEEG time-series. More specifically, we learn distinct graphical models (so called Chow-Liu (CL) trees) as models for the spatial dependencies between iEEG signals. Bayesian inference is then applied to the CL trees, allowing for an analytic derivation/prediction of functional networks, based on thresholding of the absolute value Pearson correlation coefficient (CC) matrix. Using various measures, the thus obtained networks are then compared to those which were derived in the classical way from the empirical CC-matrix. In the high threshold limit we find (a) an excellent agreement between the two networks and (b) key features of periictal networks as they have previously been reported in the literature. Apart from functional networks, both matrices are also compared element-wise, showing that the CL approach leads to a sparse representation, by setting small correlations to values close to zero while preserving the larger ones. Overall, this paper shows the validity of CL-trees as simple, spatially predictive models for periictal iEEG data. Moreover, we suggest straightforward generalizations of the CL-approach for modeling also the temporal features of iEEG signals. Copyright © 2015 Elsevier Inc. All rights

7. Improving Spring Maize Yield Estimation at Field Scale by Assimilating Time-Series HJ-1 CCD Data into the WOFOST Model Using a New Method with Fast Algorithms

Directory of Open Access Journals (Sweden)

Zhiqiang Cheng

2016-04-01

Full Text Available Field crop yield prediction is crucial to grain storage, agricultural field management, and national agricultural decision-making. Currently, crop models are widely used for crop yield prediction. However, they are hampered by the uncertainty or similarity of input parameters when extrapolated to field scale. Data assimilation methods that combine crop models and remote sensing are the most effective methods for field yield estimation. In this study, the World Food Studies (WOFOST model is used to simulate the growing process of spring maize. Common assimilation methods face some difficulties due to the scarce, constant, or similar nature of the input parameters. For example, yield spatial heterogeneity simulation, coexistence of common assimilation methods and the nutrient module, and time cost are relatively important limiting factors. To address the yield simulation problems at field scale, a simple yet effective method with fast algorithms is presented for assimilating the time-series HJ-1 A/B data into the WOFOST model in order to improve the spring maize yield simulation. First, the WOFOST model is calibrated and validated to obtain the precise mean yield. Second, the time-series leaf area index (LAI is calculated from the HJ data using an empirical regression model. Third, some fast algorithms are developed to complete assimilation. Finally, several experiments are conducted in a large farmland (Hongxing to evaluate the yield simulation results. In general, the results indicate that the proposed method reliably improves spring maize yield estimation in terms of spatial heterogeneity simulation ability and prediction accuracy without affecting the simulation efficiency.

8. Hurst exponents for short time series

Science.gov (United States)

Qi, Jingchao; Yang, Huijie

2011-12-01

A concept called balanced estimator of diffusion entropy is proposed to detect quantitatively scalings in short time series. The effectiveness is verified by detecting successfully scaling properties for a large number of artificial fractional Brownian motions. Calculations show that this method can give reliable scalings for short time series with length ˜102. It is also used to detect scalings in the Shanghai Stock Index, five stock catalogs, and a total of 134 stocks collected from the Shanghai Stock Exchange Market. The scaling exponent for each catalog is significantly larger compared with that for the stocks included in the catalog. Selecting a window with size 650, the evolution of scaling for the Shanghai Stock Index is obtained by the window's sliding along the series. Global patterns in the evolutionary process are captured from the smoothed evolutionary curve. By comparing the patterns with the important event list in the history of the considered stock market, the evolution of scaling is matched with the stock index series. We can find that the important events fit very well with global transitions of the scaling behaviors.

9. Detecting structural breaks in time series via genetic algorithms

DEFF Research Database (Denmark)

Doerr, Benjamin; Fischer, Paul; Hilbert, Astrid

2016-01-01

Detecting structural breaks is an essential task for the statistical analysis of time series, for example, for fitting parametric models to it. In short, structural breaks are points in time at which the behaviour of the time series substantially changes. Typically, no solid background knowledge...... of the time series under consideration is available. Therefore, a black-box optimization approach is our method of choice for detecting structural breaks. We describe a genetic algorithm framework which easily adapts to a large number of statistical settings. To evaluate the usefulness of different crossover...... operator alone. Moreover, we present a specific fitness function which exploits the sparse structure of the break points and which can be evaluated particularly efficiently. The experiments on artificial and real-world time series show that the resulting algorithm detects break points with high precision...

10. Time series analysis based on two-part models for excessive zero count data to detect farm-level outbreaks of swine echinococcosis during meat inspections.

Science.gov (United States)

2017-12-01

Echinococcus multilocularis is a parasite that causes highly pathogenic zoonoses and is maintained in foxes and rodents on Hokkaido Island, Japan. Detection of E. multilocularis infections in swine is epidemiologically important. In Hokkaido, administrative information is provided to swine producers based on the results of meat inspections. However, as the current criteria for providing administrative information often results in delays in providing information to producers, novel criteria are needed. Time series models were developed to monitor autocorrelations between data and lags using data collected from 84 producers at the Higashi-Mokoto Meat Inspection Center between April 2003 and November 2015. The two criteria were quantitatively compared using the sign test for the ability to rapidly detect farm-level outbreaks. Overall, the time series models based on an autoexponentially regressed zero-inflated negative binomial distribution with 60th percentile cumulative distribution function of the model detected outbreaks earlier more frequently than the current criteria (90.5%, 276/305, pdisadvantages of the current criteria to provide an earlier indication of increases in the rate of echinococcosis. Copyright © 2017 Elsevier B.V. All rights reserved.

11. A Comparison of Pseudo-Maximum Likelihood and Asymptotically Distribution-Free Dynamic Factor Analysis Parameter Estimation in Fitting Covariance-Structure Models to Block-Toeplitz Representing Single-Subject Multivariate Time-Series

NARCIS (Netherlands)

1998-01-01

The study of intraindividual variability pervades empirical inquiry in virtually all subdisciplines of psychology. The statistical analysis of multivariate time-series data - a central product of intraindividual investigations - requires special modeling techniques. The dynamic factor model (DFM),

12. Inverse statistical approach in heartbeat time series

International Nuclear Information System (INIS)

Ebadi, H; Shirazi, A H; Mani, Ali R; Jafari, G R

2011-01-01

We present an investigation on heart cycle time series, using inverse statistical analysis, a concept borrowed from studying turbulence. Using this approach, we studied the distribution of the exit times needed to achieve a predefined level of heart rate alteration. Such analysis uncovers the most likely waiting time needed to reach a certain change in the rate of heart beat. This analysis showed a significant difference between the raw data and shuffled data, when the heart rate accelerates or decelerates to a rare event. We also report that inverse statistical analysis can distinguish between the electrocardiograms taken from healthy volunteers and patients with heart failure

13. COMPUTATION OF IMAGE SIMILARITY WITH TIME SERIES

Directory of Open Access Journals (Sweden)

V. Balamurugan

2011-11-01

Full Text Available Searching for similar sequence in large database is an important task in temporal data mining. Similarity search is concerned with efficiently locating subsequences or whole sequences in large archives of sequences. It is useful in typical data mining applications and it can be easily extended to image retrieval. In this work, time series similarity analysis that involves dimensionality reduction and clustering is adapted on digital images to find similarity between them. The dimensionality reduced time series is represented as clusters by the use of K-Means clustering and the similarity distance between two images is found by finding the distance between the signatures of their clusters. To quantify the extent of similarity between two sequences, Earth Mover’s Distance (EMD is used. From the experiments on different sets of images, it is found that this technique is well suited for measuring the subjective similarity between two images.

14. Track Irregularity Time Series Analysis and Trend Forecasting

Directory of Open Access Journals (Sweden)

Jia Chaolong

2012-01-01

Full Text Available The combination of linear and nonlinear methods is widely used in the prediction of time series data. This paper analyzes track irregularity time series data by using gray incidence degree models and methods of data transformation, trying to find the connotative relationship between the time series data. In this paper, GM (1,1 is based on first-order, single variable linear differential equations; after an adaptive improvement and error correction, it is used to predict the long-term changing trend of track irregularity at a fixed measuring point; the stochastic linear AR, Kalman filtering model, and artificial neural network model are applied to predict the short-term changing trend of track irregularity at unit section. Both long-term and short-term changes prove that the model is effective and can achieve the expected accuracy.

