75 FR 57880 - Airworthiness Directives; Airbus Model A310 Series Airplanes
2010-09-23
... docket. Relevant Service Information Airbus has issued Mandatory Service Bulletin A310-27-2099, Revision... Service Bulletin A310-27-2099, dated February 17, 2006; or Airbus Mandatory Service Bulletin A310-27-2099...- 2099, Revision 01, dated March 21, 2008. Note 1: For the purposes of this AD, a detailed inspection is...
2011-06-22
... pieces unit with 3 welds (old design) as pictured in Appendix 1 of this [EASA] AD. The welding process of... pre-modification 02447.. Blue and Green. A300 airplanes post-modification 02447. Blue. A300-600 airplanes Blue. A310 airplanes Green. Table 2--Applicable service information Airbus mandatory service...
2010-02-23
... Airworthiness Directives; Airbus Model A310-203, -221, -222 Airplanes; and Model A300 F4-605R and -622R...-222 airplanes, all serial numbers. (2) Airbus Model A300 F4-605R and A300 F4-622R airplanes, all...
75 FR 39863 - Airworthiness Directives; Airbus Model A310 Series Airplanes
2010-07-13
... condition as: Analysis performed in the frame of the Extended Service Goal has led Airbus to modify the... performed in the frame of the Extended Service Goal has led Airbus to modify the inspection programme...) states: Analysis performed in the frame of the Extended Service Goal has led Airbus to modify the...
76 FR 27227 - Airworthiness Directives; Airbus Model A310 Series Airplanes
2011-05-11
... program, EASA [European Aviation Safety Agency] published AD 2007-0053, which replaced DGAC France AD F... airplane configuration: Cold working of trellis boom drainage holes. Repetitive detailed or rotating probe... take about 137 work-hours per product to comply with the basic requirements of this AD. The average...
76 FR 27232 - Airworthiness Directives; Airbus Model A310 Series Airplanes
2011-05-11
... directive (AD) for the products listed above that would supersede two existing ADs. This AD results from mandatory continuing airworthiness information (MCAI) originated by an aviation authority of another country... product. Authority for This Rulemaking Title 49 of the United States Code specifies the FAA's authority to...
76 FR 27220 - Airworthiness Directives; Airbus Model A310 Series Airplanes
2011-05-11
... cracking in certain bolt holes where the main landing gear forward pick-up fitting is attached to the rear... labor rate is $85 per work-hour. Based on these figures, we estimate the cost of this AD to the U.S... government and the States, or on the distribution of power and responsibilities among the various levels of...
76 FR 42 - Airworthiness Directives; Airbus Model A310 Series Airplanes
2011-01-03
... may send comments by any of the following methods: Federal eRulemaking Portal: Go to http://www... 1992-106-132(B)R4, dated June 5, 1996]. Following the Extended Design Service Goal activities part of... inspection to detect cracks in the area of frame 47 and frame 54, install new doublers, and repair if...
2010-05-21
... Airworthiness Directives; Airbus Model A300 Series Airplanes; Model A300 B4-600, B4-600R, F4-600R Series..., B4-622, B4- 605R, B4-622R, F4-605R, F4-622R, and C4-605R Variant F airplanes; and Model A310-203...
Introduction to Time Series Modeling
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
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)
2010-01-01
... 15 Commerce and Foreign Trade 1 2010-01-01 2010-01-01 false Recruitment. 8a.310 Section 8a.310... in Admission and Recruitment Prohibited § 8a.310 Recruitment. (a) Nondiscriminatory recruitment. A... recruitment and admission of students. A recipient may be required to undertake additional recruitment efforts...
Models for dependent time series
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
2011-04-08
... B4-600, B4-600R, and F4-600R Series Airplanes, and Model C4-605R Variant F Airplanes (Collectively... F4-605R and F4-622R airplanes, and Model A300 C4-605R Variant F airplanes; and Model A310-203, -204...
Stochastic models for time series
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 ...
FOURIER SERIES MODELS THROUGH TRANSFORMATION
African Journals Online (AJOL)
DEPT
monthly temperature data (1996 – 2005) collected from the National Root ... KEY WORDS: Fourier series, square transformation, multiplicative model, ... fluctuations or movements are often periodic(Ekpeyong,2005). .... significant trend or not, if the trend is not significant, the grand mean may be used as an estimate of trend.
Multiple Indicator Stationary Time Series Models.
Sivo, Stephen A.
2001-01-01
Discusses the propriety and practical advantages of specifying multivariate time series models in the context of structural equation modeling for time series and longitudinal panel data. For time series data, the multiple indicator model specification improves on classical time series analysis. For panel data, the multiple indicator model…
2011-05-04
... Airworthiness Directives; Airbus Model A300 B4-600, A300 B4-600R, and A300 F4-600R Series Airplanes, and Model...-605R, B4-622R, F4-605R, F4-622R, and C4-605R Variant F airplanes; and Model A310-203, -204, -221, -222...
2011-08-05
... Airworthiness Directives; Airbus Model A300 B4-600, A300 B4-600R, and A300 F4-600R Series Airplanes, and Model..., B4-622R, F4-605R, F4-622R, and C4-605R Variant F airplanes; and Model A310-203, -204, -221, -222...
2011-01-25
... after receipt. FOR FURTHER INFORMATION CONTACT: Dan Rodina, Aerospace Engineer, International Branch... ensure the continued damage tolerance of the affected structure. The FAA has provided guidance for this... after April 3, 2007. Corrective Actions (j) Damaged, cracked, or corroded structure detected during any...
2011-05-13
... Jersey Avenue, SE., Washington, DC. FOR FURTHER INFORMATION CONTACT: Dan Rodina, Aerospace Engineer... the required inspections that will ensure the continued damage tolerance of the affected structure... corroded structure detected during any inspection done in accordance with the Airbus A300 Airworthiness...
2011-04-21
... bulletins were revised for minor changes such as deleting THS zeroing in job set-up and deleting the THSA... this suspect gimbal could be on another unit is if it was swapped from one unit to another in the shop...
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...
Time series modeling, computation, and inference
Prado, Raquel
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
Modelling conditional heteroscedasticity in nonstationary series
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
Modeling seasonality in bimonthly time series
Ph.H.B.F. Franses (Philip Hans)
1992-01-01
textabstractA recurring issue in modeling seasonal time series variables is the choice of the most adequate model for the seasonal movements. One selection method for quarterly data is proposed in Hylleberg et al. (1990). Market response models are often constructed for bimonthly variables, and
Modeling of Volatility with Non-linear Time Series Model
Kim Song Yon; Kim Mun Chol
2013-01-01
In this paper, non-linear time series models are used to describe volatility in financial time series data. To describe volatility, two of the non-linear time series are combined into form TAR (Threshold Auto-Regressive Model) with AARCH (Asymmetric Auto-Regressive Conditional Heteroskedasticity) error term and its parameter estimation is studied.
Bootstrapping a time series model
International Nuclear Information System (INIS)
Son, M.S.
1984-01-01
The bootstrap is a methodology for estimating standard errors. The idea is to use a Monte Carlo simulation experiment based on a nonparametric estimate of the error distribution. The main objective of this dissertation was to demonstrate the use of the bootstrap to attach standard errors to coefficient estimates and multi-period forecasts in a second-order autoregressive model fitted by least squares and maximum likelihood estimation. A secondary objective of this article was to present the bootstrap in the context of two econometric equations describing the unemployment rate and individual income tax in the state of Oklahoma. As it turns out, the conventional asymptotic formulae (both the least squares and maximum likelihood estimates) for estimating standard errors appear to overestimate the true standard errors. But there are two problems: 1) the first two observations y 1 and y 2 have been fixed, and 2) the residuals have not been inflated. After these two factors are considered in the trial and bootstrap experiment, both the conventional maximum likelihood and bootstrap estimates of the standard errors appear to be performing quite well. At present, there does not seem to be a good rule of thumb for deciding when the conventional asymptotic formulae will give acceptable results
Time series modeling in traffic safety research.
Lavrenz, Steven M; Vlahogianni, Eleni I; Gkritza, Konstantina; Ke, Yue
2018-08-01
The use of statistical models for analyzing traffic safety (crash) data has been well-established. However, time series techniques have traditionally been underrepresented in the corresponding literature, due to challenges in data collection, along with a limited knowledge of proper methodology. In recent years, new types of high-resolution traffic safety data, especially in measuring driver behavior, have made time series modeling techniques an increasingly salient topic of study. Yet there remains a dearth of information to guide analysts in their use. This paper provides an overview of the state of the art in using time series models in traffic safety research, and discusses some of the fundamental techniques and considerations in classic time series modeling. It also presents ongoing and future opportunities for expanding the use of time series models, and explores newer modeling techniques, including computational intelligence models, which hold promise in effectively handling ever-larger data sets. The information contained herein is meant to guide safety researchers in understanding this broad area of transportation data analysis, and provide a framework for understanding safety trends that can influence policy-making. Copyright © 2017 Elsevier Ltd. All rights reserved.
Building Chaotic Model From Incomplete Time Series
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
Forecasting with nonlinear time series models
DEFF Research Database (Denmark)
Kock, Anders Bredahl; Teräsvirta, Timo
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...... 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...... and two versions of a simple artificial neural network model. Techniques for generating multi-period forecasts from nonlinear models recursively are considered, and the direct (non-recursive) method for this purpose is mentioned as well. Forecasting with com- plex dynamic systems, albeit less frequently...
Modeling vector nonlinear time series using POLYMARS
de Gooijer, J.G.; Ray, B.K.
2003-01-01
A modified multivariate adaptive regression splines method for modeling vector nonlinear time series is investigated. The method results in models that can capture certain types of vector self-exciting threshold autoregressive behavior, as well as provide good predictions for more general vector
Forecasting with periodic autoregressive time series models
Ph.H.B.F. Franses (Philip Hans); R. Paap (Richard)
1999-01-01
textabstractThis paper is concerned with forecasting univariate seasonal time series data using periodic autoregressive models. We show how one should account for unit roots and deterministic terms when generating out-of-sample forecasts. We illustrate the models for various quarterly UK consumption
On modeling panels of time series
Ph.H.B.F. Franses (Philip Hans)
2002-01-01
textabstractThis paper reviews research issues in modeling panels of time series. Examples of this type of data are annually observed macroeconomic indicators for all countries in the world, daily returns on the individual stocks listed in the S&P500, and the sales records of all items in a
Time Series Modelling using Proc Varmax
DEFF Research Database (Denmark)
Milhøj, Anders
2007-01-01
In this paper it will be demonstrated how various time series problems could be met using Proc Varmax. The procedure is rather new and hence new features like cointegration, testing for Granger causality are included, but it also means that more traditional ARIMA modelling as outlined by Box...
Outlier Detection in Structural Time Series Models
DEFF Research Database (Denmark)
Marczak, Martyna; Proietti, Tommaso
investigate via Monte Carlo simulations how this approach performs for detecting additive outliers and level shifts in the analysis of nonstationary seasonal time series. The reference model is the basic structural model, featuring a local linear trend, possibly integrated of order two, stochastic seasonality......Structural change affects the estimation of economic signals, like the underlying growth rate or the seasonally adjusted series. An important issue, which has attracted a great deal of attention also in the seasonal adjustment literature, is its detection by an expert procedure. The general......–to–specific approach to the detection of structural change, currently implemented in Autometrics via indicator saturation, has proven to be both practical and effective in the context of stationary dynamic regression models and unit–root autoregressions. By focusing on impulse– and step–indicator saturation, we...
Modeling Non-Gaussian Time Series with Nonparametric Bayesian Model.
Xu, Zhiguang; MacEachern, Steven; Xu, Xinyi
2015-02-01
We present a class of Bayesian copula models whose major components are the marginal (limiting) distribution of a stationary time series and the internal dynamics of the series. We argue that these are the two features with which an analyst is typically most familiar, and hence that these are natural components with which to work. For the marginal distribution, we use a nonparametric Bayesian prior distribution along with a cdf-inverse cdf transformation to obtain large support. For the internal dynamics, we rely on the traditionally successful techniques of normal-theory time series. Coupling the two components gives us a family of (Gaussian) copula transformed autoregressive models. The models provide coherent adjustments of time scales and are compatible with many extensions, including changes in volatility of the series. We describe basic properties of the models, show their ability to recover non-Gaussian marginal distributions, and use a GARCH modification of the basic model to analyze stock index return series. The models are found to provide better fit and improved short-range and long-range predictions than Gaussian competitors. The models are extensible to a large variety of fields, including continuous time models, spatial models, models for multiple series, models driven by external covariate streams, and non-stationary models.
Estimating High-Dimensional Time Series Models
DEFF Research Database (Denmark)
Medeiros, Marcelo C.; Mendes, Eduardo F.
We study the asymptotic properties of the Adaptive LASSO (adaLASSO) in sparse, high-dimensional, linear time-series models. We assume both the number of covariates in the model and candidate variables can increase with the number of observations and the number of candidate variables is, possibly......, larger than the number of observations. We show the adaLASSO consistently chooses the relevant variables as the number of observations increases (model selection consistency), and has the oracle property, even when the errors are non-Gaussian and conditionally heteroskedastic. A simulation study shows...