15. Next Day Price Forecasting in Deregulated Market by Combination of Artificial Neural Network and ARIMA Time Series Models

Science.gov (United States)

Areekul, Phatchakorn; Senjyu, Tomonobu; Urasaki, Naomitsu; Yona, Atsushi

Electricity price forecasting is becoming increasingly relevant to power producers and consumers in the new competitive electric power markets, when planning bidding strategies in order to maximize their benefits and utilities, respectively. This paper proposed a method to predict hourly electricity prices for next-day electricity markets by combination methodology of ARIMA and ANN models. The proposed method is examined on the Australian National Electricity Market (NEM), New South Wales regional in year 2006. Comparison of forecasting performance with the proposed ARIMA, ANN and combination (ARIMA-ANN) models are presented. Empirical results indicate that an ARIMA-ANN model can improve the price forecasting accuracy.

16. A combined method to estimate parameters of the thalamocortical model from a heavily noise-corrupted time series of action potential

Science.gov (United States)

Wang, Ruofan; Wang, Jiang; Deng, Bin; Liu, Chen; Wei, Xile; Tsang, K. M.; Chan, W. L.

2014-03-01

A combined method composing of the unscented Kalman filter (UKF) and the synchronization-based method is proposed for estimating electrophysiological variables and parameters of a thalamocortical (TC) neuron model, which is commonly used for studying Parkinson's disease for its relay role of connecting the basal ganglia and the cortex. In this work, we take into account the condition when only the time series of action potential with heavy noise are available. Numerical results demonstrate that not only this method can estimate model parameters from the extracted time series of action potential successfully but also the effect of its estimation is much better than the only use of the UKF or synchronization-based method, with a higher accuracy and a better robustness against noise, especially under the severe noise conditions. Considering the rather important role of TC neuron in the normal and pathological brain functions, the exploration of the method to estimate the critical parameters could have important implications for the study of its nonlinear dynamics and further treatment of Parkinson's disease.

17. A combined method to estimate parameters of the thalamocortical model from a heavily noise-corrupted time series of action potential

International Nuclear Information System (INIS)

Wang, Ruofan; Wang, Jiang; Deng, Bin; Liu, Chen; Wei, Xile; Tsang, K. M.; Chan, W. L.

2014-01-01

A combined method composing of the unscented Kalman filter (UKF) and the synchronization-based method is proposed for estimating electrophysiological variables and parameters of a thalamocortical (TC) neuron model, which is commonly used for studying Parkinson's disease for its relay role of connecting the basal ganglia and the cortex. In this work, we take into account the condition when only the time series of action potential with heavy noise are available. Numerical results demonstrate that not only this method can estimate model parameters from the extracted time series of action potential successfully but also the effect of its estimation is much better than the only use of the UKF or synchronization-based method, with a higher accuracy and a better robustness against noise, especially under the severe noise conditions. Considering the rather important role of TC neuron in the normal and pathological brain functions, the exploration of the method to estimate the critical parameters could have important implications for the study of its nonlinear dynamics and further treatment of Parkinson's disease

18. Measurement and interpretation of subtle deformation signals at Unimak Island from 2003 to 2010 using weather model-assisted time series InSAR

Science.gov (United States)

Gong, W.; Meyer, F. J.; Lee, C.-W.; Lu, Z.; Freymueller, J.

2015-02-01

A 7 year time series of satellite radar images over Unimak Island, Alaska—site of Westdahl Volcano, Fisher Caldera, and Shishaldin Volcano—was processed using a model-free Persistent Scatterer Interferometry technique assisted by numerical weather prediction model. The deformation-only signals were optimally extracted from atmosphere-contaminated phase records. The reconstructed deformation time series maps are compared with campaign and continuous Global Positioning System (GPS) measurements as well as Small Baseline Subset interferometric synthetic aperture radar (InSAR) results for quality assessment and geophysical interpretation. We observed subtle surface inflation at Westdahl Volcano that can be fit by a Mogi source located at approximately 3.6 km north of Westdahl peak and at depth of about 6.9 km that is consistent with the GPS-estimated depth for the 1998 to 2001 time period. The magma chamber volume change decays during the period of 2003 to 2010. The deformation field over Fisher Caldera is steadily subsiding over time. Its best fit analytical model is a sill source that is about 7.9 km in length, 0.54 km in width, and located at about 5.5 km below sea level underneath the center of Fisher Caldera with strike angle of N52°E. Very little deformation was detected near Shishaldin peak; however, a region approximately 15 km east of Shishaldin, as well as an area at the Tugamak range at about 30 km northwest of Shishaldin, shows evidence for movement toward the satellite, with a temporal signature correlated with the 2004 Shishaldin eruption. The cause of these movements is unknown.

19. Clinical and epidemiological round: Interrupted time series

Directory of Open Access Journals (Sweden)

León-Álvarez, Alba Luz

2017-07-01

Full Text Available In quasi-experimental research, it is commonly used the interrupted time series analysis, which measures the effect of an intervention from a specific time point. This technique integrates longitudinal data and allows to discover detailed trends before and after such intervention. It is considered an important tool to understand the patterns of change after any event, it is applicable in different disciplines and have a great potential to draw conclusions in research with long follow-up periods that require objective evaluation of interventions.

20. Progress on the calibration of channel geometry and friction parameters of the LISFLOOD-FP hydraulic model using time series of SAR flood images

Science.gov (United States)

Wood, M.; Neal, J. C.; Hostache, R.; Corato, G.; Bates, P. D.; Chini, M.; Giustarini, L.; Matgen, P.; Wagener, T.

2014-12-01

The objective of this work is to calibrate channel depth and roughness parameters of the LISFLOOD-FP Sub-Grid 2D hydraulic model using SAR image-derived flood extent maps. The aim is to reduce uncertainty in flood model predictions for those rivers where channel geometry is unknown and/or cannot be easily measured. In particular we consider the effectiveness of using real SAR data for calibration and whether the number and timings of SAR acquisitions is of benefit to the final result. Terrain data are processed from 2m LiDAR images and inflows to the model are taken from gauged data. As a test case we applied the method to the River Severn between Worcester and Tewkesbury. We firstly applied the automatic flood mapping algorithm of Giustarini[1] et al. (2013) to ENVISAT ASAR (wide swath mode) flood images; generating a series of flood maps. We then created an ensemble of flood extent maps with the hydraulic model (each model representing a unique parameter set). Where there is a favourable comparison between the modelled flood map and the SAR obtained flood map we may suggest an optimal parameter set. Applying the method to a sequence of SAR acquisitions provides insight into the advantages, disadvantages and limitations of using series of acquired images. To complete the investigation we simultaneously explore parameter 'identifiabilty' within a sequence of available satellite observations by adopting the DYNIA method proposed by Wagener[2] et al. (2003). We show where we might most easily detect the depth and roughness parameters within the SAR acquisition sequence. [1] Giustarini. 2013. 'A Change Detection Approach to Flood Mapping in Urban Areas Using TerraSAR-X'. IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 4. [2] Wagener. 2003. 'Towards reduced uncertainty in conceptual rainfall-runoff modelling: Dynamic identifiability analysis'. Hydrol. Process. 17, 455-476.

1. Grey-box Modelling of a Household Refrigeration Unit Using Time Series Data in Application to Demand Side Management

DEFF Research Database (Denmark)

Sossan, Fabrizio; Lakshmanan, Venkatachalam; Costanzo, Giuseppe Tommaso

2016-01-01

) to shift the electricity consumption of a freezer in demand response experiments, thereby addressing the model selection problem also from the application point of view and showing in an experimental context the ability of MPC to exploit the freezer as a demand side resource (DSR)....

2. Reconstruction of gap-free time series satellite observations of land surface temperature to model spectral soil thermal admittance

NARCIS (Netherlands)

Ghafarian Malamiri, H.R.

2015-01-01

The soil thermal properties (soil thermal conductivity, soil heat capacity and soil diffusivity) are the main parameters in the applications that need quantitative information on soil heat transfer. Conventionally, these properties are either measured in situ or estimated by semi-empirical models

3. Multivariate Time Series Analysis for Optimum Production Forecast ...

African Journals Online (AJOL)

... by 0.002579KG/Month. Finally, this work adds to the growing body of literature on data-driven production and inventory management by utilizing historical data in the development of useful forecasting mathematical model. Keywords: production model, inventory management, multivariate time series, production forecast ...

4. Multivariate Time Series Analysis for Optimum Production Forecast ...

African Journals Online (AJOL)

on data-driven production and inventory management by utilizing historical data in the development of useful forecasting mathematical model. Keywords: production model, inventory management, multivariate time series, production forecast. Introduction. A large assortment of forecasting techniques has been developed ...

5. Neural networks prediction and fault diagnosis applied to stationary and non stationary ARMA (Autoregressive moving average) modeled time series

International Nuclear Information System (INIS)

Marseguerra, M.; Minoggio, S.; Rossi, A.; Zio, E.

1992-01-01

The correlated noise affecting many industrial plants under stationary or cyclo-stationary conditions - nuclear reactors included -has been successfully modeled by autoregressive moving average (ARMA) due to the versatility of this technique. The relatively recent neural network methods have similar features and much effort is being devoted to exploring their usefulness in forecasting and control. Identifying a signal by means of an ARMA model gives rise to the problem of selecting its correct order. Similar difficulties must be faced when applying neural network methods and, specifically, particular care must be given to the setting up of the appropriate network topology, the data normalization procedure and the learning code. In the present paper the capability of some neural networks of learning ARMA and seasonal ARMA processes is investigated. The results of the tested cases look promising since they indicate that the neural networks learn the underlying process with relative ease so that their forecasting capability may represent a convenient fault diagnosis tool. (Author)

6. Application of time series analysis in modelling and forecasting emergency department visits in a medical centre in Southern Taiwan

OpenAIRE

Juang, Wang-Chuan; Huang, Sin-Jhih; Huang, Fong-Dee; Cheng, Pei-Wen; Wann, Shue-Ren

2017-01-01

Objective Emergency department (ED) overcrowding is acknowledged as an increasingly important issue worldwide. Hospital managers are increasingly paying attention to ED crowding in order to provide higher quality medical services to patients. One of the crucial elements for a good management strategy is demand forecasting. Our study sought to construct an adequate model and to forecast monthly ED visits. Methods We retrospectively gathered monthly ED visits from January 2009 to December 2016 ...

7. Reconstruction of ensembles of coupled time-delay systems from time series.

Science.gov (United States)

Sysoev, I V; Prokhorov, M D; Ponomarenko, V I; Bezruchko, B P

2014-06-01

We propose a method to recover from time series the parameters of coupled time-delay systems and the architecture of couplings between them. The method is based on a reconstruction of model delay-differential equations and estimation of statistical significance of couplings. It can be applied to networks composed of nonidentical nodes with an arbitrary number of unidirectional and bidirectional couplings. We test our method on chaotic and periodic time series produced by model equations of ensembles of diffusively coupled time-delay systems in the presence of noise, and apply it to experimental time series obtained from electronic oscillators with delayed feedback coupled by resistors.

8. Quasi-Maximum Likelihood Estimation and Bootstrap Inference in Fractional Time Series Models with Heteroskedasticity of Unknown Form

DEFF Research Database (Denmark)

Cavaliere, Giuseppe; Nielsen, Morten Ørregaard; Taylor, Robert

of the estimator now depends on nuisance parameters derived both from the weak dependence and heteroskedasticity present in the shocks. We then investigate classical methods of inference based on the Wald, likelihood ratio and Lagrange multiplier tests for linear hypotheses on either or both of the long and short...... memory parameters of the model. The limiting null distributions of these test statistics are shown to be non-pivotal under heteroskedasticity, while that of a robustWald statistic (based around a sandwich estimator of the variance) is pivotal. We show that wild bootstrap implementations of the tests...

9. Timing calibration and spectral cleaning of LOFAR time series data

OpenAIRE

Corstanje, A.; Buitink, S.; Enriquez, J. E.; Falcke, H.; Hörandel, J. R.; Krause, M.; Nelles, A.; Rachen, J. P.; Schellart, P.; Scholten, O.; ter Veen, S.; Thoudam, S.; Trinh, T. N. G.

2016-01-01

We describe a method for spectral cleaning and timing calibration of short time series data of the voltage in individual radio interferometer receivers. It makes use of phase differences in fast Fourier transform (FFT) spectra across antenna pairs. For strong, localized terrestrial sources these are stable over time, while being approximately uniform-random for a sum over many sources or for noise. Using only milliseconds-long datasets, the method finds the strongest interfering transmitters,...

10. Detecting spatio-temporal changes in agricultural land use in Heilongjiang province, China using MODIS time-series data and a random forest regression model

Science.gov (United States)

Hu, Q.; Friedl, M. A.; Wu, W.

2017-12-01

Accurate and timely information regarding the spatial distribution of crop types and their changes is essential for acreage surveys, yield estimation, water management, and agricultural production decision-making. In recent years, increasing population, dietary shifts and climate change have driven drastic changes in China's agricultural land use. However, no maps are currently available that document the spatial and temporal patterns of these agricultural land use changes. Because of its short revisit period, rich spectral bands and global coverage, MODIS time series data has been shown to have great potential for detecting the seasonal dynamics of different crop types. However, its inherently coarse spatial resolution limits the accuracy with which crops can be identified from MODIS in regions with small fields or complex agricultural landscapes. To evaluate this more carefully and specifically understand the strengths and weaknesses of MODIS data for crop-type mapping, we used MODIS time-series imagery to map the sub-pixel fractional crop area for four major crop types (rice, corn, soybean and wheat) at 500-m spatial resolution for Heilongjiang province, one of the most important grain-production regions in China where recent agricultural land use change has been rapid and pronounced. To do this, a random forest regression (RF-g) model was constructed to estimate the percentage of each sub-pixel crop type in 2006, 2011 and 2016. Crop type maps generated through expert visual interpretation of high spatial resolution images (i.e., Landsat and SPOT data) were used to calibrate the regression model. Five different time series of vegetation indices (155 features) derived from different spectral channels of MODIS land surface reflectance (MOD09A1) data were used as candidate features for the RF-g model. An out-of-bag strategy and backward elimination approach was applied to select the optimal spectra-temporal feature subset for each crop type. The resulting crop maps

11. A Numerical Method to Generate High Temporal Resolution Precipitation Time Series by Combining Weather Radar Measurements with a Nowcast Model

DEFF Research Database (Denmark)

Nielsen, Jesper Ellerbæk; Thorndahl, Søren Liedtke; Rasmussen, Michael R.

2014-01-01

12. Time Series Analysis of Insar Data: Methods and Trends

Science.gov (United States)

Osmanoglu, Batuhan; Sunar, Filiz; Wdowinski, Shimon; Cano-Cabral, Enrique

2015-01-01

Time series analysis of InSAR data has emerged as an important tool for monitoring and measuring the displacement of the Earth's surface. Changes in the Earth's surface can result from a wide range of phenomena such as earthquakes, volcanoes, landslides, variations in ground water levels, and changes in wetland water levels. Time series analysis is applied to interferometric phase measurements, which wrap around when the observed motion is larger than one-half of the radar wavelength. Thus, the spatio-temporal ''unwrapping" of phase observations is necessary to obtain physically meaningful results. Several different algorithms have been developed for time series analysis of InSAR data to solve for this ambiguity. These algorithms may employ different models for time series analysis, but they all generate a first-order deformation rate, which can be compared to each other. However, there is no single algorithm that can provide optimal results in all cases. Since time series analyses of InSAR data are used in a variety of applications with different characteristics, each algorithm possesses inherently unique strengths and weaknesses. In this review article, following a brief overview of InSAR technology, we discuss several algorithms developed for time series analysis of InSAR data using an example set of results for measuring subsidence rates in Mexico City.

13. Quirky patterns in time-series of estimates of recruitment could be artefacts

DEFF Research Database (Denmark)

Dickey-Collas, M.; Hinzen, N.T.; Nash, R.D.M.

2015-01-01

employed, and the associated modelling assumptions, can have an important influence on the characteristics of each time-series. We explore this idea by investigating recruitment time-series with three different recruitment parameterizations: a stock–recruitment model, a random-walk time-series model...

14. Analysis of the impact of crude oil price fluctuations on China's stock market in different periods-Based on time series network model

Science.gov (United States)

An, Yang; Sun, Mei; Gao, Cuixia; Han, Dun; Li, Xiuming

2018-02-01

This paper studies the influence of Brent oil price fluctuations on the stock prices of China's two distinct blocks, namely, the petrochemical block and the electric equipment and new energy block, applying the Shannon entropy of information theory. The co-movement trend of crude oil price and stock prices is divided into different fluctuation patterns with the coarse-graining method. Then, the bivariate time series network model is established for the two blocks stock in five different periods. By joint analysis of the network-oriented metrics, the key modes and underlying evolutionary mechanisms were identified. The results show that the both networks have different fluctuation characteristics in different periods. Their co-movement patterns are clustered in some key modes and conversion intermediaries. The study not only reveals the lag effect of crude oil price fluctuations on the stock in Chinese industry blocks but also verifies the necessity of research on special periods, and suggests that the government should use different energy policies to stabilize market volatility in different periods. A new way is provided to study the unidirectional influence between multiple variables or complex time series.