From Taylor series to Taylor models
International Nuclear Information System (INIS)
Berz, Martin
1997-01-01
An overview of the background of Taylor series methods and the utilization of the differential algebraic structure is given, and various associated techniques are reviewed. The conventional Taylor methods are extended to allow for a rigorous treatment of bounds for the remainder of the expansion in a similarly universal way. Utilizing differential algebraic and functional analytic arguments on the set of Taylor models, arbitrary order integrators with rigorous remainder treatment are developed. The integrators can meet pre-specified accuracy requirements in a mathematically strict way, and are a stepping stone towards fully rigorous estimates of stability of repetitive systems
From Taylor series to Taylor models
International Nuclear Information System (INIS)
Berz, M.
1997-01-01
An overview of the background of Taylor series methods and the utilization of the differential algebraic structure is given, and various associated techniques are reviewed. The conventional Taylor methods are extended to allow for a rigorous treatment of bounds for the remainder of the expansion in a similarly universal way. Utilizing differential algebraic and functional analytic arguments on the set of Taylor models, arbitrary order integrators with rigorous remainder treatment are developed. The integrators can meet pre-specified accuracy requirements in a mathematically strict way, and are a stepping stone towards fully rigorous estimates of stability of repetitive systems. copyright 1997 American Institute of Physics
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
Fisher information framework for time series modeling
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.
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.
Foundations of Sequence-to-Sequence Modeling for Time Series
Kuznetsov, Vitaly; Mariet, Zelda
2018-01-01
The availability of large amounts of time series data, paired with the performance of deep-learning algorithms on a broad class of problems, has recently led to significant interest in the use of sequence-to-sequence models for time series forecasting. We provide the first theoretical analysis of this time series forecasting framework. We include a comparison of sequence-to-sequence modeling to classical time series models, and as such our theory can serve as a quantitative guide for practiti...
Neural Network Models for Time Series Forecasts
Tim Hill; Marcus O'Connor; William Remus
1996-01-01
Neural networks have been advocated as an alternative to traditional statistical forecasting methods. In the present experiment, time series forecasts produced by neural networks are compared with forecasts from six statistical time series methods generated in a major forecasting competition (Makridakis et al. [Makridakis, S., A. Anderson, R. Carbone, R. Fildes, M. Hibon, R. Lewandowski, J. Newton, E. Parzen, R. Winkler. 1982. The accuracy of extrapolation (time series) methods: Results of a ...
Modeling Time Series Data for Supervised Learning
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…
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.
Hidden Markov Models for Time Series An Introduction Using R
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.
Modelling road accidents: An approach using structural time series
Junus, Noor Wahida Md; Ismail, Mohd Tahir
2014-09-01
In this paper, the trend of road accidents in Malaysia for the years 2001 until 2012 was modelled using a structural time series approach. The structural time series model was identified using a stepwise method, and the residuals for each model were tested. The best-fitted model was chosen based on the smallest Akaike Information Criterion (AIC) and prediction error variance. In order to check the quality of the model, a data validation procedure was performed by predicting the monthly number of road accidents for the year 2012. Results indicate that the best specification of the structural time series model to represent road accidents is the local level with a seasonal model.
Fourier Series, the DFT and Shape Modelling
DEFF Research Database (Denmark)
Skoglund, Karl
2004-01-01
This report provides an introduction to Fourier series, the discrete Fourier transform, complex geometry and Fourier descriptors for shape analysis. The content is aimed at undergraduate and graduate students who wish to learn about Fourier analysis in general, as well as its application to shape...
Trend time-series modeling and forecasting with neural networks.
Qi, Min; Zhang, G Peter
2008-05-01
Despite its great importance, there has been no general consensus on how to model the trends in time-series data. Compared to traditional approaches, neural networks (NNs) have shown some promise in time-series forecasting. This paper investigates how to best model trend time series using NNs. Four different strategies (raw data, raw data with time index, detrending, and differencing) are used to model various trend patterns (linear, nonlinear, deterministic, stochastic, and breaking trend). We find that with NNs differencing often gives meritorious results regardless of the underlying data generating processes (DGPs). This finding is also confirmed by the real gross national product (GNP) series.
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
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.
Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models
Price, Larry R.
2012-01-01
The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…
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 ...
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...
DEFF Research Database (Denmark)
Madsen, Henrik; Rasmussen, Peter F.; Rosbjerg, Dan
1997-01-01
Two different models for analyzing extreme hydrologic events, based on, respectively, partial duration series (PDS) and annual maximum series (AMS), are compared. The PDS model assumes a generalized Pareto distribution for modeling threshold exceedances corresponding to a generalized extreme value......). In the case of ML estimation, the PDS model provides the most efficient T-year event estimator. In the cases of MOM and PWM estimation, the PDS model is generally preferable for negative shape parameters, whereas the AMS model yields the most efficient estimator for positive shape parameters. A comparison...... of the considered methods reveals that in general, one should use the PDS model with MOM estimation for negative shape parameters, the PDS model with exponentially distributed exceedances if the shape parameter is close to zero, the AMS model with MOM estimation for moderately positive shape parameters, and the PDS...
Time series sightability modeling of animal populations.
ArchMiller, Althea A; Dorazio, Robert M; 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.
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...... and available storage capacity in the pipe as well as predictions of future states. A linear overflow relation is found, differing significantly from the traditional modelling approach. This is due to complicated overflow structures in a hydraulic sense where the overflow is governed by inertia from the inflow...... 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...
Time series sightability modeling of animal populations
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.
The use of synthetic input sequences in time series modeling
International Nuclear Information System (INIS)
Oliveira, Dair Jose de; Letellier, Christophe; Gomes, Murilo E.D.; Aguirre, Luis A.
2008-01-01
In many situations time series models obtained from noise-like data settle to trivial solutions under iteration. This Letter proposes a way of producing a synthetic (dummy) input, that is included to prevent the model from settling down to a trivial solution, while maintaining features of the original signal. Simulated benchmark models and a real time series of RR intervals from an ECG are used to illustrate the procedure
Fourier series models through transformation | Omekara | Global ...
African Journals Online (AJOL)
As a result, the square transformation which outperforms the others is adopted. Consequently, each of the multiplicative and additive FSA models fitted to the transformed data are then subjected to a test for white noise based on spectral analysis. The result of this test shows that only the multiplicative model is adequate.
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.
Long Memory Models to Generate Synthetic Hydrological Series
Directory of Open Access Journals (Sweden)
Guilherme Armando de Almeida Pereira
2014-01-01
Full Text Available In Brazil, much of the energy production comes from hydroelectric plants whose planning is not trivial due to the strong dependence on rainfall regimes. This planning is accomplished through optimization models that use inputs such as synthetic hydrologic series generated from the statistical model PAR(p (periodic autoregressive. Recently, Brazil began the search for alternative models able to capture the effects that the traditional model PAR(p does not incorporate, such as long memory effects. Long memory in a time series can be defined as a significant dependence between lags separated by a long period of time. Thus, this research develops a study of the effects of long dependence in the series of streamflow natural energy in the South subsystem, in order to estimate a long memory model capable of generating synthetic hydrologic series.
Tempered fractional time series model for turbulence in geophysical flows
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.
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)
forecasting with nonlinear time series model: a monte-carlo
African Journals Online (AJOL)
PUBLICATIONS1
Carlo method of forecasting using a special nonlinear time series model, called logistic smooth transition ... We illustrate this new method using some simulation ..... in MATLAB 7.5.0. ... process (DGP) using the logistic smooth transi-.
Structural Equation Modeling of Multivariate Time Series
du Toit, Stephen H. C.; Browne, Michael W.
2007-01-01
The covariance structure of a vector autoregressive process with moving average residuals (VARMA) is derived. It differs from other available expressions for the covariance function of a stationary VARMA process and is compatible with current structural equation methodology. Structural equation modeling programs, such as LISREL, may therefore be…
LPV system identification using series expansion models
Toth, R.; Heuberger, P.S.C.; Hof, Van den P.M.J.; Santos, dos P.L.; Perdicoúlis, T.P.A.; Novara, C.; Ramos, J.A.; Rivera, D.E.
2011-01-01
This review volume reports the state-of-the-art in Linear Parameter Varying (LPV) system identification. Written by world renowned researchers, the book contains twelve chapters, focusing on the most recent LPV identification methods for both discrete-time and continuous-time models, using different
Forecasting daily meteorological time series using ARIMA and regression models
Murat, Małgorzata; Malinowska, Iwona; Gos, Magdalena; Krzyszczak, Jaromir
2018-04-01
The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.
With string model to time series forecasting
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.
Koopman Operator Framework for Time Series Modeling and Analysis
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.
Stochastic modeling of hourly rainfall times series in Campania (Italy)
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
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.
Small-signal model for the series resonant converter
King, R. J.; Stuart, T. A.
1985-01-01
The results of a previous discrete-time model of the series resonant dc-dc converter are reviewed and from these a small signal dynamic model is derived. This model is valid for low frequencies and is based on the modulation of the diode conduction angle for control. The basic converter is modeled separately from its output filter to facilitate the use of these results for design purposes. Experimental results are presented.
Identification of human operator performance models utilizing time series analysis
Holden, F. M.; Shinners, S. M.
1973-01-01
The results of an effort performed by Sperry Systems Management Division for AMRL in applying time series analysis as a tool for modeling the human operator are presented. This technique is utilized for determining the variation of the human transfer function under various levels of stress. The human operator's model is determined based on actual input and output data from a tracking experiment.
RADON CONCENTRATION TIME SERIES MODELING AND APPLICATION DISCUSSION.
Stránský, V; Thinová, L
2017-11-01
In the year 2010 a continual radon measurement was established at Mladeč Caves in the Czech Republic using a continual radon monitor RADIM3A. In order to model radon time series in the years 2010-15, the Box-Jenkins Methodology, often used in econometrics, was applied. Because of the behavior of radon concentrations (RCs), a seasonal integrated, autoregressive moving averages model with exogenous variables (SARIMAX) has been chosen to model the measured time series. This model uses the time series seasonality, previously acquired values and delayed atmospheric parameters, to forecast RC. The developed model for RC time series is called regARIMA(5,1,3). Model residuals could be retrospectively compared with seismic evidence of local or global earthquakes, which occurred during the RCs measurement. This technique enables us to asses if continuously measured RC could serve an earthquake precursor. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
High-temperature series expansions for random Potts models
Directory of Open Access Journals (Sweden)
M.Hellmund
2005-01-01
Full Text Available We discuss recently generated high-temperature series expansions for the free energy and the susceptibility of random-bond q-state Potts models on hypercubic lattices. Using the star-graph expansion technique, quenched disorder averages can be calculated exactly for arbitrary uncorrelated coupling distributions while keeping the disorder strength p as well as the dimension d as symbolic parameters. We present analyses of the new series for the susceptibility of the Ising (q=2 and 4-state Potts model in three dimensions up to the order 19 and 18, respectively, and compare our findings with results from field-theoretical renormalization group studies and Monte Carlo simulations.
New series of 3 D lattice integrable models
International Nuclear Information System (INIS)
Mangazeev, V.V.; Sergeev, S.M.; Stroganov, Yu.G.
1993-01-01
In this paper we present a new series of 3-dimensional integrable lattice models with N colors. The weight functions of the models satisfy modified tetrahedron equations with N states and give a commuting family of two-layer transfer-matrices. The dependence on the spectral parameters corresponds to the static limit of the modified tetrahedron equations and weights are parameterized in terms of elliptic functions. The models contain two free parameters: elliptic modulus and additional parameter η. 12 refs
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....
Single-Index Additive Vector Autoregressive Time Series Models
LI, YEHUA; GENTON, MARC G.
2009-01-01
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
Book Review: "Hidden Markov Models for Time Series: An ...
African Journals Online (AJOL)
Hidden Markov Models for Time Series: An Introduction using R. by Walter Zucchini and Iain L. MacDonald. Chapman & Hall (CRC Press), 2009. Full Text: EMAIL FULL TEXT EMAIL FULL TEXT · DOWNLOAD FULL TEXT DOWNLOAD FULL TEXT · http://dx.doi.org/10.4314/saaj.v10i1.61717 · AJOL African Journals Online.
Modeling the full-bridge series-resonant power converter
King, R. J.; Stuart, T. A.
1982-01-01
A steady state model is derived for the full-bridge series-resonant power converter. Normalized parametric curves for various currents and voltages are then plotted versus the triggering angle of the switching devices. The calculations are compared with experimental measurements made on a 50 kHz converter and a discussion of certain operating problems is presented.
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 ...
Recursive Bayesian recurrent neural networks for time-series modeling.
Mirikitani, Derrick T; Nikolaev, Nikolay
2010-02-01
This paper develops a probabilistic approach to recursive second-order training of recurrent neural networks (RNNs) for improved time-series modeling. A general recursive Bayesian Levenberg-Marquardt algorithm is derived to sequentially update the weights and the covariance (Hessian) matrix. The main strengths of the approach are a principled handling of the regularization hyperparameters that leads to better generalization, and stable numerical performance. The framework involves the adaptation of a noise hyperparameter and local weight prior hyperparameters, which represent the noise in the data and the uncertainties in the model parameters. Experimental investigations using artificial and real-world data sets show that RNNs equipped with the proposed approach outperform standard real-time recurrent learning and extended Kalman training algorithms for recurrent networks, as well as other contemporary nonlinear neural models, on time-series modeling.