15. Long-term retrospective analysis of mackerel spawning in the North Sea: a new time series and modeling approach to CPR data.

Science.gov (United States)

Jansen, Teunis; Kristensen, Kasper; Payne, Mark; Edwards, Martin; Schrum, Corinna; Pitois, Sophie

2012-01-01

We present a unique view of mackerel (Scomber scombrus) in the North Sea based on a new time series of larvae caught by the Continuous Plankton Recorder (CPR) survey from 1948-2005, covering the period both before and after the collapse of the North Sea stock. Hydrographic backtrack modelling suggested that the effect of advection is very limited between spawning and larvae capture in the CPR survey. Using a statistical technique not previously applied to CPR data, we then generated a larval index that accounts for both catchability as well as spatial and temporal autocorrelation. The resulting time series documents the significant decrease of spawning from before 1970 to recent depleted levels. Spatial distributions of the larvae, and thus the spawning area, showed a shift from early to recent decades, suggesting that the central North Sea is no longer as important as the areas further west and south. These results provide a consistent and unique perspective on the dynamics of mackerel in this region and can potentially resolve many of the unresolved questions about this stock.

16. Long-term retrospective analysis of mackerel spawning in the North Sea: a new time series and modeling approach to CPR data.

Directory of Open Access Journals (Sweden)

Teunis Jansen

Full Text Available We present a unique view of mackerel (Scomber scombrus in the North Sea based on a new time series of larvae caught by the Continuous Plankton Recorder (CPR survey from 1948-2005, covering the period both before and after the collapse of the North Sea stock. Hydrographic backtrack modelling suggested that the effect of advection is very limited between spawning and larvae capture in the CPR survey. Using a statistical technique not previously applied to CPR data, we then generated a larval index that accounts for both catchability as well as spatial and temporal autocorrelation. The resulting time series documents the significant decrease of spawning from before 1970 to recent depleted levels. Spatial distributions of the larvae, and thus the spawning area, showed a shift from early to recent decades, suggesting that the central North Sea is no longer as important as the areas further west and south. These results provide a consistent and unique perspective on the dynamics of mackerel in this region and can potentially resolve many of the unresolved questions about this stock.

17. Time series modeling with pruned multi-layer perceptron and 2-stage damped least-squares method

International Nuclear Information System (INIS)

Voyant, Cyril; Tamas, Wani; Paoli, Christophe; Balu, Aurélia; Muselli, Marc; Nivet, Marie-Laure; Notton, Gilles

2014-01-01

A Multi-Layer Perceptron (MLP) defines a family of artificial neural networks often used in TS modeling and forecasting. Because of its ''black box'' aspect, many researchers refuse to use it. Moreover, the optimization (often based on the exhaustive approach where ''all'' configurations are tested) and learning phases of this artificial intelligence tool (often based on the Levenberg-Marquardt algorithm; LMA) are weaknesses of this approach (exhaustively and local minima). These two tasks must be repeated depending on the knowledge of each new problem studied, making the process, long, laborious and not systematically robust. In this paper a pruning process is proposed. This method allows, during the training phase, to carry out an inputs selecting method activating (or not) inter-nodes connections in order to verify if forecasting is improved. We propose to use iteratively the popular damped least-squares method to activate inputs and neurons. A first pass is applied to 10% of the learning sample to determine weights significantly different from 0 and delete other. Then a classical batch process based on LMA is used with the new MLP. The validation is done using 25 measured meteorological TS and cross-comparing the prediction results of the classical LMA and the 2-stage LMA

18. Fractal fluctuations in cardiac time series

Science.gov (United States)

West, B. J.; Zhang, R.; Sanders, A. W.; Miniyar, S.; Zuckerman, J. H.; Levine, B. D.; Blomqvist, C. G. (Principal Investigator)

1999-01-01

Human heart rate, controlled by complex feedback mechanisms, is a vital index of systematic circulation. However, it has been shown that beat-to-beat values of heart rate fluctuate continually over a wide range of time scales. Herein we use the relative dispersion, the ratio of the standard deviation to the mean, to show, by systematically aggregating the data, that the correlation in the beat-to-beat cardiac time series is a modulated inverse power law. This scaling property indicates the existence of long-time memory in the underlying cardiac control process and supports the conclusion that heart rate variability is a temporal fractal. We argue that the cardiac control system has allometric properties that enable it to respond to a dynamical environment through scaling.

19. Period Estimation in Astronomical Time Series

Science.gov (United States)

Protopapas, Pavlos

2011-09-01

Detection of periodicity and period estimation in non-uniformly sampled time series data is frequently a goal in Astronomical data analysis. There are various problems faced: Firstly, data is sampled non-uniformly which makes it difficult to use simple Fourier transform for performing spectral analysis. Secondly, there are large gaps in data which makes it difficult to interpolate the signal for re-sampling. Finally, in data sets with smaller time periods the non-uniformity in sampling and noise in data pose even greater problems because of the lesser number of samples per period. In this talk we review existing methods and then we propose new approaches in determining periods. We first use correntropy (an alternative to autocorrelation) that encapsulates non-linear correlations using a spatio-temporal kernel to estimate accurately the time period of the data. The other uses periodic kernels in non-parametric Gaussian process. These new techniques are also used for identifying periodic signals.

20. Fourier analysis of time series an introduction

CERN Document Server

Bloomfield, Peter

2000-01-01

A new, revised edition of a yet unrivaled work on frequency domain analysis Long recognized for his unique focus on frequency domain methods for the analysis of time series data as well as for his applied, easy-to-understand approach, Peter Bloomfield brings his well-known 1976 work thoroughly up to date. With a minimum of mathematics and an engaging, highly rewarding style, Bloomfield provides in-depth discussions of harmonic regression, harmonic analysis, complex demodulation, and spectrum analysis. All methods are clearly illustrated using examples of specific data sets, while ample

1. Groundwater Visualisation System (GVS): A software framework for integrated display and interrogation of conceptual hydrogeological models, data and time-series animation

Science.gov (United States)

Cox, Malcolm E.; James, Allan; Hawke, Amy; Raiber, Matthias

2013-05-01

Management of groundwater systems requires realistic conceptual hydrogeological models as a framework for numerical simulation modelling, but also for system understanding and communicating this to stakeholders and the broader community. To help overcome these challenges we developed GVS (Groundwater Visualisation System), a stand-alone desktop software package that uses interactive 3D visualisation and animation techniques. The goal was a user-friendly groundwater management tool that could support a range of existing real-world and pre-processed data, both surface and subsurface, including geology and various types of temporal hydrological information. GVS allows these data to be integrated into a single conceptual hydrogeological model. In addition, 3D geological models produced externally using other software packages, can readily be imported into GVS models, as can outputs of simulations (e.g. piezometric surfaces) produced by software such as MODFLOW or FEFLOW. Boreholes can be integrated, showing any down-hole data and properties, including screen information, intersected geology, water level data and water chemistry. Animation is used to display spatial and temporal changes, with time-series data such as rainfall, standing water levels and electrical conductivity, displaying dynamic processes. Time and space variations can be presented using a range of contouring and colour mapping techniques, in addition to interactive plots of time-series parameters. Other types of data, for example, demographics and cultural information, can also be readily incorporated. The GVS software can execute on a standard Windows or Linux-based PC with a minimum of 2 GB RAM, and the model output is easy and inexpensive to distribute, by download or via USB/DVD/CD. Example models are described here for three groundwater systems in Queensland, northeastern Australia: two unconfined alluvial groundwater systems with intensive irrigation, the Lockyer Valley and the upper Condamine

2. Multiresolution analysis of Bursa Malaysia KLCI time series

Science.gov (United States)

Ismail, Mohd Tahir; Dghais, Amel Abdoullah Ahmed

2017-05-01

In general, a time series is simply a sequence of numbers collected at regular intervals over a period. Financial time series data processing is concerned with the theory and practice of processing asset price over time, such as currency, commodity data, and stock market data. The primary aim of this study is to understand the fundamental characteristics of selected financial time series by using the time as well as the frequency domain analysis. After that prediction can be executed for the desired system for in sample forecasting. In this study, multiresolution analysis which the assist of discrete wavelet transforms (DWT) and maximal overlap discrete wavelet transform (MODWT) will be used to pinpoint special characteristics of Bursa Malaysia KLCI (Kuala Lumpur Composite Index) daily closing prices and return values. In addition, further case study discussions include the modeling of Bursa Malaysia KLCI using linear ARIMA with wavelets to address how multiresolution approach improves fitting and forecasting results.

3. Time series analysis and its applications with R examples

CERN Document Server

Shumway, Robert H

2017-01-01

The fourth edition of this popular graduate textbook, like its predecessors, presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Numerous examples using nontrivial data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and monitoring a nuclear test ban treaty. The book is designed as a textbook for graduate level students in the physical, biological, and social sciences and as a graduate level text in statistics. Some parts may also serve as an undergraduate introductory course. Theory and methodology are separated to allow presentations on different levels. In addition to coverage of classical methods of time series regression, ARIMA models, spectral analysis and state-space models, the text includes modern developments including categorical time series analysis, multivariate spectral methods, long memory series, nonli...

4. Calibrating a hydraulic model using water levels derived from time series high-resolution Radarsat-2 synthetic aperture radar images and elevation data

Science.gov (United States)

Trudel, M.; Desrochers, N.; Leconte, R.

2017-12-01

Knowledge of water extent (WE) and level (WL) of rivers is necessary to calibrate and validate hydraulic models and thus to better simulate and forecast floods. Synthetic aperture radar (SAR) has demonstrated its potential for delineating water bodies, as backscattering of water is much lower than that of other natural surfaces. The ability of SAR to obtain information despite cloud cover makes it an interesting tool for temporal monitoring of water bodies. The delineation of WE combined with a high-resolution digital terrain model (DTM) allows extracting WL. However, most research using SAR data to calibrate hydraulic models has been carried out using one or two images. The objectives of this study is to use WL derived from time series high resolution Radarsat-2 SAR images for the calibration of a 1-D hydraulic model (HEC-RAS). Twenty high-resolution (5 m) Radarsat-2 images were acquired over a 40 km reach of the Athabasca River, in northern Alberta, Canada, between 2012 and 2016, covering both low and high flow regimes. A high-resolution (2m) DTM was generated combining information from LIDAR data and bathymetry acquired between 2008 and 2016 by boat surveying. The HEC-RAS model was implemented on the Athabasca River to simulate WL using cross-sections spaced by 100 m. An image histogram thresholding method was applied on each Radarsat-2 image to derive WE. WE were then compared against each cross-section to identify those were the slope of the banks is not too abrupt and therefore amenable to extract WL. 139 observations of WL at different locations along the river reach and with streamflow measurements were used to calibrate the HEC-RAS model. The RMSE between SAR-derived and simulated WL is under 0.35 m. Validation was performed using in situ observations of WL measured in 2008, 2012 and 2016. The RMSE between the simulated water levels calibrated with SAR images and in situ observations is less than 0.20 m. In addition, a critical success index (CSI) was

5. Time Series Based for Online Signature Verification

Directory of Open Access Journals (Sweden)

I Ketut Gede Darma Putra

2013-11-01

Full Text Available Signature verification system is to match the tested signature with a claimed signature. This paper proposes time series based for feature extraction method and dynamic time warping for match method. The system made by process of testing 900 signatures belong to 50 participants, 3 signatures for reference and 5 signatures from original user, simple imposters and trained imposters for signatures test. The final result system was tested with 50 participants with 3 references. This test obtained that system accuracy without imposters is 90,44897959% at threshold 44 with rejection errors (FNMR is 5,2% and acceptance errors (FMR is 4,35102%, when with imposters system accuracy is 80,1361% at threshold 27 with error rejection (FNMR is 15,6% and acceptance errors (average FMR is 4,263946%, with details as follows: acceptance errors is 0,391837%, acceptance errors simple imposters is 3,2% and acceptance errors trained imposters is 9,2%.

6. Recurrent Neural Network Applications for Astronomical Time Series

Science.gov (United States)

Protopapas, Pavlos

2017-06-01

The benefits of good predictive models in astronomy lie in early event prediction systems and effective resource allocation. Current time series methods applicable to regular time series have not evolved to generalize for irregular time series. In this talk, I will describe two Recurrent Neural Network methods, Long Short-Term Memory (LSTM) and Echo State Networks (ESNs) for predicting irregular time series. Feature engineering along with a non-linear modeling proved to be an effective predictor. For noisy time series, the prediction is improved by training the network on error realizations using the error estimates from astronomical light curves. In addition to this, we propose a new neural network architecture to remove correlation from the residuals in order to improve prediction and compensate for the noisy data. Finally, I show how to set hyperparameters for a stable and performant solution correctly. In this work, we circumvent this obstacle by optimizing ESN hyperparameters using Bayesian optimization with Gaussian Process priors. This automates the tuning procedure, enabling users to employ the power of RNN without needing an in-depth understanding of the tuning procedure.

7. Analysis and generation of groundwater concentration time series

Science.gov (United States)

Crăciun, Maria; Vamoş, Călin; Suciu, Nicolae

2018-01-01

Concentration time series are provided by simulated concentrations of a nonreactive solute transported in groundwater, integrated over the transverse direction of a two-dimensional computational domain and recorded at the plume center of mass. The analysis of a statistical ensemble of time series reveals subtle features that are not captured by the first two moments which characterize the approximate Gaussian distribution of the two-dimensional concentration fields. The concentration time series exhibit a complex preasymptotic behavior driven by a nonstationary trend and correlated fluctuations with time-variable amplitude. Time series with almost the same statistics are generated by successively adding to a time-dependent trend a sum of linear regression terms, accounting for correlations between fluctuations around the trend and their increments in time, and terms of an amplitude modulated autoregressive noise of order one with time-varying parameter. The algorithm generalizes mixing models used in probability density function approaches. The well-known interaction by exchange with the mean mixing model is a special case consisting of a linear regression with constant coefficients.

8. Tropospheric correction of InSAR time-series with the weather research forecasting model: an application to volcanic deformation monitoring

Science.gov (United States)

Gong, W.; Meyer, F.; Webley, P.; Lu, Z.

2010-12-01

The potential of Interferometric Synthetic Aperture Radar (InSAR) for surveying volcanic deformation has been proven extensively over the last few decades. However, the value and applicability of InSAR for detecting the subtle signs of the onset of an eruptive even is limited by the influence of temporal decorrelation and electromagnetic path delay variations (e.g. the troposphere and ionosphere effects), as they reduce the sensitivity and accuracy of the technique. In this paper, we will present an integration of time series InSAR processing with predictions from the high-resolution Weather Research and Forecasting (WRF) Model to improve the performance of InSAR for volcano monitoring application, especially to increase InSAR ability to detect subtle pre-eruptive deformation. The non-hydrostatic WRF model is part of the latest generation of numerical weather prediction (NWP) and atmospheric simulation systems. WRF can be implemented with a nested grid system, allowing atmospheric delay phase maps to be created at a spatial resolution down to the 500 m and is expected to outperform other NWPs in term of prediction accuracy. We show that the integration of WRF with InSAR measurements allows reducing the amplitude and variance of atmospheric phase delay signals which increases the sensitivity of InSAR to deformation signals. Moreover, for some InSAR algorithms that select Phase-stable-points based on an analysis of phase variance, the mitigation of atmospheric signals with WRF leads to an increase of the density of detected coherent points. We apply the WRF-assisted InSAR technique to Unimak Island at the eastern Aleutians, where three active volcanic systems exist. By comparing the deformation monitoring results from the InSAR time series with and without the tropospheric correction, we demonstrate the advantage of applying WRF simulation result in InSAR tropospheric correction and the contribution this technique will provide to volcano monitoring. Structure

9. Compounding approach for univariate time series with nonstationary variances

Science.gov (United States)

Schäfer, Rudi; Barkhofen, Sonja; Guhr, Thomas; Stöckmann, Hans-Jürgen; Kuhl, Ulrich

2015-12-01

A defining feature of nonstationary systems is the time dependence of their statistical parameters. Measured time series may exhibit Gaussian statistics on short time horizons, due to the central limit theorem. The sample statistics for long time horizons, however, averages over the time-dependent variances. To model the long-term statistical behavior, we compound the local distribution with the distribution of its parameters. Here, we consider two concrete, but diverse, examples of such nonstationary systems: the turbulent air flow of a fan and a time series of foreign exchange rates. Our main focus is to empirically determine the appropriate parameter distribution for the compounding approach. To this end, we extract the relevant time scales by decomposing the time signals into windows and determine the distribution function of the thus obtained local variances.