Modeling Philippine Stock Exchange Composite Index Using Time Series Analysis
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.
Neural network versus classical time series forecasting models
Nor, Maria Elena; Safuan, Hamizah Mohd; Shab, Noorzehan Fazahiyah Md; Asrul, Mohd; Abdullah, Affendi; Mohamad, Nurul Asmaa Izzati; Lee, Muhammad Hisyam
2017-05-01
Artificial neural network (ANN) has advantage in time series forecasting as it has potential to solve complex forecasting problems. This is because ANN is data driven approach which able to be trained to map past values of a time series. In this study the forecast performance between neural network and classical time series forecasting method namely seasonal autoregressive integrated moving average models was being compared by utilizing gold price data. Moreover, the effect of different data preprocessing on the forecast performance of neural network being examined. The forecast accuracy was evaluated using mean absolute deviation, root mean square error and mean absolute percentage error. It was found that ANN produced the most accurate forecast when Box-Cox transformation was used as data preprocessing.
Time series ARIMA models for daily price of palm oil
Ariff, Noratiqah Mohd; Zamhawari, Nor Hashimah; Bakar, Mohd Aftar Abu
2015-02-01
Palm oil is deemed as one of the most important commodity that forms the economic backbone of Malaysia. Modeling and forecasting the daily price of palm oil is of great interest for Malaysia's economic growth. In this study, time series ARIMA models are used to fit the daily price of palm oil. The Akaike Infromation Criterion (AIC), Akaike Infromation Criterion with a correction for finite sample sizes (AICc) and Bayesian Information Criterion (BIC) are used to compare between different ARIMA models being considered. It is found that ARIMA(1,2,1) model is suitable for daily price of crude palm oil in Malaysia for the year 2010 to 2012.
Time Series Analysis, Modeling and Applications A Computational Intelligence Perspective
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...
Time series regression model for infectious disease and weather.
Imai, Chisato; Armstrong, Ben; Chalabi, Zaid; Mangtani, Punam; Hashizume, Masahiro
2015-10-01
Time series regression has been developed and long used to evaluate the short-term associations of air pollution and weather with mortality or morbidity of non-infectious diseases. The application of the regression approaches from this tradition to infectious diseases, however, is less well explored and raises some new issues. We discuss and present potential solutions for five issues often arising in such analyses: changes in immune population, strong autocorrelations, a wide range of plausible lag structures and association patterns, seasonality adjustments, and large overdispersion. The potential approaches are illustrated with datasets of cholera cases and rainfall from Bangladesh and influenza and temperature in Tokyo. Though this article focuses on the application of the traditional time series regression to infectious diseases and weather factors, we also briefly introduce alternative approaches, including mathematical modeling, wavelet analysis, and autoregressive integrated moving average (ARIMA) models. Modifications proposed to standard time series regression practice include using sums of past cases as proxies for the immune population, and using the logarithm of lagged disease counts to control autocorrelation due to true contagion, both of which are motivated from "susceptible-infectious-recovered" (SIR) models. The complexity of lag structures and association patterns can often be informed by biological mechanisms and explored by using distributed lag non-linear models. For overdispersed models, alternative distribution models such as quasi-Poisson and negative binomial should be considered. Time series regression can be used to investigate dependence of infectious diseases on weather, but may need modifying to allow for features specific to this context. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
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.
Identification of neutral biochemical network models from time series data.
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.
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
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
Univaried models in the series of temperature of the air
International Nuclear Information System (INIS)
Leon Aristizabal Gloria esperanza
2000-01-01
The theoretical framework for the study of the air's temperature time series is the theory of stochastic processes, particularly those known as ARIMA, that make it possible to carry out a univaried analysis. ARIMA models are built in order to explain the structure of the monthly temperatures corresponding to the mean, the absolute maximum, absolute minimum, maximum mean and minimum mean temperatures, for four stations in Colombia. By means of those models, the possible evolution of the latter variables is estimated with predictive aims in mind. The application and utility of the models is discussed
Pin failure modeling of the A series CABRI tests
International Nuclear Information System (INIS)
Young, M.F.; Portugal, J.L.
1978-01-01
The EXPAND pin fialure model, a research tool designed to model pin failure under prompt burst conditions, has been used to predict failure conditions for several of the A series CABRI tests as part of the United States participation in the CABRI Joint Project. The Project is an international program involving France, Germany, England, Japan, and the United States and has the goal of obtaining experimental data relating to the safety of LMFBR's. The A series, designed to simulate high ramp rate TOP conditions, initially utilizes single, fresh UO 2 pins of the PHENIX type in a flowing sodium loop. The pins are preheated at constant power in the CABRI reactor to establish steady state conditions (480 w/cm at the axial peak) and then subjected to a power pulse of 14 ms to 24 ms duration
A Parsimonious Bootstrap Method to Model Natural Inflow Energy Series
Directory of Open Access Journals (Sweden)
Fernando Luiz Cyrino Oliveira
2014-01-01
Full Text Available The Brazilian energy generation and transmission system is quite peculiar in its dimension and characteristics. As such, it can be considered unique in the world. It is a high dimension hydrothermal system with huge participation of hydro plants. Such strong dependency on hydrological regimes implies uncertainties related to the energetic planning, requiring adequate modeling of the hydrological time series. This is carried out via stochastic simulations of monthly inflow series using the family of Periodic Autoregressive models, PAR(p, one for each period (month of the year. In this paper it is shown the problems in fitting these models by the current system, particularly the identification of the autoregressive order “p” and the corresponding parameter estimation. It is followed by a proposal of a new approach to set both the model order and the parameters estimation of the PAR(p models, using a nonparametric computational technique, known as Bootstrap. This technique allows the estimation of reliable confidence intervals for the model parameters. The obtained results using the Parsimonious Bootstrap Method of Moments (PBMOM produced not only more parsimonious model orders but also adherent stochastic scenarios and, in the long range, lead to a better use of water resources in the energy operation planning.
Modelling of series of types of automated trenchless works tunneling
Gendarz, P.; Rzasinski, R.
2016-08-01
Microtunneling is the newest method for making underground installations. Show method is the result of experience and methods applied in other, previous methods of trenchless underground works. It is considered reasonable to elaborate a series of types of construction of tunneling machines, to develop this particular earthworks method. There are many design solutions of machines, but the current goal is to develop non - excavation robotized machine. Erosion machines with main dimensions of the tunnels which are: 1600, 2000, 2500, 3150 are design with use of the computer aided methods. Series of types of construction of tunneling machines creating process was preceded by analysis of current state. The verification of practical methodology of creating the systematic part series was based on the designed erosion machines series of types. There were developed: method of construction similarity of the erosion machines, algorithmic methods of quantitative construction attributes variant analyzes in the I-DEAS advanced graphical program, relational and program parameterization. There manufacturing process of the parts will be created, which allows to verify the technological process on the CNC machines. The models of designed will be modified and the construction will be consulted with erosion machine users and manufacturers like: Tauber Rohrbau GmbH & Co.KG from Minster, OHL ZS a.s. from Brna,. The companies’ acceptance will result in practical verification by JUMARPOL company.
Physics constrained nonlinear regression models for time series
International Nuclear Information System (INIS)
Majda, Andrew J; Harlim, John
2013-01-01
A central issue in contemporary science is the development of data driven statistical nonlinear dynamical models for time series of partial observations of nature or a complex physical model. It has been established recently that ad hoc quadratic multi-level regression (MLR) models can have finite-time blow up of statistical solutions and/or pathological behaviour of their invariant measure. Here a new class of physics constrained multi-level quadratic regression models are introduced, analysed and applied to build reduced stochastic models from data of nonlinear systems. These models have the advantages of incorporating memory effects in time as well as the nonlinear noise from energy conserving nonlinear interactions. The mathematical guidelines for the performance and behaviour of these physics constrained MLR models as well as filtering algorithms for their implementation are developed here. Data driven applications of these new multi-level nonlinear regression models are developed for test models involving a nonlinear oscillator with memory effects and the difficult test case of the truncated Burgers–Hopf model. These new physics constrained quadratic MLR models are proposed here as process models for Bayesian estimation through Markov chain Monte Carlo algorithms of low frequency behaviour in complex physical data. (paper)
State-space prediction model for chaotic time series
Alparslan, A. K.; Sayar, M.; Atilgan, A. R.
1998-08-01
A simple method for predicting the continuation of scalar chaotic time series ahead in time is proposed. The false nearest neighbors technique in connection with the time-delayed embedding is employed so as to reconstruct the state space. A local forecasting model based upon the time evolution of the topological neighboring in the reconstructed phase space is suggested. A moving root-mean-square error is utilized in order to monitor the error along the prediction horizon. The model is tested for the convection amplitude of the Lorenz model. The results indicate that for approximately 100 cycles of the training data, the prediction follows the actual continuation very closely about six cycles. The proposed model, like other state-space forecasting models, captures the long-term behavior of the system due to the use of spatial neighbors in the state space.
Single-Index Additive Vector Autoregressive Time Series Models
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.
Stochastic series expansion simulation of the t -V model
Wang, Lei; Liu, Ye-Hua; Troyer, Matthias
2016-04-01
We present an algorithm for the efficient simulation of the half-filled spinless t -V model on bipartite lattices, which combines the stochastic series expansion method with determinantal quantum Monte Carlo techniques widely used in fermionic simulations. The algorithm scales linearly in the inverse temperature, cubically with the system size, and is free from the time-discretization error. We use it to map out the finite-temperature phase diagram of the spinless t -V model on the honeycomb lattice and observe a suppression of the critical temperature of the charge-density-wave phase in the vicinity of a fermionic quantum critical point.
Disease management with ARIMA model in time series.
Sato, Renato Cesar
2013-01-01
The evaluation of infectious and noninfectious disease management can be done through the use of a time series analysis. In this study, we expect to measure the results and prevent intervention effects on the disease. Clinical studies have benefited from the use of these techniques, particularly for the wide applicability of the ARIMA model. This study briefly presents the process of using the ARIMA model. This analytical tool offers a great contribution for researchers and healthcare managers in the evaluation of healthcare interventions in specific populations.
Modeling Periodic Impulsive Effects on Online TV Series Diffusion.
Fu, Peihua; Zhu, Anding; Fang, Qiwen; Wang, Xi
Online broadcasting substantially affects the production, distribution, and profit of TV series. In addition, online word-of-mouth significantly affects the diffusion of TV series. Because on-demand streaming rates are the most important factor that influences the earnings of online video suppliers, streaming statistics and forecasting trends are valuable. In this paper, we investigate the effects of periodic impulsive stimulation and pre-launch promotion on on-demand streaming dynamics. We consider imbalanced audience feverish distribution using an impulsive susceptible-infected-removed(SIR)-like model. In addition, we perform a correlation analysis of online buzz volume based on Baidu Index data. We propose a PI-SIR model to evolve audience dynamics and translate them into on-demand streaming fluctuations, which can be observed and comprehended by online video suppliers. Six South Korean TV series datasets are used to test the model. We develop a coarse-to-fine two-step fitting scheme to estimate the model parameters, first by fitting inter-period accumulation and then by fitting inner-period feverish distribution. We find that audience members display similar viewing habits. That is, they seek new episodes every update day but fade away. This outcome means that impulsive intensity plays a crucial role in on-demand streaming diffusion. In addition, the initial audience size and online buzz are significant factors. On-demand streaming fluctuation is highly correlated with online buzz fluctuation. To stimulate audience attention and interpersonal diffusion, it is worthwhile to invest in promotion near update days. Strong pre-launch promotion is also a good marketing tool to improve overall performance. It is not advisable for online video providers to promote several popular TV series on the same update day. Inter-period accumulation is a feasible forecasting tool to predict the future trend of the on-demand streaming amount. The buzz in public social communities
Modeling Periodic Impulsive Effects on Online TV Series Diffusion.