10. Quasi-static time-series simulation using OpenDSS in IEEE distribution feeder model with high PV penetration and its impact on solar forecasting

Science.gov (United States)

Mohammed, Touseef Ahmed Faisal

Distribution System Simulator developed by Electric Power Research Institute, to simulate grid voltage profile with a large scale PV system under quasi-static time series considering variations of PV output in seconds, minutes, and the average daily load variations. A 13 bus IEEE distribution feeder model is utilized with distributed residential and commercial scale PV at different buses for simulation studies. Time series simulations are discussed for various modes of operation considering dynamic PV penetration at different time periods in a day. In addition, this thesis demonstrates simulations taking into account the presence of moving cloud for solar forecasting studies.

11. Seasonal time series data imputation: Comparison between feed ...

African Journals Online (AJOL)

Specifically we examine how recursive and direct estimates from forward and backward learning Artificial Neural Networks (ANN) compares with seasonal ARIMA estimates and interpolation estimates of Additive outliers in seasonal ARIMA models. A comparison statistics is also proposed. Keywords: Time Series; Artificial ...

12. Long-memory time series theory and methods

CERN Document Server

Palma, Wilfredo

2007-01-01

Wilfredo Palma, PhD, is Chairman and Professor of Statistics in the Department of Statistics at Pontificia Universidad Católica de Chile. Dr. Palma has published several refereed articles and has received over a dozen academic honors and awards. His research interests include time series analysis, prediction theory, state space systems, linear models, and econometrics.

13. Time Series Factor Analysis with an Application to Measuring Money

NARCIS (Netherlands)

Gilbert, Paul D.; Meijer, Erik

2005-01-01

Time series factor analysis (TSFA) and its associated statistical theory is developed. Unlike dynamic factor analysis (DFA), TSFA obviates the need for explicitly modeling the process dynamics of the underlying phenomena. It also differs from standard factor analysis (FA) in important respects: the

14. Seasonal time series forecasting: a comparative study of arima and ...

African Journals Online (AJOL)

This paper addresses the concerns of Faraway and Chatfield (1998) who questioned the forecasting ability of Artificial Neural Networks (ANN). In particular the paper compares the performance of Artificial Neural Networks (ANN) and ARIMA models in forecasting of seasonal (monthly) Time series. Using the Airline data ...

15. Palmprint Verification Using Time Series Method

Directory of Open Access Journals (Sweden)

A. A. Ketut Agung Cahyawan Wiranatha

2013-11-01

Full Text Available The use of biometrics as an automatic recognition system is growing rapidly in solving security problems, palmprint is one of biometric system which often used. This paper used two steps in center of mass moment method for region of interest (ROI segmentation and apply the time series method combined with block window method as feature representation. Normalized Euclidean Distance is used to measure the similarity degrees of two feature vectors of palmprint. System testing is done using 500 samples palms, with 4 samples as the reference image and the 6 samples as test images. Experiment results show that this system can achieve a high performance with success rate about 97.33% (FNMR=1.67%, FMR=1.00 %, T=0.036.

16. Learning and Prediction of Relational Time Series

Science.gov (United States)

2013-03-01

r S ub gr ap h Is om or ph is m (s ec ) Number of Constants in one situation Snort Dataset 1 & 2: Runtime over constant count Attention BFS 130...the scalability of the attention technique. 0 0.2 0.4 0.6 0.8 1 0 50 100 150 200 250 300 Ti m e pe r S ub gr ap h Is om or ph is m (s ec ) Number...φ, φ). Segment: A segment in the relational time-series r = p1p2…pn is comprised of the percept subsequence [ papa +1pa+2…pa+mpb) such that pa

17. On the C++ Object Programming for Time Series, in the Linux framework

OpenAIRE

Mateescu, George Daniel

2013-01-01

We study the implementation of time series trough C++ classes, using the fundamentals of C++ programming language, in the Linux framework. Such an implementation may be useful in time series modelling.

18. Time Series Prediction based on Hybrid Neural Networks

Directory of Open Access Journals (Sweden)

S. A. Yarushev

2016-01-01

Full Text Available In this paper, we suggest to use hybrid approach to time series forecasting problem. In first part of paper, we create a literature review of time series forecasting methods based on hybrid neural networks and neuro-fuzzy approaches. Hybrid neural networks especially effective for specific types of applications such as forecasting or classification problem, in contrast to traditional monolithic neural networks. These classes of problems include problems with different characteristics in different modules. The main part of paper create a detailed overview of hybrid networks benefits, its architectures and performance under traditional neural networks. Hybrid neural networks models for time series forecasting are discussed in the paper. Experiments with modular neural networks are given.

19. A Hidden Markov Models Approach for Crop Classification: Linking Crop Phenology to Time Series of Multi-Sensor Remote Sensing Data

Directory of Open Access Journals (Sweden)

Sofia Siachalou

2015-03-01

Full Text Available Vegetation monitoring and mapping based on multi-temporal imagery has recently received much attention due to the plethora of medium-high spatial resolution satellites and the improved classification accuracies attained compared to uni-temporal approaches. Efficient image processing strategies are needed to exploit the phenological information present in temporal image sequences and to limit data redundancy and computational complexity. Within this framework, we implement the theory of Hidden Markov Models in crop classification, based on the time-series analysis of phenological states, inferred by a sequence of remote sensing observations. More specifically, we model the dynamics of vegetation over an agricultural area of Greece, characterized by spatio-temporal heterogeneity and small-sized fields, using RapidEye and Landsat ETM+ imagery. In addition, the classification performance of image sequences with variable spatial and temporal characteristics is evaluated and compared. The classification model considering one RapidEye and four pan-sharpened Landsat ETM+ images was found superior, resulting in a conditional kappa from 0.77 to 0.94 per class and an overall accuracy of 89.7%. The results highlight the potential of the method for operational crop mapping in Euro-Mediterranean areas and provide some hints for optimal image acquisition windows regarding major crop types in Greece.

20. Estimation of pure autoregressive vector models for revenue series ...

African Journals Online (AJOL)

This paper aims at applying multivariate approach to Box and Jenkins univariate time series modeling to three vector series. General Autoregressive Vector Models with time varying coefficients are estimated. The first vector is a response vector, while others are predictor vectors. By matrix expansion each vector, whether ...

1. A methodology to filter time series: application to minute-by-minute electric load series

Directory of Open Access Journals (Sweden)

Mayte Suarez-Farinas

2004-12-01

Full Text Available In this article a methodology for filtering a time series is presented, with application to high frequency series such as the minute-by-minute electric load series. The goal of this approach is to detect and substitute the irregularities of the time series that can produce distortions on the modelling stage. Outlier values are detected through a dynamic linear model and the Bayes factor tool; missing values are then interpolated with a Smoothing Cubic Spline. The performance of the proposed approach is illustrated using real data and evaluated through a series of tests where the irregularities have been simulated.Neste artigo apresenta-se uma metodologia para a filtragem de séries temporais, com aplicação em séries de alta freqüência. Esta metodologia tem como objetivo detectar e substituir as irregularidades da série temporal que podem comprometer a etapa de modelagem. São detalhados o modelo linear dinâmico utilizado para detectar os valores outliers e o emprego do Fator de Bayes. Na interpolação de valores faltantes utiliza-se o Spline Cúbico Suavizado. O desempenho da metodologia proposta é avaliado a través de vários testes onde as irregularidade foram simuladas.

2. Investigation of market efficiency and Financial Stability between S&P 500 and London Stock Exchange: Monthly and yearly Forecasting of Time Series Stock Returns using ARMA model

Science.gov (United States)

2016-08-01

We investigated the presence and changes in, long memory features in the returns and volatility dynamics of S&P 500 and London Stock Exchange using ARMA model. Recently, multifractal analysis has been evolved as an important way to explain the complexity of financial markets which can hardly be described by linear methods of efficient market theory. In financial markets, the weak form of the efficient market hypothesis implies that price returns are serially uncorrelated sequences. In other words, prices should follow a random walk behavior. The random walk hypothesis is evaluated against alternatives accommodating either unifractality or multifractality. Several studies find that the return volatility of stocks tends to exhibit long-range dependence, heavy tails, and clustering. Because stochastic processes with self-similarity possess long-range dependence and heavy tails, it has been suggested that self-similar processes be employed to capture these characteristics in return volatility modeling. The present study applies monthly and yearly forecasting of Time Series Stock Returns in S&P 500 and London Stock Exchange using ARMA model. The statistical analysis of S&P 500 shows that the ARMA model for S&P 500 outperforms the London stock exchange and it is capable for predicting medium or long horizons using real known values. The statistical analysis in London Stock Exchange shows that the ARMA model for monthly stock returns outperforms the yearly. ​A comparison between S&P 500 and London Stock Exchange shows that both markets are efficient and have Financial Stability during periods of boom and bust.

3. Modeling In-Use Steel Stock in China’s Buildings and Civil Engineering Infrastructure Using Time-Series of DMSP/OLS Nighttime Lights

Directory of Open Access Journals (Sweden)

Hanwei Liang

2014-05-01

Full Text Available China’s rapid urbanization has led to increasing steel consumption for buildings and civil engineering infrastructure. The in-use steel stock in the same is considered to be closely related to social welfare and urban metabolism. Traditional approaches for determining the in-use steel stock are labor-intensive and time-consuming processes and always hindered by the availability of statistical data. To address this issue, this study proposed the use of long-term nighttime lights as a proxy to effectively estimate in-use steel stock for buildings (IUSSB and civil engineering infrastructure (IUSSCE at the provincial level in China. Significant relationships between nighttime lights versus IUSSB and IUSSCE were observed for provincial variables in a single year, as well as for time series variables of a single province. However, these relationships were found to differ among provinces (referred to as “inter-individual differences” and with time (referred to as “temporal differences”. Panel regression models were therefore proposed to estimate IUSSB and IUSSCE in consideration of the temporal and inter-individual differences based on a dataset covering 1992–2007. These models were validated using data for 2008, and the results showed good estimation for both IUSSB and IUSSCE. The proposed approach can be used to easily monitor the dynamic of IUSSB and IUSSCE in China. This should be critical in providing valuable information for policy making regarding regional development of buildings and infrastructure, sustainable urban resource management, and cross-boundary material recycling.

4. Analyzing the Potential Risk of Climate Change on Lyme Disease in Eastern Ontario, Canada Using Time Series Remotely Sensed Temperature Data and Tick Population Modelling

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Angela Cheng

2017-06-01

Full Text Available The number of Lyme disease cases (Lyme borreliosis in Ontario, Canada has increased over the last decade, and that figure is projected to continue to increase. The northern limit of Lyme disease cases has also been progressing northward from the northeastern United States into southeastern Ontario. Several factors such as climate change, changes in host abundance, host and vector migration, or possibly a combination of these factors likely contribute to the emergence of Lyme disease cases in eastern Ontario. This study first determined areas of warming using time series remotely sensed temperature data within Ontario, then analyzed possible spatial-temporal changes in Lyme disease risk in eastern Ontario from 2000 to 2013 due to climate change using tick population modeling. The outputs of the model were validated by using tick surveillance data from 2002 to 2012. Our results indicated areas in Ontario where Lyme disease risk changed from unsustainable to sustainable for sustaining Ixodes scapularis (black-legged tick populations. This study provides evidence that climate change has facilitated the northward expansion of black-legged tick populations’ geographic range over the past decade. The results demonstrate that remote sensing data can be used to increase the spatial detail for Lyme disease risk mapping and provide risk maps for better awareness of possible Lyme disease cases. Further studies are required to determine the contribution of host migration and abundance on changes in eastern Ontario’s Lyme disease risk.

5. Stochastic generation of hourly wind speed time series

International Nuclear Information System (INIS)

Shamshad, A.; Wan Mohd Ali Wan Hussin; Bawadi, M.A.; Mohd Sanusi, S.A.

2006-01-01

In the present study hourly wind speed data of Kuala Terengganu in Peninsular Malaysia are simulated by using transition matrix approach of Markovian process. The wind speed time series is divided into various states based on certain criteria. The next wind speed states are selected based on the previous states. The cumulative probability transition matrix has been formed in which each row ends with 1. Using the uniform random numbers between 0 and 1, a series of future states is generated. These states have been converted to the corresponding wind speed values using another uniform random number generator. The accuracy of the model has been determined by comparing the statistical characteristics such as average, standard deviation, root mean square error, probability density function and autocorrelation function of the generated data to those of the original data. The generated wind speed time series data is capable to preserve the wind speed characteristics of the observed data

6. A novel time series link prediction method: Learning automata approach

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2017-09-01

7. Time Series Analysis of Hand-Foot-Mouth Disease Hospitalization in Zhengzhou: Establishment of Forecasting Models Using Climate Variables as Predictors

Science.gov (United States)

Feng, Huifen; Duan, Guangcai; Zhang, Rongguang; Zhang, Weidong

2014-01-01

Background Large-scale outbreaks of hand-foot-mouth disease (HFMD) have occurred frequently and caused neurological sequelae in mainland China since 2008. Prediction of the activity of HFMD epidemics a few weeks ahead is useful in taking preventive measures for efficient HFMD control. Methods Samples obtained from children hospitalized with HFMD in Zhengzhou, Henan, China, were examined for the existence of pathogens with reverse-transcriptase polymerase chain reaction (RT-PCR) from 2008 to 2012. Seasonal Autoregressive Integrated Moving Average (SARIMA) models for the weekly number of HFMD, Human enterovirs 71(HEV71) and CoxsackievirusA16 (CoxA16) associated HFMD were developed and validated. Cross correlation between the number of HFMD hospitalizations and climatic variables was computed to identify significant variables to be included as external factors. Time series modeling was carried out using multivariate SARIMA models when there was significant predictor meteorological variable. Results 2932 samples from the patients hospitalized with HFMD, 748 were detected with HEV71, 527 with CoxA16 and 787 with other enterovirus (other EV) from January 2008 to June 2012. Average atmospheric temperature (T{avg}) lagged at 2 or 3 weeks were identified as significant predictors for the number of HFMD and the pathogens. SARIMA(0,1,0)(1,0,0)52 associated with T{avg} at lag 2 (T{avg}-Lag 2) weeks, SARIMA(0,1,2)(1,0,0)52 with T{avg}-Lag 2 weeks and SARIMA(0,1,1)(1,1,0)52 with T{avg}-Lag 3 weeks were developed and validated for description and predication the weekly number of HFMD, HEV71-associated HFMD, and Cox A16-associated HFMD hospitalizations. Conclusion Seasonal pattern of certain HFMD pathogens can be associated by meteorological factors. The SARIMA model including climatic variables could be used as an early and reliable monitoring system to predict annual HFMD epidemics. PMID:24498221

8. An attempt at solving the problem of autocorrelation associated with use of mean approach for pooling cross-section and time series in regression modelling

International Nuclear Information System (INIS)

Nuamah, N.N.N.N.

1990-12-01

The paradoxical nature of results of the mean approach in pooling cross-section and time series data has been identified to be caused by the presence in the normal equations of phenomena such as autocovariances, multicollinear covariances, drift covariances and drift multicollinear covariances. This paper considers the problem of autocorrelation and suggests ways of solving it. (author). 4 refs

9. Climate Prediction Center (CPC) Global Precipitation Time Series

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The global precipitation time series provides time series charts showing observations of daily precipitation as well as accumulated precipitation compared to normal...

10. Climate Prediction Center (CPC) Global Temperature Time Series

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The global temperature time series provides time series charts using station based observations of daily temperature. These charts provide information about the...

11. Transmission of linear regression patterns between time series: from relationship in time series to complex networks.

Science.gov (United States)

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

2014-07-01

The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

12. Multiple imputation for time series data with Amelia package.

Science.gov (United States)

Zhang, Zhongheng

2016-02-01

Time series data are common in medical researches. Many laboratory variables or study endpoints could be measured repeatedly over time. Multiple imputation (MI) without considering time trend of a variable may cause it to be unreliable. The article illustrates how to perform MI by using Amelia package in a clinical scenario. Amelia package is powerful in that it allows for MI for time series data. External information on the variable of interest can also be incorporated by using prior or bound argument. Such information may be based on previous published observations, academic consensus, and personal experience. Diagnostics of imputation model can be performed by examining the distributions of imputed and observed values, or by using over-imputation technique.