Directory of Open Access Journals (Sweden)
Peihua Fu
Full Text Available Online broadcasting substantially affects the production, distribution, and profit of TV series. In addition, online word-of-mouth significantly affects the diffusion of TV series. Because on-demand streaming rates are the most important factor that influences the earnings of online video suppliers, streaming statistics and forecasting trends are valuable. In this paper, we investigate the effects of periodic impulsive stimulation and pre-launch promotion on on-demand streaming dynamics. We consider imbalanced audience feverish distribution using an impulsive susceptible-infected-removed(SIR-like model. In addition, we perform a correlation analysis of online buzz volume based on Baidu Index data.We propose a PI-SIR model to evolve audience dynamics and translate them into on-demand streaming fluctuations, which can be observed and comprehended by online video suppliers. Six South Korean TV series datasets are used to test the model. We develop a coarse-to-fine two-step fitting scheme to estimate the model parameters, first by fitting inter-period accumulation and then by fitting inner-period feverish distribution.We find that audience members display similar viewing habits. That is, they seek new episodes every update day but fade away. This outcome means that impulsive intensity plays a crucial role in on-demand streaming diffusion. In addition, the initial audience size and online buzz are significant factors. On-demand streaming fluctuation is highly correlated with online buzz fluctuation.To stimulate audience attention and interpersonal diffusion, it is worthwhile to invest in promotion near update days. Strong pre-launch promotion is also a good marketing tool to improve overall performance. It is not advisable for online video providers to promote several popular TV series on the same update day. Inter-period accumulation is a feasible forecasting tool to predict the future trend of the on-demand streaming amount. The buzz in public
Modeling Periodic Impulsive Effects on Online TV Series Diffusion
Fang, Qiwen; Wang, Xi
2016-01-01
Background Online broadcasting substantially affects the production, distribution, and profit of TV series. In addition, online word-of-mouth significantly affects the diffusion of TV series. Because on-demand streaming rates are the most important factor that influences the earnings of online video suppliers, streaming statistics and forecasting trends are valuable. In this paper, we investigate the effects of periodic impulsive stimulation and pre-launch promotion on on-demand streaming dynamics. We consider imbalanced audience feverish distribution using an impulsive susceptible-infected-removed(SIR)-like model. In addition, we perform a correlation analysis of online buzz volume based on Baidu Index data. Methods We propose a PI-SIR model to evolve audience dynamics and translate them into on-demand streaming fluctuations, which can be observed and comprehended by online video suppliers. Six South Korean TV series datasets are used to test the model. We develop a coarse-to-fine two-step fitting scheme to estimate the model parameters, first by fitting inter-period accumulation and then by fitting inner-period feverish distribution. Results We find that audience members display similar viewing habits. That is, they seek new episodes every update day but fade away. This outcome means that impulsive intensity plays a crucial role in on-demand streaming diffusion. In addition, the initial audience size and online buzz are significant factors. On-demand streaming fluctuation is highly correlated with online buzz fluctuation. Conclusion To stimulate audience attention and interpersonal diffusion, it is worthwhile to invest in promotion near update days. Strong pre-launch promotion is also a good marketing tool to improve overall performance. It is not advisable for online video providers to promote several popular TV series on the same update day. Inter-period accumulation is a feasible forecasting tool to predict the future trend of the on-demand streaming amount
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...
Hybrid perturbation methods based on statistical time series models
San-Juan, Juan Félix; San-Martín, Montserrat; Pérez, Iván; López, Rosario
2016-04-01
In this work we present a new methodology for orbit propagation, the hybrid perturbation theory, based on the combination of an integration method and a prediction technique. The former, which can be a numerical, analytical or semianalytical theory, generates an initial approximation that contains some inaccuracies derived from the fact that, in order to simplify the expressions and subsequent computations, not all the involved forces are taken into account and only low-order terms are considered, not to mention the fact that mathematical models of perturbations not always reproduce physical phenomena with absolute precision. The prediction technique, which can be based on either statistical time series models or computational intelligence methods, is aimed at modelling and reproducing missing dynamics in the previously integrated approximation. This combination results in the precision improvement of conventional numerical, analytical and semianalytical theories for determining the position and velocity of any artificial satellite or space debris object. In order to validate this methodology, we present a family of three hybrid orbit propagators formed by the combination of three different orders of approximation of an analytical theory and a statistical time series model, and analyse their capability to process the effect produced by the flattening of the Earth. The three considered analytical components are the integration of the Kepler problem, a first-order and a second-order analytical theories, whereas the prediction technique is the same in the three cases, namely an additive Holt-Winters method.
On the maximum-entropy/autoregressive modeling of time series
Chao, B. F.
1984-01-01
The autoregressive (AR) model of a random process is interpreted in the light of the Prony's relation which relates a complex conjugate pair of poles of the AR process in the z-plane (or the z domain) on the one hand, to the complex frequency of one complex harmonic function in the time domain on the other. Thus the AR model of a time series is one that models the time series as a linear combination of complex harmonic functions, which include pure sinusoids and real exponentials as special cases. An AR model is completely determined by its z-domain pole configuration. The maximum-entropy/autogressive (ME/AR) spectrum, defined on the unit circle of the z-plane (or the frequency domain), is nothing but a convenient, but ambiguous visual representation. It is asserted that the position and shape of a spectral peak is determined by the corresponding complex frequency, and the height of the spectral peak contains little information about the complex amplitude of the complex harmonic functions.
Modeling financial time series with S-plus
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...
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.
Assimilation of LAI time-series in crop production models
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
Empirical intrinsic geometry for nonlinear modeling and time series filtering.
Talmon, Ronen; Coifman, Ronald R
2013-07-30
In this paper, we present a method for time series analysis based on empirical intrinsic geometry (EIG). EIG enables one to reveal the low-dimensional parametric manifold as well as to infer the underlying dynamics of high-dimensional time series. By incorporating concepts of information geometry, this method extends existing geometric analysis tools to support stochastic settings and parametrizes the geometry of empirical distributions. However, the statistical models are not required as priors; hence, EIG may be applied to a wide range of real signals without existing definitive models. We show that the inferred model is noise-resilient and invariant under different observation and instrumental modalities. In addition, we show that it can be extended efficiently to newly acquired measurements in a sequential manner. These two advantages enable us to revisit the Bayesian approach and incorporate empirical dynamics and intrinsic geometry into a nonlinear filtering framework. We show applications to nonlinear and non-Gaussian tracking problems as well as to acoustic signal localization.
Empirical investigation on modeling solar radiation series with ARMA–GARCH models
International Nuclear Information System (INIS)
Sun, Huaiwei; Yan, Dong; Zhao, Na; Zhou, Jianzhong
2015-01-01
Highlights: • Apply 6 ARMA–GARCH(-M) models to model and forecast solar radiation. • The ARMA–GARCH(-M) models produce more accurate radiation forecasting than conventional methods. • Show that ARMA–GARCH-M models are more effective for forecasting solar radiation mean and volatility. • The ARMA–EGARCH-M is robust and the ARMA–sGARCH-M is very competitive. - Abstract: Simulation of radiation is one of the most important issues in solar utilization. Time series models are useful tools in the estimation and forecasting of solar radiation series and their changes. In this paper, the effectiveness of autoregressive moving average (ARMA) models with various generalized autoregressive conditional heteroskedasticity (GARCH) processes, namely ARMA–GARCH models are evaluated for their effectiveness in radiation series. Six different GARCH approaches, which contain three different ARMA–GARCH models and corresponded GARCH in mean (ARMA–GARCH-M) models, are applied in radiation data sets from two representative climate stations in China. Multiple evaluation metrics of modeling sufficiency are used for evaluating the performances of models. The results show that the ARMA–GARCH(-M) models are effective in radiation series estimation. Both in fitting and prediction of radiation series, the ARMA–GARCH(-M) models show better modeling sufficiency than traditional models, while ARMA–EGARCH-M models are robustness in two sites and the ARMA–sGARCH-M models appear very competitive. Comparisons of statistical diagnostics and model performance clearly show that the ARMA–GARCH-M models make the mean radiation equations become more sufficient. It is recommended the ARMA–GARCH(-M) models to be the preferred method to use in the modeling of solar radiation series
Incorporating Satellite Time-Series Data into Modeling
Gregg, Watson
2008-01-01
In situ time series observations have provided a multi-decadal view of long-term changes in ocean biology. These observations are sufficiently reliable to enable discernment of even relatively small changes, and provide continuous information on a host of variables. Their key drawback is their limited domain. Satellite observations from ocean color sensors do not suffer the drawback of domain, and simultaneously view the global oceans. This attribute lends credence to their use in global and regional model validation and data assimilation. We focus on these applications using the NASA Ocean Biogeochemical Model. The enhancement of the satellite data using data assimilation is featured and the limitation of tongterm satellite data sets is also discussed.
Self-organising mixture autoregressive model for non-stationary time series modelling.
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.
Time series modelling and forecasting of emergency department overcrowding.
Kadri, Farid; Harrou, Fouzi; Chaabane, Sondès; Tahon, Christian
2014-09-01
Efficient management of patient flow (demand) in emergency departments (EDs) has become an urgent issue for many hospital administrations. Today, more and more attention is being paid to hospital management systems to optimally manage patient flow and to improve management strategies, efficiency and safety in such establishments. To this end, EDs require significant human and material resources, but unfortunately these are limited. Within such a framework, the ability to accurately forecast demand in emergency departments has considerable implications for hospitals to improve resource allocation and strategic planning. The aim of this study was to develop models for forecasting daily attendances at the hospital emergency department in Lille, France. The study demonstrates how time-series analysis can be used to forecast, at least in the short term, demand for emergency services in a hospital emergency department. The forecasts were based on daily patient attendances at the paediatric emergency department in Lille regional hospital centre, France, from January 2012 to December 2012. An autoregressive integrated moving average (ARIMA) method was applied separately to each of the two GEMSA categories and total patient attendances. Time-series analysis was shown to provide a useful, readily available tool for forecasting emergency department demand.
Optimal model-free prediction from multivariate time series
Runge, Jakob; Donner, Reik V.; Kurths, Jürgen
2015-05-01
Forecasting a time series from multivariate predictors constitutes a challenging problem, especially using model-free approaches. Most techniques, such as nearest-neighbor prediction, quickly suffer from the curse of dimensionality and overfitting for more than a few predictors which has limited their application mostly to the univariate case. Therefore, selection strategies are needed that harness the available information as efficiently as possible. Since often the right combination of predictors matters, ideally all subsets of possible predictors should be tested for their predictive power, but the exponentially growing number of combinations makes such an approach computationally prohibitive. Here a prediction scheme that overcomes this strong limitation is introduced utilizing a causal preselection step which drastically reduces the number of possible predictors to the most predictive set of causal drivers making a globally optimal search scheme tractable. The information-theoretic optimality is derived and practical selection criteria are discussed. As demonstrated for multivariate nonlinear stochastic delay processes, the optimal scheme can even be less computationally expensive than commonly used suboptimal schemes like forward selection. The method suggests a general framework to apply the optimal model-free approach to select variables and subsequently fit a model to further improve a prediction or learn statistical dependencies. The performance of this framework is illustrated on a climatological index of El Niño Southern Oscillation.
2010-08-13
... cycling and that a previous study on a Model A310 airplane had shown no reduction in stiffness due to age... considers the equipment for both inspections to be special tooling and the inspection methods to be of... of civil aircraft in air commerce by prescribing regulations for practices, methods, and procedures...
An SVM model with hybrid kernels for hydrological time series
Wang, C.; Wang, H.; Zhao, X.; Xie, Q.
2017-12-01
Support Vector Machine (SVM) models have been widely applied to the forecast of climate/weather and its impact on other environmental variables such as hydrologic response to climate/weather. When using SVM, the choice of the kernel function plays the key role. Conventional SVM models mostly use one single type of kernel function, e.g., radial basis kernel function. Provided that there are several featured kernel functions available, each having its own advantages and drawbacks, a combination of these kernel functions may give more flexibility and robustness to SVM approach, making it suitable for a wide range of application scenarios. This paper presents such a linear combination of radial basis kernel and polynomial kernel for the forecast of monthly flowrate in two gaging stations using SVM approach. The results indicate significant improvement in the accuracy of predicted series compared to the approach with either individual kernel function, thus demonstrating the feasibility and advantages of such hybrid kernel approach for SVM applications.
Thin stillage fractionation using ultrafiltration: resistance in series model.
Arora, Amit; Dien, Bruce S; Belyea, Ronald L; Wang, Ping; Singh, Vijay; Tumbleson, M E; Rausch, Kent D
2009-02-01
The corn based dry grind process is the most widely used method in the US for fuel ethanol production. Fermentation of corn to ethanol produces whole stillage after ethanol is removed by distillation. It is centrifuged to separate thin stillage from wet grains. Thin stillage contains 5-10% solids. To concentrate solids of thin stillage, it requires evaporation of large amounts of water and maintenance of evaporators. Evaporator maintenance requires excess evaporator capacity at the facility, increasing capital expenses, requiring plant slowdowns or shut downs and results in revenue losses. Membrane filtration is one method that could lead to improved value of thin stillage and may offer an alternative to evaporation. Fractionation of thin stillage using ultrafiltration was conducted to evaluate membranes as an alternative to evaporators in the ethanol industry. Two regenerated cellulose membranes with molecular weight cut offs of 10 and 100 kDa were evaluated. Total solids (suspended and soluble) contents recovered through membrane separation process were similar to those from commercial evaporators. Permeate flux decline of thin stillage using a resistance in series model was determined. Each of the four components of total resistance was evaluated experimentally. Effects of operating variables such as transmembrane pressure and temperature on permeate flux rate and resistances were determined and optimum conditions for maximum flux rates were evaluated. Model equations were developed to evaluate the resistance components that are responsible for fouling and to predict total flux decline with respect to time. Modeling results were in agreement with experimental results (R(2) > 0.98).
Neural network modeling of nonlinear systems based on Volterra series extension of a linear model
Soloway, Donald I.; Bialasiewicz, Jan T.
1992-01-01
A Volterra series approach was applied to the identification of nonlinear systems which are described by a neural network model. A procedure is outlined by which a mathematical model can be developed from experimental data obtained from the network structure. Applications of the results to the control of robotic systems are discussed.
Kennedy, Curtis E; Turley, James P
2011-10-24
Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of candidate features; 9
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 ... Ugwuowo (2006) proposed asymmetric angular- linear multivariate regression models, ..... Using the parameter estimates in Table 3, the fitted Fourier series model is ..... For the SARIMA model with the stochastic component also being white noise, ...
Modeling Financial Time Series Based on a Market Microstructure Model with Leverage Effect
Yanhui Xi; Hui Peng; Yemei Qin
2016-01-01
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....
vector bilinear autoregressive time series model and its superiority
African Journals Online (AJOL)
KEYWORDS: Linear time series, Autoregressive process, Autocorrelation function, Partial autocorrelation function,. Vector time .... important result on matrix algebra with respect to the spectral ..... application to covariance analysis of super-.
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...
Rotation in the Dynamic Factor Modeling of Multivariate Stationary Time Series.
Molenaar, Peter C. M.; Nesselroade, John R.
2001-01-01
Proposes a special rotation procedure for the exploratory dynamic factor model for stationary multivariate time series. The rotation procedure applies separately to each univariate component series of a q-variate latent factor series and transforms such a component, initially represented as white noise, into a univariate moving-average.…
Rotation in the dynamic factor modeling of multivariate stationary time series.
Molenaar, P.C.M.; Nesselroade, J.R.
2001-01-01
A special rotation procedure is proposed for the exploratory dynamic factor model for stationary multivariate time series. The rotation procedure applies separately to each univariate component series of a q-variate latent factor series and transforms such a component, initially represented as white
New Models for Forecasting Enrollments: Fuzzy Time Series and Neural Network Approaches.
Song, Qiang; Chissom, Brad S.
Since university enrollment forecasting is very important, many different methods and models have been proposed by researchers. Two new methods for enrollment forecasting are introduced: (1) the fuzzy time series model; and (2) the artificial neural networks model. Fuzzy time series has been proposed to deal with forecasting problems within a…
A Sandwich-Type Standard Error Estimator of SEM Models with Multivariate Time Series
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…
Time series modeling and forecasting using memetic algorithms for regime-switching models.
Bergmeir, Christoph; Triguero, Isaac; Molina, Daniel; Aznarte, José Luis; Benitez, José Manuel
2012-11-01
In this brief, we present a novel model fitting procedure for the neuro-coefficient smooth transition autoregressive model (NCSTAR), as presented by Medeiros and Veiga. The model is endowed with a statistically founded iterative building procedure and can be interpreted in terms of fuzzy rule-based systems. The interpretability of the generated models and a mathematically sound building procedure are two very important properties of forecasting models. The model fitting procedure employed by the original NCSTAR is a combination of initial parameter estimation by a grid search procedure with a traditional local search algorithm. We propose a different fitting procedure, using a memetic algorithm, in order to obtain more accurate models. An empirical evaluation of the method is performed, applying it to various real-world time series originating from three forecasting competitions. The results indicate that we can significantly enhance the accuracy of the models, making them competitive to models commonly used in the field.
Using Disks as Models for Proofs of Series
Somchaipeng, Tongta; Kruatong, Tussatrin; Panijpan, Bhinyo
2012-01-01
Exploring and deriving proofs of closed-form expressions for series can be fun for students. However, for some students, a physical representation of such problems is more meaningful. Various approaches have been designed to help students visualize squares of sums and sums of squares; these approaches may be arithmetic-algebraic or combinatorial…
multivariate time series modeling of selected childhood diseases
African Journals Online (AJOL)
2016-06-17
Jun 17, 2016 ... KEYWORDS: Multivariate Approach, Pre-whitening, Vector Time Series, .... Alternatively, the process may be written in mean adjusted form as .... The AIC criterion asymptotically over estimates the order with positive probability, whereas the BIC and HQC criteria ... has the same asymptotic distribution as Ǫ.
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.
Protection against wing icing for Airbus A300 and A310
Woelfer, G.
1981-01-01
To improve economy of operation, it is now planned to modify the anti-icing system used on the A300 Airbus wing. Thus, for the A310 Airbus, the deicing system will be applied to only half the wing length. Other essential modifications are a substantial simplification of the warm-air system and discontinuation of the use of a double wall in slats.
Evolution of Black-Box Models Based on Volterra Series
Directory of Open Access Journals (Sweden)
Daniel D. Silveira
2015-01-01
Full Text Available This paper presents a historical review of the many behavioral models actually used to model radio frequency power amplifiers and a new classification of these behavioral models. It also discusses the evolution of these models, from a single polynomial to multirate Volterra models, presenting equations and estimation methods. New trends in RF power amplifier behavioral modeling are suggested.
The First European Parabolic Flight Campaign with the Airbus A310 ZERO-G
Pletser, Vladimir; Rouquette, Sebastien; Friedrich, Ulrike; Clervoy, Jean-Francois; Gharib, Thierry; Gai, Frederic; Mora, Christophe
2016-12-01
Aircraft parabolic flights repetitively provide up to 23 seconds of reduced gravity during ballistic flight manoeuvres. Parabolic flights are used to conduct short microgravity investigations in Physical and Life Sciences and in Technology, to test instrumentation prior to space flights and to train astronauts before a space mission. The use of parabolic flights is complementary to other microgravity carriers (drop towers, sounding rockets), and preparatory to manned space missions on board the International Space Station and other manned spacecraft, such as Shenzhou and the future Chinese Space Station. After 17 years of using the Airbus A300 ZERO-G, the French company Novespace, a subsidiary of the ' Centre National d'Etudes Spatiales' (CNES, French Space Agency), based in Bordeaux, France, purchased a new aircraft, an Airbus A310, to perform parabolic flights for microgravity research in Europe. Since April 2015, the European Space Agency (ESA), CNES and the ` Deutsches Zentrum für Luft- und Raumfahrt e.V.' (DLR, the German Aerospace Center) use this new aircraft, the Airbus A310 ZERO-G, for research experiments in microgravity. The first campaign was a Cooperative campaign shared by the three agencies, followed by respectively a CNES, an ESA and a DLR campaign. This paper presents the new Airbus A310 ZERO-G and its main characteristics and interfaces for scientific experiments. The experiments conducted during the first European campaign are presented.
Predicting long-term catchment nutrient export: the use of nonlinear time series models
Valent, Peter; Howden, Nicholas J. K.; Szolgay, Jan; Komornikova, Magda
2010-05-01
After the Second World War the nitrate concentrations in European water bodies changed significantly as the result of increased nitrogen fertilizer use and changes in land use. However, in the last decades, as a consequence of the implementation of nitrate-reducing measures in Europe, the nitrate concentrations in water bodies slowly decrease. This causes that the mean and variance of the observed time series also changes with time (nonstationarity and heteroscedascity). In order to detect changes and properly describe the behaviour of such time series by time series analysis, linear models (such as autoregressive (AR), moving average (MA) and autoregressive moving average models (ARMA)), are no more suitable. Time series with sudden changes in statistical characteristics can cause various problems in the calibration of traditional water quality models and thus give biased predictions. Proper statistical analysis of these non-stationary and heteroscedastic time series with the aim of detecting and subsequently explaining the variations in their statistical characteristics requires the use of nonlinear time series models. This information can be then used to improve the model building and calibration of conceptual water quality model or to select right calibration periods in order to produce reliable predictions. The objective of this contribution is to analyze two long time series of nitrate concentrations of the rivers Ouse and Stour with advanced nonlinear statistical modelling techniques and compare their performance with traditional linear models of the ARMA class in order to identify changes in the time series characteristics. The time series were analysed with nonlinear models with multiple regimes represented by self-exciting threshold autoregressive (SETAR) and Markov-switching models (MSW). The analysis showed that, based on the value of residual sum of squares (RSS) in both datasets, SETAR and MSW models described the time-series better than models of the
Evaluation of nonlinearity and validity of nonlinear modeling for complex time series.
Suzuki, Tomoya; Ikeguchi, Tohru; Suzuki, Masuo
2007-10-01
Even if an original time series exhibits nonlinearity, it is not always effective to approximate the time series by a nonlinear model because such nonlinear models have high complexity from the viewpoint of information criteria. Therefore, we propose two measures to evaluate both the nonlinearity of a time series and validity of nonlinear modeling applied to it by nonlinear predictability and information criteria. Through numerical simulations, we confirm that the proposed measures effectively detect the nonlinearity of an observed time series and evaluate the validity of the nonlinear model. The measures are also robust against observational noises. We also analyze some real time series: the difference of the number of chickenpox and measles patients, the number of sunspots, five Japanese vowels, and the chaotic laser. We can confirm that the nonlinear model is effective for the Japanese vowel /a/, the difference of the number of measles patients, and the chaotic laser.
Visser, H.; Molenaar, J.
1995-05-01
The detection of trends in climatological data has become central to the discussion on climate change due to the enhanced greenhouse effect. To prove detection, a method is needed (i) to make inferences on significant rises or declines in trends, (ii) to take into account natural variability in climate series, and (iii) to compare output from GCMs with the trends in observed climate data. To meet these requirements, flexible mathematical tools are needed. A structural time series model is proposed with which a stochastic trend, a deterministic trend, and regression coefficients can be estimated simultaneously. The stochastic trend component is described using the class of ARIMA models. The regression component is assumed to be linear. However, the regression coefficients corresponding with the explanatory variables may be time dependent to validate this assumption. The mathematical technique used to estimate this trend-regression model is the Kaiman filter. The main features of the filter are discussed.Examples of trend estimation are given using annual mean temperatures at a single station in the Netherlands (1706-1990) and annual mean temperatures at Northern Hemisphere land stations (1851-1990). The inclusion of explanatory variables is shown by regressing the latter temperature series on four variables: Southern Oscillation index (SOI), volcanic dust index (VDI), sunspot numbers (SSN), and a simulated temperature signal, induced by increasing greenhouse gases (GHG). In all analyses, the influence of SSN on global temperatures is found to be negligible. The correlations between temperatures and SOI and VDI appear to be negative. For SOI, this correlation is significant, but for VDI it is not, probably because of a lack of volcanic eruptions during the sample period. The relation between temperatures and GHG is positive, which is in agreement with the hypothesis of a warming climate because of increasing levels of greenhouse gases. The prediction performance of
Fitting ARMA Time Series by Structural Equation Models.
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)
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...
Time Series Modelling of Syphilis Incidence in China from 2005 to 2012.
Zhang, Xingyu; Zhang, Tao; Pei, Jiao; Liu, Yuanyuan; Li, Xiaosong; Medrano-Gracia, Pau
2016-01-01
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. 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). 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. 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.
Time Series Modelling of Syphilis Incidence in China from 2005 to 2012
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
Modeling time-series data from microbial communities.
Ridenhour, Benjamin J; Brooker, Sarah L; Williams, Janet E; Van Leuven, James T; Miller, Aaron W; Dearing, M Denise; Remien, Christopher H
2017-11-01
As sequencing technologies have advanced, the amount of information regarding the composition of bacterial communities from various environments (for example, skin or soil) has grown exponentially. To date, most work has focused on cataloging taxa present in samples and determining whether the distribution of taxa shifts with exogenous covariates. However, important questions regarding how taxa interact with each other and their environment remain open thus preventing in-depth ecological understanding of microbiomes. Time-series data from 16S rDNA amplicon sequencing are becoming more common within microbial ecology, but methods to infer ecological interactions from these longitudinal data are limited. We address this gap by presenting a method of analysis using Poisson regression fit with an elastic-net penalty that (1) takes advantage of the fact that the data are time series; (2) constrains estimates to allow for the possibility of many more interactions than data; and (3) is scalable enough to handle data consisting of thousands of taxa. We test the method on gut microbiome data from white-throated woodrats (Neotoma albigula) that were fed varying amounts of the plant secondary compound oxalate over a period of 22 days to estimate interactions between OTUs and their environment.
Parametric, nonparametric and parametric modelling of a chaotic circuit time series
Timmer, J.; Rust, H.; Horbelt, W.; Voss, H. U.
2000-09-01
The determination of a differential equation underlying a measured time series is a frequently arising task in nonlinear time series analysis. In the validation of a proposed model one often faces the dilemma that it is hard to decide whether possible discrepancies between the time series and model output are caused by an inappropriate model or by bad estimates of parameters in a correct type of model, or both. We propose a combination of parametric modelling based on Bock's multiple shooting algorithm and nonparametric modelling based on optimal transformations as a strategy to test proposed models and if rejected suggest and test new ones. We exemplify this strategy on an experimental time series from a chaotic circuit where we obtain an extremely accurate reconstruction of the observed attractor.
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...
Modeling sports highlights using a time-series clustering framework and model interpretation
Radhakrishnan, Regunathan; Otsuka, Isao; Xiong, Ziyou; Divakaran, Ajay
2005-01-01
In our past work on sports highlights extraction, we have shown the utility of detecting audience reaction using an audio classification framework. The audio classes in the framework were chosen based on intuition. In this paper, we present a systematic way of identifying the key audio classes for sports highlights extraction using a time series clustering framework. We treat the low-level audio features as a time series and model the highlight segments as "unusual" events in a background of an "usual" process. The set of audio classes to characterize the sports domain is then identified by analyzing the consistent patterns in each of the clusters output from the time series clustering framework. The distribution of features from the training data so obtained for each of the key audio classes, is parameterized by a Minimum Description Length Gaussian Mixture Model (MDL-GMM). We also interpret the meaning of each of the mixture components of the MDL-GMM for the key audio class (the "highlight" class) that is correlated with highlight moments. Our results show that the "highlight" class is a mixture of audience cheering and commentator's excited speech. Furthermore, we show that the precision-recall performance for highlights extraction based on this "highlight" class is better than that of our previous approach which uses only audience cheering as the key highlight class.
Time Series ARIMA Models of Undergraduate Grade Point Average.
Rogers, Bruce G.
The Auto-Regressive Integrated Moving Average (ARIMA) Models, often referred to as Box-Jenkins models, are regression methods for analyzing sequential dependent observations with large amounts of data. The Box-Jenkins approach, a three-stage procedure consisting of identification, estimation and diagnosis, was used to select the most appropriate…
SEM based CARMA time series modeling for arbitrary N
Oud, J.H.L.; Völkle, M.C.; Driver, C.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
Modeling Large Time Series for Efficient Approximate Query Processing
DEFF Research Database (Denmark)
Perera, Kasun S; Hahmann, Martin; Lehner, Wolfgang
2015-01-01
query statistics derived from experiments and when running the system. Our approach can also reduce communication load by exchanging models instead of data. To allow seamless integration of model-based querying into traditional data warehouses, we introduce a SQL compatible query terminology. Our...
Modelling Changes in the Unconditional Variance of Long Stock Return Series
DEFF Research Database (Denmark)
Amado, Cristina; Teräsvirta, Timo
In this paper we develop a testing and modelling procedure for describing the long-term volatility movements over very long return series. For the purpose, we assume that volatility is multiplicatively decomposed into a conditional and an unconditional component as in Amado and Teräsvirta (2011...... show that the long-memory property in volatility may be explained by ignored changes in the unconditional variance of the long series. Finally, based on a formal statistical test we find evidence of the superiority of volatility forecast accuracy of the new model over the GJR-GARCH model at all...... horizons for a subset of the long return series....
Modelling changes in the unconditional variance of long stock return series
DEFF Research Database (Denmark)
Amado, Cristina; Teräsvirta, Timo
2014-01-01
In this paper we develop a testing and modelling procedure for describing the long-term volatility movements over very long daily return series. For this purpose we assume that volatility is multiplicatively decomposed into a conditional and an unconditional component as in Amado and Teräsvirta...... that the apparent long memory property in volatility may be interpreted as changes in the unconditional variance of the long series. Finally, based on a formal statistical test we find evidence of the superiority of volatility forecasting accuracy of the new model over the GJR-GARCH model at all horizons for eight...... subsets of the long return series....
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.
A Feature Fusion Based Forecasting Model for Financial Time Series
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
SEM Based CARMA Time Series Modeling for Arbitrary N.
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.
Time series modelling of the Kobe-Osaka earthquake recordings
Directory of Open Access Journals (Sweden)
N. Singh
2002-01-01
generated by an earthquake. With a view of comparing these two types of waveforms, Singh (1992 developed a technique for identifying a model in time domain. Fortunately this technique has been found useful in modelling the recordings of the killer earthquake occurred in the Kobe-Osaka region of Japan at 5.46 am on 17 January, 1995. The aim of the present study is to show how well the method for identifying a model (developed by Singh (1992 can be used for describing the vibrations of the above mentioned earthquake recorded at Charters Towers in Queensland, Australia.
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.
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%.
Liu, Zitao; Hauskrecht, Milos
2017-11-01
Building of an accurate predictive model of clinical time series for a patient is critical for understanding of the patient condition, its dynamics, and optimal patient management. Unfortunately, this process is not straightforward. First, patient-specific variations are typically large and population-based models derived or learned from many different patients are often unable to support accurate predictions for each individual patient. Moreover, time series observed for one patient at any point in time may be too short and insufficient to learn a high-quality patient-specific model just from the patient's own data. To address these problems we propose, develop and experiment with a new adaptive forecasting framework for building multivariate clinical time series models for a patient and for supporting patient-specific predictions. The framework relies on the adaptive model switching approach that at any point in time selects the most promising time series model out of the pool of many possible models, and consequently, combines advantages of the population, patient-specific and short-term individualized predictive models. We demonstrate that the adaptive model switching framework is very promising approach to support personalized time series prediction, and that it is able to outperform predictions based on pure population and patient-specific models, as well as, other patient-specific model adaptation strategies.
A neuro-fuzzy computing technique for modeling hydrological time series
Nayak, P. C.; Sudheer, K. P.; Rangan, D. M.; Ramasastri, K. S.
2004-05-01
Intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are proven to be efficient when applied individually to a variety of problems. Recently there has been a growing interest in combining both these approaches, and as a result, neuro-fuzzy computing techniques have evolved. This approach has been tested and evaluated in the field of signal processing and related areas, but researchers have only begun evaluating the potential of this neuro-fuzzy hybrid approach in hydrologic modeling studies. This paper presents the application of an adaptive neuro fuzzy inference system (ANFIS) to hydrologic time series modeling, and is illustrated by an application to model the river flow of Baitarani River in Orissa state, India. An introduction to the ANFIS modeling approach is also presented. The advantage of the method is that it does not require the model structure to be known a priori, in contrast to most of the time series modeling techniques. The results showed that the ANFIS forecasted flow series preserves the statistical properties of the original flow series. The model showed good performance in terms of various statistical indices. The results are highly promising, and a comparative analysis suggests that the proposed modeling approach outperforms ANNs and other traditional time series models in terms of computational speed, forecast errors, efficiency, peak flow estimation etc. It was observed that the ANFIS model preserves the potential of the ANN approach fully, and eases the model building process.
Modeling activity patterns of wildlife using time-series analysis.
Zhang, Jindong; Hull, Vanessa; Ouyang, Zhiyun; He, Liang; Connor, Thomas; Yang, Hongbo; Huang, Jinyan; Zhou, Shiqiang; Zhang, Zejun; Zhou, Caiquan; Zhang, Hemin; Liu, Jianguo
2017-04-01
The study of wildlife activity patterns is an effective approach to understanding fundamental ecological and evolutionary processes. However, traditional statistical approaches used to conduct quantitative analysis have thus far had limited success in revealing underlying mechanisms driving activity patterns. Here, we combine wavelet analysis, a type of frequency-based time-series analysis, with high-resolution activity data from accelerometers embedded in GPS collars to explore the effects of internal states (e.g., pregnancy) and external factors (e.g., seasonal dynamics of resources and weather) on activity patterns of the endangered giant panda ( Ailuropoda melanoleuca ). Giant pandas exhibited higher frequency cycles during the winter when resources (e.g., water and forage) were relatively poor, as well as during spring, which includes the giant panda's mating season. During the summer and autumn when resources were abundant, pandas exhibited a regular activity pattern with activity peaks every 24 hr. A pregnant individual showed distinct differences in her activity pattern from other giant pandas for several months following parturition. These results indicate that animals adjust activity cycles to adapt to seasonal variation of the resources and unique physiological periods. Wavelet coherency analysis also verified the synchronization of giant panda activity level with air temperature and solar radiation at the 24-hr band. Our study also shows that wavelet analysis is an effective tool for analyzing high-resolution activity pattern data and its relationship to internal and external states, an approach that has the potential to inform wildlife conservation and management across species.
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...
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.
Multiband Prediction Model for Financial Time Series with Multivariate Empirical Mode Decomposition
Directory of Open Access Journals (Sweden)
Md. Rabiul Islam
2012-01-01
Full Text Available This paper presents a subband approach to financial time series prediction. Multivariate empirical mode decomposition (MEMD is employed here for multiband representation of multichannel financial time series together. Autoregressive moving average (ARMA model is used in prediction of individual subband of any time series data. Then all the predicted subband signals are summed up to obtain the overall prediction. The ARMA model works better for stationary signal. With multiband representation, each subband becomes a band-limited (narrow band signal and hence better prediction is achieved. The performance of the proposed MEMD-ARMA model is compared with classical EMD, discrete wavelet transform (DWT, and with full band ARMA model in terms of signal-to-noise ratio (SNR and mean square error (MSE between the original and predicted time series. The simulation results show that the MEMD-ARMA-based method performs better than the other methods.
Mathematical model of thyristor inverter including a series-parallel resonant circuit
Luft, M.; Szychta, E.
2008-01-01
The article presents a mathematical model of thyristor inverter including a series-parallel resonant circuit with the aid of state variable method. Maple procedures are used to compute current and voltage waveforms in the inverter.
Mathematical Model of Thyristor Inverter Including a Series-parallel Resonant Circuit
Miroslaw Luft; Elzbieta Szychta
2008-01-01
The article presents a mathematical model of thyristor inverter including a series-parallel resonant circuit with theaid of state variable method. Maple procedures are used to compute current and voltage waveforms in the inverter.
Mathematical Model of Thyristor Inverter Including a Series-parallel Resonant Circuit
Directory of Open Access Journals (Sweden)
Miroslaw Luft
2008-01-01
Full Text Available The article presents a mathematical model of thyristor inverter including a series-parallel resonant circuit with theaid of state variable method. Maple procedures are used to compute current and voltage waveforms in the inverter.
Linear models for multivariate, time series, and spatial data
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...
Monitoring Poisson time series using multi-process models
DEFF Research Database (Denmark)
Engebjerg, Malene Dahl Skov; Lundbye-Christensen, Søren; Kjær, Birgitte B.
aspects of health resource management may also be addressed. In this paper we center on the detection of outbreaks of infectious diseases. This is achieved by a multi-process Poisson state space model taking autocorrelation and overdispersion into account, which has been applied to a data set concerning...
SSM: Inference for time series analysis with State Space Models
Dureau, Joseph; Ballesteros, Sébastien; Bogich, Tiffany
2013-01-01
The main motivation behind the open source library SSM is to reduce the technical friction that prevents modellers from sharing their work, quickly iterating in crisis situations, and making their work directly usable by public authorities to serve decision-making.
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.
Spatially adaptive mixture modeling for analysis of FMRI time series.
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
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....
MODELLING BIOPHYSICAL PARAMETERS OF MAIZE USING LANDSAT 8 TIME SERIES
Directory of Open Access Journals (Sweden)
T. Dahms
2016-06-01
Full Text Available Open and free access to multi-frequent high-resolution data (e.g. Sentinel – 2 will fortify agricultural applications based on satellite data. The temporal and spatial resolution of these remote sensing datasets directly affects the applicability of remote sensing methods, for instance a robust retrieving of biophysical parameters over the entire growing season with very high geometric resolution. In this study we use machine learning methods to predict biophysical parameters, namely the fraction of absorbed photosynthetic radiation (FPAR, the leaf area index (LAI and the chlorophyll content, from high resolution remote sensing. 30 Landsat 8 OLI scenes were available in our study region in Mecklenburg-Western Pomerania, Germany. In-situ data were weekly to bi-weekly collected on 18 maize plots throughout the summer season 2015. The study aims at an optimized prediction of biophysical parameters and the identification of the best explaining spectral bands and vegetation indices. For this purpose, we used the entire in-situ dataset from 24.03.2015 to 15.10.2015. Random forest and conditional inference forests were used because of their explicit strong exploratory and predictive character. Variable importance measures allowed for analysing the relation between the biophysical parameters with respect to the spectral response, and the performance of the two approaches over the plant stock evolvement. Classical random forest regression outreached the performance of conditional inference forests, in particular when modelling the biophysical parameters over the entire growing period. For example, modelling biophysical parameters of maize for the entire vegetation period using random forests yielded: FPAR: R² = 0.85; RMSE = 0.11; LAI: R² = 0.64; RMSE = 0.9 and chlorophyll content (SPAD: R² = 0.80; RMSE=4.9. Our results demonstrate the great potential in using machine-learning methods for the interpretation of long-term multi-frequent remote sensing
Modelling Biophysical Parameters of Maize Using Landsat 8 Time Series
Dahms, Thorsten; Seissiger, Sylvia; Conrad, Christopher; Borg, Erik
2016-06-01
Open and free access to multi-frequent high-resolution data (e.g. Sentinel - 2) will fortify agricultural applications based on satellite data. The temporal and spatial resolution of these remote sensing datasets directly affects the applicability of remote sensing methods, for instance a robust retrieving of biophysical parameters over the entire growing season with very high geometric resolution. In this study we use machine learning methods to predict biophysical parameters, namely the fraction of absorbed photosynthetic radiation (FPAR), the leaf area index (LAI) and the chlorophyll content, from high resolution remote sensing. 30 Landsat 8 OLI scenes were available in our study region in Mecklenburg-Western Pomerania, Germany. In-situ data were weekly to bi-weekly collected on 18 maize plots throughout the summer season 2015. The study aims at an optimized prediction of biophysical parameters and the identification of the best explaining spectral bands and vegetation indices. For this purpose, we used the entire in-situ dataset from 24.03.2015 to 15.10.2015. Random forest and conditional inference forests were used because of their explicit strong exploratory and predictive character. Variable importance measures allowed for analysing the relation between the biophysical parameters with respect to the spectral response, and the performance of the two approaches over the plant stock evolvement. Classical random forest regression outreached the performance of conditional inference forests, in particular when modelling the biophysical parameters over the entire growing period. For example, modelling biophysical parameters of maize for the entire vegetation period using random forests yielded: FPAR: R² = 0.85; RMSE = 0.11; LAI: R² = 0.64; RMSE = 0.9 and chlorophyll content (SPAD): R² = 0.80; RMSE=4.9. Our results demonstrate the great potential in using machine-learning methods for the interpretation of long-term multi-frequent remote sensing datasets to model
Sensitivity analysis of machine-learning models of hydrologic time series
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.
An Illustration of Generalised Arma (garma) Time Series Modeling of Forest Area in Malaysia
Pillai, Thulasyammal Ramiah; Shitan, Mahendran
Forestry is the art and science of managing forests, tree plantations, and related natural resources. The main goal of forestry is to create and implement systems that allow forests to continue a sustainable provision of environmental supplies and services. Forest area is land under natural or planted stands of trees, whether productive or not. Forest area of Malaysia has been observed over the years and it can be modeled using time series models. A new class of GARMA models have been introduced in the time series literature to reveal some hidden features in time series data. For these models to be used widely in practice, we illustrate the fitting of GARMA (1, 1; 1, δ) model to the Annual Forest Area data of Malaysia which has been observed from 1987 to 2008. The estimation of the model was done using Hannan-Rissanen Algorithm, Whittle's Estimation and Maximum Likelihood Estimation.
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...
Assessing and improving the quality of modeling : a series of empirical studies about the UML
Lange, C.F.J.
2007-01-01
Assessing and Improving the Quality of Modeling A Series of Empirical Studies about the UML This thesis addresses the assessment and improvement of the quality of modeling in software engineering. In particular, we focus on the Unified Modeling Language (UML), which is the de facto standard in
A Four-Stage Hybrid Model for Hydrological Time Series Forecasting
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
A four-stage hybrid model for hydrological time series forecasting.
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.
Moeeni, Hamid; Bonakdari, Hossein; Fatemi, Seyed Ehsan
2017-04-01
Because time series stationarization has a key role in stochastic modeling results, three methods are analyzed in this study. The methods are seasonal differencing, seasonal standardization and spectral analysis to eliminate the periodic effect on time series stationarity. First, six time series including 4 streamflow series and 2 water temperature series are stationarized. The stochastic term for these series obtained with ARIMA is subsequently modeled. For the analysis, 9228 models are introduced. It is observed that seasonal standardization and spectral analysis eliminate the periodic term completely, while seasonal differencing maintains seasonal correlation structures. The obtained results indicate that all three methods present acceptable performance overall. However, model accuracy in monthly streamflow prediction is higher with seasonal differencing than with the other two methods. Another advantage of seasonal differencing over the other methods is that the monthly streamflow is never estimated as negative. Standardization is the best method for predicting monthly water temperature although it is quite similar to seasonal differencing, while spectral analysis performed the weakest in all cases. It is concluded that for each monthly seasonal series, seasonal differencing is the best stationarization method in terms of periodic effect elimination. Moreover, the monthly water temperature is predicted with more accuracy than monthly streamflow. The criteria of the average stochastic term divided by the amplitude of the periodic term obtained for monthly streamflow and monthly water temperature were 0.19 and 0.30, 0.21 and 0.13, and 0.07 and 0.04 respectively. As a result, the periodic term is more dominant than the stochastic term for water temperature in the monthly water temperature series compared to streamflow series.
Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox
DEFF Research Database (Denmark)
Nonejad, Nima
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...... and efficient framework for estimation. These advantages are used to for instance estimate stochastic volatility models with leverage effect or with Student-t distributed errors. We also model changing time series characteristics of the US inflation rate by considering a heteroskedastic ARFIMA model where...
Hybrid model for forecasting time series with trend, seasonal and salendar variation patterns
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.
Road safety forecasts in five European countries using structural time series models.
Antoniou, Constantinos; Papadimitriou, Eleonora; Yannis, George
2014-01-01
Modeling road safety development is a complex task and needs to consider both the quantifiable impact of specific parameters as well as the underlying trends that cannot always be measured or observed. The objective of this research is to apply structural time series models for obtaining reliable medium- to long-term forecasts of road traffic fatality risk using data from 5 countries with different characteristics from all over Europe (Cyprus, Greece, Hungary, Norway, and Switzerland). Two structural time series models are considered: (1) the local linear trend model and the (2) latent risk time series model. Furthermore, a structured decision tree for the selection of the applicable model for each situation (developed within the Road Safety Data, Collection, Transfer and Analysis [DaCoTA] research project, cofunded by the European Commission) is outlined. First, the fatality and exposure data that are used for the development of the models are presented and explored. Then, the modeling process is presented, including the model selection process, introduction of intervention variables, and development of mobility scenarios. The forecasts using the developed models appear to be realistic and within acceptable confidence intervals. The proposed methodology is proved to be very efficient for handling different cases of data availability and quality, providing an appropriate alternative from the family of structural time series models in each country. A concluding section providing perspectives and directions for future research is presented.
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...
Adaptive time-variant models for fuzzy-time-series forecasting.
Wong, Wai-Keung; Bai, Enjian; Chu, Alice Wai-Ching
2010-12-01
A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, and other domains. Related studies mainly focus on three factors, namely, the partition of discourse, the content of forecasting rules, and the methods of defuzzification, all of which greatly influence the prediction accuracy of forecasting models. These studies use fixed analysis window sizes for forecasting. In this paper, an adaptive time-variant fuzzy-time-series forecasting model (ATVF) is proposed to improve forecasting accuracy. The proposed model automatically adapts the analysis window size of fuzzy time series based on the prediction accuracy in the training phase and uses heuristic rules to generate forecasting values in the testing phase. The performance of the ATVF model is tested using both simulated and actual time series including the enrollments at the University of Alabama, Tuscaloosa, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The experiment results show that the proposed ATVF model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series forecasting models.
A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method
Jun-He Yang; Ching-Hsue Cheng; Chia-Pan Chan
2017-01-01
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 m...
Applying ARIMA model for annual volume time series of the Magdalena River
Gloria Amaris; Humberto Ávila; Thomas Guerrero
2017-01-01
Context: Climate change effects, human interventions, and river characteristics are factors that increase the risk on the population and the water resources. However, negative impacts such as flooding, and river droughts may be previously identified using appropriate numerical tools. Objectives: The annual volume (Millions of m3/year) time series of the Magdalena River was analyzed by an ARIMA model, using the historical time series of the Calamar station (Instituto de Hidrología, Meteoro...
Characteristics of the LeRC/Hughes J-series 30-cm engineering model thruster
Collett, C. R.; Poeschel, R. L.; Kami, S.
1981-01-01
As a consequence of endurance and structural tests performed on 900-series engineering model thrusters (EMT), several modifications in design were found to be necessary for achieving performance goals. The modified thruster is known as the J-series EMT. The most important of the design modifications affect the accelerator grid, gimbal mount, cathode polepiece, and wiring harness. The paper discusses the design modifications incorporated, the condition(s) they corrected, and the characteristics of the modified thruster.
Development of Simulink-Based SiC MOSFET Modeling Platform for Series Connected Devices
DEFF Research Database (Denmark)
Tsolaridis, Georgios; Ilves, Kalle; Reigosa, Paula Diaz
2016-01-01
A new MATLAB/Simulink-based modeling platform has been developed for SiC MOSFET power modules. The modeling platform describes the electrical behavior f a single 1.2 kV/ 350 A SiC MOSFET power module, as well as the series connection of two of them. A fast parameter initialization is followed...... by an optimization process to facilitate the extraction of the model’s parameters in a more automated way relying on a small number of experimental waveforms. Through extensive experimental work, it is shown that the model accurately predicts both static and dynamic performances. The series connection of two Si......C power modules has been investigated through the validation of the static and dynamic conditions. Thanks to the developed model, a better understanding of the challenges introduced by uneven voltage balance sharing among series connected devices is possible....
The application of time series models to cloud field morphology analysis
Chin, Roland T.; Jau, Jack Y. C.; Weinman, James A.
1987-01-01
A modeling method for the quantitative description of remotely sensed cloud field images is presented. A two-dimensional texture modeling scheme based on one-dimensional time series procedures is adopted for this purpose. The time series procedure used is the seasonal autoregressive, moving average (ARMA) process in Box and Jenkins. Cloud field properties such as directionality, clustering and cloud coverage can be retrieved by this method. It has been demonstrated that a cloud field image can be quantitatively defined by a small set of parameters and synthesized surrogates can be reconstructed from these model parameters. This method enables cloud climatology to be studied quantitatively.
International Nuclear Information System (INIS)
Dong, Xiangyuan; Guo, Shuqing
2008-01-01
In this paper, a novel image reconstruction method for electrical capacitance tomography (ECT) based on the combined series and parallel model is presented. A regularization technique is used to obtain a stabilized solution of the inverse problem. Also, the adaptive coefficient of the combined model is deduced by numerical optimization. Simulation results indicate that it can produce higher quality images when compared to the algorithm based on the parallel or series models for the cases tested in this paper. It provides a new algorithm for ECT application
Hierarchical Hidden Markov Models for Multivariate Integer-Valued Time-Series
DEFF Research Database (Denmark)
Catania, Leopoldo; Di Mari, Roberto
2018-01-01
We propose a new flexible dynamic model for multivariate nonnegative integer-valued time-series. Observations are assumed to depend on the realization of two additional unobserved integer-valued stochastic variables which control for the time-and cross-dependence of the data. An Expectation......-Maximization algorithm for maximum likelihood estimation of the model's parameters is derived. We provide conditional and unconditional (cross)-moments implied by the model, as well as the limiting distribution of the series. A Monte Carlo experiment investigates the finite sample properties of our estimation...
A new model for reliability optimization of series-parallel systems with non-homogeneous components
International Nuclear Information System (INIS)
Feizabadi, Mohammad; Jahromi, Abdolhamid Eshraghniaye
2017-01-01
In discussions related to reliability optimization using redundancy allocation, one of the structures that has attracted the attention of many researchers, is series-parallel structure. In models previously presented for reliability optimization of series-parallel systems, there is a restricting assumption based on which all components of a subsystem must be homogeneous. This constraint limits system designers in selecting components and prevents achieving higher levels of reliability. In this paper, a new model is proposed for reliability optimization of series-parallel systems, which makes possible the use of non-homogeneous components in each subsystem. As a result of this flexibility, the process of supplying system components will be easier. To solve the proposed model, since the redundancy allocation problem (RAP) belongs to the NP-hard class of optimization problems, a genetic algorithm (GA) is developed. The computational results of the designed GA are indicative of high performance of the proposed model in increasing system reliability and decreasing costs. - Highlights: • In this paper, a new model is proposed for reliability optimization of series-parallel systems. • In the previous models, there is a restricting assumption based on which all components of a subsystem must be homogeneous. • The presented model provides a possibility for the subsystems’ components to be non- homogeneous in the required conditions. • The computational results demonstrate the high performance of the proposed model in improving reliability and reducing costs.
Effect of calibration data series length on performance and optimal parameters of hydrological model
Directory of Open Access Journals (Sweden)
Chuan-zhe Li
2010-12-01
Full Text Available In order to assess the effects of calibration data series length on the performance and optimal parameter values of a hydrological model in ungauged or data-limited catchments (data are non-continuous and fragmental in some catchments, we used non-continuous calibration periods for more independent streamflow data for SIMHYD (simple hydrology model calibration. Nash-Sutcliffe efficiency and percentage water balance error were used as performance measures. The particle swarm optimization (PSO method was used to calibrate the rainfall-runoff models. Different lengths of data series ranging from one year to ten years, randomly sampled, were used to study the impact of calibration data series length. Fifty-five relatively unimpaired catchments located all over Australia with daily precipitation, potential evapotranspiration, and streamflow data were tested to obtain more general conclusions. The results show that longer calibration data series do not necessarily result in better model performance. In general, eight years of data are sufficient to obtain steady estimates of model performance and parameters for the SIMHYD model. It is also shown that most humid catchments require fewer calibration data to obtain a good performance and stable parameter values. The model performs better in humid and semi-humid catchments than in arid catchments. Our results may have useful and interesting implications for the efficiency of using limited observation data for hydrological model calibration in different climates.
Bayesian near-boundary analysis in basic macroeconomic time series models
M.D. de Pooter (Michiel); F. Ravazzolo (Francesco); R. Segers (René); H.K. van Dijk (Herman)
2008-01-01
textabstractSeveral lessons learnt from a Bayesian analysis of basic macroeconomic time series models are presented for the situation where some model parameters have substantial posterior probability near the boundary of the parameter region. This feature refers to near-instability within dynamic
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.
Berendrecht, W.L.; Heemink, A.W.; Geer, F.C. van; Gehrels, J.C.
2003-01-01
A state-space representation of the transfer function-noise (TFN) model allows the choice of a modeling (input) interval that is smaller than the measuring interval of the output variable. Since in geohydrological applications the interval of the available input series (precipitation excess) is
2013-10-31
... an association, business, labor union, etc.). DOT's complete Privacy Act Statement can be found in... supplemental type certificate (STC) change in the digital systems architecture in the Learjet Model 45 series... plus two crew members. The proposed Learjet Model 45 architecture is new and novel for commercial...
Kusev, Petko; van Schaik, Paul; Tsaneva-Atanasova, Krasimira; Juliusson, Asgeir; Chater, Nick
2018-01-01
When attempting to predict future events, people commonly rely on historical data. One psychological characteristic of judgmental forecasting of time series, established by research, is that when people make forecasts from series, they tend to underestimate future values for upward trends and overestimate them for downward ones, so-called trend-damping (modeled by anchoring on, and insufficient adjustment from, the average of recent time series values). Events in a time series can be experienced sequentially (dynamic mode), or they can also be retrospectively viewed simultaneously (static mode), not experienced individually in real time. In one experiment, we studied the influence of presentation mode (dynamic and static) on two sorts of judgment: (a) predictions of the next event (forecast) and (b) estimation of the average value of all the events in the presented series (average estimation). Participants' responses in dynamic mode were anchored on more recent events than in static mode for all types of judgment but with different consequences; hence, dynamic presentation improved prediction accuracy, but not estimation. These results are not anticipated by existing theoretical accounts; we develop and present an agent-based model-the adaptive anchoring model (ADAM)-to account for the difference between processing sequences of dynamically and statically presented stimuli (visually presented data). ADAM captures how variation in presentation mode produces variation in responses (and the accuracy of these responses) in both forecasting and judgment tasks. ADAM's model predictions for the forecasting and judgment tasks fit better with the response data than a linear-regression time series model. Moreover, ADAM outperformed autoregressive-integrated-moving-average (ARIMA) and exponential-smoothing models, while neither of these models accounts for people's responses on the average estimation task. Copyright © 2017 The Authors. Cognitive Science published by Wiley
New insights into soil temperature time series modeling: linear or nonlinear?
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
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.
A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress
2018-01-01
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. PMID:29765399
A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress.
Cheng, Ching-Hsue; Chan, Chia-Pang; Yang, Jun-He
2018-01-01
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.
Nonlinear Prediction Model for Hydrologic Time Series Based on Wavelet Decomposition
Kwon, H.; Khalil, A.; Brown, C.; Lall, U.; Ahn, H.; Moon, Y.
2005-12-01
Traditionally forecasting and characterizations of hydrologic systems is performed utilizing many techniques. Stochastic linear methods such as AR and ARIMA and nonlinear ones such as statistical learning theory based tools have been extensively used. The common difficulty to all methods is the determination of sufficient and necessary information and predictors for a successful prediction. Relationships between hydrologic variables are often highly nonlinear and interrelated across the temporal scale. A new hybrid approach is proposed for the simulation of hydrologic time series combining both the wavelet transform and the nonlinear model. The present model employs some merits of wavelet transform and nonlinear time series model. The Wavelet Transform is adopted to decompose a hydrologic nonlinear process into a set of mono-component signals, which are simulated by nonlinear model. The hybrid methodology is formulated in a manner to improve the accuracy of a long term forecasting. The proposed hybrid model yields much better results in terms of capturing and reproducing the time-frequency properties of the system at hand. Prediction results are promising when compared to traditional univariate time series models. An application of the plausibility of the proposed methodology is provided and the results conclude that wavelet based time series model can be utilized for simulating and forecasting of hydrologic variable reasonably well. This will ultimately serve the purpose of integrated water resources planning and management.
Model-based Clustering of Categorical Time Series with Multinomial Logit Classification
Frühwirth-Schnatter, Sylvia; Pamminger, Christoph; Winter-Ebmer, Rudolf; Weber, Andrea
2010-09-01
A common problem in many areas of applied statistics is to identify groups of similar time series in a panel of time series. However, distance-based clustering methods cannot easily be extended to time series data, where an appropriate distance-measure is rather difficult to define, particularly for discrete-valued time series. Markov chain clustering, proposed by Pamminger and Frühwirth-Schnatter [6], is an approach for clustering discrete-valued time series obtained by observing a categorical variable with several states. This model-based clustering method is based on finite mixtures of first-order time-homogeneous Markov chain models. In order to further explain group membership we present an extension to the approach of Pamminger and Frühwirth-Schnatter [6] by formulating a probabilistic model for the latent group indicators within the Bayesian classification rule by using a multinomial logit model. The parameters are estimated for a fixed number of clusters within a Bayesian framework using an Markov chain Monte Carlo (MCMC) sampling scheme representing a (full) Gibbs-type sampler which involves only draws from standard distributions. Finally, an application to a panel of Austrian wage mobility data is presented which leads to an interesting segmentation of the Austrian labour market.
Improved time series prediction with a new method for selection of model parameters
International Nuclear Information System (INIS)
Jade, A M; Jayaraman, V K; Kulkarni, B D
2006-01-01
A new method for model selection in prediction of time series is proposed. Apart from the conventional criterion of minimizing RMS error, the method also minimizes the error on the distribution of singularities, evaluated through the local Hoelder estimates and its probability density spectrum. Predictions of two simulated and one real time series have been done using kernel principal component regression (KPCR) and model parameters of KPCR have been selected employing the proposed as well as the conventional method. Results obtained demonstrate that the proposed method takes into account the sharp changes in a time series and improves the generalization capability of the KPCR model for better prediction of the unseen test data. (letter to the editor)
Nonlinear time series modeling and forecasting the seismic data of the Hindu Kush region
Khan, Muhammad Yousaf; Mittnik, Stefan
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.
Cointegration and Error Correction Modelling in Time-Series Analysis: A Brief Introduction
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Helmut Thome
2015-07-01
Full Text Available Criminological research is often based on time-series data showing some type of trend movement. Trending time-series may correlate strongly even in cases where no causal relationship exists (spurious causality. To avoid this problem researchers often apply some technique of detrending their data, such as by differencing the series. This approach, however, may bring up another problem: that of spurious non-causality. Both problems can, in principle, be avoided if the series under investigation are “difference-stationary” (if the trend movements are stochastic and “cointegrated” (if the stochastically changing trendmovements in different variables correspond to each other. The article gives a brief introduction to key instruments and interpretative tools applied in cointegration modelling.
Bayesian dynamic modeling of time series of dengue disease case counts.
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
Modeling and Forecasting of Water Demand in Isfahan Using Underlying Trend Concept and Time Series
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H. Sadeghi
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
A multivariate time series approach to modeling and forecasting demand in the emergency department.
Jones, Spencer S; Evans, R Scott; Allen, Todd L; Thomas, Alun; Haug, Peter J; Welch, Shari J; Snow, Gregory L
2009-02-01
The goals of this investigation were to study the temporal relationships between the demands for key resources in the emergency department (ED) and the inpatient hospital, and to develop multivariate forecasting models. Hourly data were collected from three diverse hospitals for the year 2006. Descriptive analysis and model fitting were carried out using graphical and multivariate time series methods. Multivariate models were compared to a univariate benchmark model in terms of their ability to provide out-of-sample forecasts of ED census and the demands for diagnostic resources. Descriptive analyses revealed little temporal interaction between the demand for inpatient resources and the demand for ED resources at the facilities considered. Multivariate models provided more accurate forecasts of ED census and of the demands for diagnostic resources. Our results suggest that multivariate time series models can be used to reliably forecast ED patient census; however, forecasts of the demands for diagnostic resources were not sufficiently reliable to be useful in the clinical setting.
Zeng, Nianyin; Wang, Zidong; Li, Yurong; Du, Min; Cao, Jie; Liu, Xiaohui
2013-12-01
In this paper, the expectation maximization (EM) algorithm is applied to the modeling of the nano-gold immunochromatographic assay (nano-GICA) via available time series of the measured signal intensities of the test and control lines. The model for the nano-GICA is developed as the stochastic dynamic model that consists of a first-order autoregressive stochastic dynamic process and a noisy measurement. By using the EM algorithm, the model parameters, the actual signal intensities of the test and control lines, as well as the noise intensity can be identified simultaneously. Three different time series data sets concerning the target concentrations are employed to demonstrate the effectiveness of the introduced algorithm. Several indices are also proposed to evaluate the inferred models. It is shown that the model fits the data very well.
A time series modeling approach in risk appraisal of violent and sexual recidivism.
Bani-Yaghoub, Majid; Fedoroff, J Paul; Curry, Susan; Amundsen, David E
2010-10-01
For over half a century, various clinical and actuarial methods have been employed to assess the likelihood of violent recidivism. Yet there is a need for new methods that can improve the accuracy of recidivism predictions. This study proposes a new time series modeling approach that generates high levels of predictive accuracy over short and long periods of time. The proposed approach outperformed two widely used actuarial instruments (i.e., the Violence Risk Appraisal Guide and the Sex Offender Risk Appraisal Guide). Furthermore, analysis of temporal risk variations based on specific time series models can add valuable information into risk assessment and management of violent offenders.
The influence of noise on nonlinear time series detection based on Volterra-Wiener-Korenberg model
Energy Technology Data Exchange (ETDEWEB)
Lei Min [State Key Laboratory of Vibration, Shock and Noise, Shanghai Jiao Tong University, Shanghai 200030 (China)], E-mail: leimin@sjtu.edu.cn; Meng Guang [State Key Laboratory of Vibration, Shock and Noise, Shanghai Jiao Tong University, Shanghai 200030 (China)
2008-04-15
This paper studies the influence of noises on Volterra-Wiener-Korenberg (VWK) nonlinear test model. Our numerical results reveal that different types of noises lead to different behavior of VWK model detection. For dynamic noise, it is difficult to distinguish chaos from nonchaotic but nonlinear determinism. For time series, measure noise has no impact on chaos determinism detection. This paper also discusses various behavior of VWK model detection with surrogate data for different noises.
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)
Application of the Laplace transform method for computational modelling of radioactive decay series
Energy Technology Data Exchange (ETDEWEB)
Oliveira, Deise L.; Damasceno, Ralf M.; Barros, Ricardo C. [Univ. do Estado do Rio de Janeiro (IME/UERJ) (Brazil). Programa de Pos-graduacao em Ciencias Computacionais
2012-03-15
It is well known that when spent fuel is removed from the core, it is still composed of considerable amount of radioactive elements with significant half-lives. Most actinides, in particular plutonium, fall into this category, and have to be safely disposed of. One solution is to store the long-lived spent fuel as it is, by encasing and burying it deep underground in a stable geological formation. This implies estimating the transmutation of these radioactive elements with time. Therefore, we describe in this paper the application of the Laplace transform technique in matrix formulation to analytically solve initial value problems that mathematically model radioactive decay series. Given the initial amount of each type of radioactive isotopes in the decay series, the computer code generates the amount at a given time of interest, or may plot a graph of the evolution in time of the amount of each type of isotopes in the series. This computer code, that we refer to as the LTRad{sub L} code, where L is the number of types of isotopes belonging to the series, was developed using the Scilab free platform for numerical computation and can model one segment or the entire chain of any of the three radioactive series existing on Earth today. Numerical results are given to typical model problems to illustrate the computer code efficiency and accuracy. (orig.)
Application of the Laplace transform method for computational modelling of radioactive decay series
International Nuclear Information System (INIS)
Oliveira, Deise L.; Damasceno, Ralf M.; Barros, Ricardo C.
2012-01-01
It is well known that when spent fuel is removed from the core, it is still composed of considerable amount of radioactive elements with significant half-lives. Most actinides, in particular plutonium, fall into this category, and have to be safely disposed of. One solution is to store the long-lived spent fuel as it is, by encasing and burying it deep underground in a stable geological formation. This implies estimating the transmutation of these radioactive elements with time. Therefore, we describe in this paper the application of the Laplace transform technique in matrix formulation to analytically solve initial value problems that mathematically model radioactive decay series. Given the initial amount of each type of radioactive isotopes in the decay series, the computer code generates the amount at a given time of interest, or may plot a graph of the evolution in time of the amount of each type of isotopes in the series. This computer code, that we refer to as the LTRad L code, where L is the number of types of isotopes belonging to the series, was developed using the Scilab free platform for numerical computation and can model one segment or the entire chain of any of the three radioactive series existing on Earth today. Numerical results are given to typical model problems to illustrate the computer code efficiency and accuracy. (orig.)
Directory of Open Access Journals (Sweden)
Yolanda Navarro-Abal
2012-12-01
Full Text Available Television fiction series sometimes generate an unreal vision of life, especially among young people, becoming a mirror in which they can see themselves reflected. The series become models of values, attitudes, skills and behaviours that tend to be imitated by some viewers. The aim of this study was to analyze the conflict management behavioural styles presented by the main characters of television fiction series. Thus, we evaluated the association between these styles and the age and sex of the main characters, as well as the nationality and genre of the fiction series. 16 fiction series were assessed by selecting two characters of both sexes from each series. We adapted the Rahim Organizational Conflict Inventory-II for observing and recording the data. The results show that there is no direct association between the conflict management behavioural styles presented in the drama series and the sex of the main characters. However, associations were found between these styles and the age of the characters and the genre of the fiction series.
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.
Time-series modeling: applications to long-term finfish monitoring data
International Nuclear Information System (INIS)
Bireley, L.E.
1985-01-01
The growing concern and awareness that developed during the 1970's over the effects that industry had on the environment caused the electric utility industry in particular to develop monitoring programs. These programs generate long-term series of data that are not very amenable to classical normal-theory statistical analysis. The monitoring data collected from three finfish programs (impingement, trawl and seine) at the Millstone Nuclear Power Station were typical of such series and thus were used to develop methodology that used the full extent of the information in the series. The basis of the methodology was classic Box-Jenkins time-series modeling; however, the models also included