13. Long Term Subsidence Analysis and Soil Fracturing Zonation Based on InSAR Time Series Modelling in Northern Zona Metropolitana del Valle de Mexico

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Gabriela Llanet Siles

2015-05-01

Full Text Available In this study deformation processes in northern Zona Metropolitana del Valle de Mexico (ZMVM are evaluated by means of advanced multi-temporal interferometry. ERS and ENVISAT time series, covering approximately an 11-year period (between 1999 and 2010, were produced showing mainly linear subsidence behaviour for almost the entire area under study, but increasing rates that reach up to 285 mm/yr. Important non-linear deformation was identified in certain areas, presumably suggesting interaction between subsidence and other processes. Thus, a methodology for identification of probable fracturing zones based on discrimination and modelling of the non-linear (quadratic function component is presented. This component was mapped and temporal subsidence evolution profiles were constructed across areas where notable acceleration (maximum of 8 mm/yr2 or deceleration (maximum of −9 mm/yr2 is found. This methodology enables location of potential soil fractures that could impact relevant infrastructure such as the Tunel Emisor Oriente (TEO (along the structure rates exceed 200 mm/yr. Additionally, subsidence behaviour during wet and dry seasons is tackled in partially urbanized areas. This paper provides useful information for geological risk assessment in the area.

14. Meta-analysis and time series modeling allow a systematic review of primary HIV-1 drug-resistant prevalence in Latin America and Caribbean.

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Coelho, Antonio Victor Campos; De Moura, Ronald Rodrigues; Da Silva, Ronaldo Celerino; Kamada, Anselmo Jiro; Guimarães, Rafael Lima; Brandão, Lucas André Cavalcanti; Coelho, Hemílio Fernandes Campos; Crovella, Sergio

2015-01-01

Here we review the prevalence of HIV-1 primary drug resistance in Latin America and Caribbean using meta-analysis as well as time-series modeling. We also discuss whether there could be a drawback to HIV/AIDS programs due to drug resistance in Latin America and Caribbean in the next years. We observed that, although some studies report low or moderate primary drug resistance prevalence in Caribbean countries, this evidence needs to be updated. In other countries, such as Brazil and Argentina, the prevalence of drug resistance appears to be rising. Mutations conferring resistance against reverse transcriptase inhibitors were the most frequent in the analyzed populations (70% of all mutational events). HIV-1 subtype B was the most prevalent in Latin America and the Caribbean, although subtype C and B/F recombinants have significant contributions in Argentina and Brazil. Thus, we suggest that primary drug resistance in Latin America and the Caribbean could have been underestimated. Clinical monitoring should be improved to offer better therapy, reducing the risk for HIV-1 resistance emergence and spread, principally in vulnerable populations, such as men who have sex with men transmission group, sex workers and intravenous drug users.

15. The complexity of carbon flux time series in Europe

Science.gov (United States)

Lange, Holger; Sippel, Sebastian

2014-05-01

Observed geophysical time series usually exhibit pronounced variability, part of which is process-related and deterministic ("signal"), another part is due to random fluctuations ("noise"). To discern these two sources for fluctuations is notoriously difficult using conventional analysis methods, unless sophisticated model assumptions are made. Here, we present an almost parameter-free innovative approach with the potential to draw a distinction between deterministic processes and structured noise, based on ordinal pattern statistics. The method determines one measure for the information content of time series (Shannon entropy) and two complexity measures, one based on global properties of the order pattern distribution (Jensen-Shannon complexity) and one based on local (derivative) properties (Fisher information or complexity). Each time series gets classified via its location in an entropy-complexity plane; using this representation, the method draws a qualitative distinction between different types of natural processes. As a case study, we investigate Gross Primary Productivity (GPP) and respiration which are key variables in terrestrial ecosystems quantifying carbon allocation and biomass growth of vegetation. Changes in GPP and ecosystem respiration can be induced by land use change, environmental disasters or extreme events, and changing climate. Numerous attempts to quantify these variables on larger spatial scales exist. Here, we investigate gridded time series at monthly resolution for the European continent either based on upscaled measurements ("observations") or modelled with two different process-based terrestrial ecosystem models ("simulations"). The complexity analysis is either visualized as maps of Europe showing "hotspots" of complexity for GPP and respiration, or used to provide a detailed observations-simulations and model-model comparison. Values found for information and complexity will be compared to known artificial reference processes

16. Notes on economic time series analysis system theoretic perspectives

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Aoki, Masanao

1983-01-01

In seminars and graduate level courses I have had several opportunities to discuss modeling and analysis of time series with economists and economic graduate students during the past several years. These experiences made me aware of a gap between what economic graduate students are taught about vector-valued time series and what is available in recent system literature. Wishing to fill or narrow the gap that I suspect is more widely spread than my personal experiences indicate, I have written these notes to augment and reor­ ganize materials I have given in these courses and seminars. I have endeavored to present, in as much a self-contained way as practicable, a body of results and techniques in system theory that I judge to be relevant and useful to economists interested in using time series in their research. I have essentially acted as an intermediary and interpreter of system theoretic results and perspectives in time series by filtering out non-essential details, and presenting coherent accounts of wha...

17. Seasonality of Tuberculosis in Delhi, India: A Time Series Analysis

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Varun Kumar

2014-01-01

Full Text Available Background. It is highly cost effective to detect a seasonal trend in tuberculosis in order to optimize disease control and intervention. Although seasonal variation of tuberculosis has been reported from different parts of the world, no definite and consistent pattern has been observed. Therefore, the study was designed to find the seasonal variation of tuberculosis in Delhi, India. Methods. Retrospective record based study was undertaken in a Directly Observed Treatment Short course (DOTS centre located in the south district of Delhi. Six-year data from January 2007 to December 2012 was analyzed. Expert modeler of SPSS ver. 21 software was used to fit the best suitable model for the time series data. Results. Autocorrelation function (ACF and partial autocorrelation function (PACF at lag 12 show significant peak suggesting seasonal component of the TB series. Seasonal adjusted factor (SAF showed peak seasonal variation from March to May. Univariate model by expert modeler in the SPSS showed that Winter’s multiplicative model could best predict the time series data with 69.8% variability. The forecast shows declining trend with seasonality. Conclusion. A seasonal pattern and declining trend with variable amplitudes of fluctuation were observed in the incidence of tuberculosis.

18. Seasonality of tuberculosis in delhi, India: a time series analysis.

Science.gov (United States)

Kumar, Varun; Singh, Abhay; Adhikary, Mrinmoy; Daral, Shailaja; Khokhar, Anita; Singh, Saudan

2014-01-01

Background. It is highly cost effective to detect a seasonal trend in tuberculosis in order to optimize disease control and intervention. Although seasonal variation of tuberculosis has been reported from different parts of the world, no definite and consistent pattern has been observed. Therefore, the study was designed to find the seasonal variation of tuberculosis in Delhi, India. Methods. Retrospective record based study was undertaken in a Directly Observed Treatment Short course (DOTS) centre located in the south district of Delhi. Six-year data from January 2007 to December 2012 was analyzed. Expert modeler of SPSS ver. 21 software was used to fit the best suitable model for the time series data. Results. Autocorrelation function (ACF) and partial autocorrelation function (PACF) at lag 12 show significant peak suggesting seasonal component of the TB series. Seasonal adjusted factor (SAF) showed peak seasonal variation from March to May. Univariate model by expert modeler in the SPSS showed that Winter's multiplicative model could best predict the time series data with 69.8% variability. The forecast shows declining trend with seasonality. Conclusion. A seasonal pattern and declining trend with variable amplitudes of fluctuation were observed in the incidence of tuberculosis.

19. Sequential Monte Carlo for inference of latent ARMA time-series with innovations correlated in time

Science.gov (United States)

Urteaga, Iñigo; Bugallo, Mónica F.; Djurić, Petar M.

2017-12-01

We consider the problem of sequential inference of latent time-series with innovations correlated in time and observed via nonlinear functions. We accommodate time-varying phenomena with diverse properties by means of a flexible mathematical representation of the data. We characterize statistically such time-series by a Bayesian analysis of their densities. The density that describes the transition of the state from time t to the next time instant t+1 is used for implementation of novel sequential Monte Carlo (SMC) methods. We present a set of SMC methods for inference of latent ARMA time-series with innovations correlated in time for different assumptions in knowledge of parameters. The methods operate in a unified and consistent manner for data with diverse memory properties. We show the validity of the proposed approach by comprehensive simulations of the challenging stochastic volatility model.

20. Financial Time Series Prediction Using Elman Recurrent Random Neural Networks

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Jie Wang

2016-01-01

(ERNN, the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices.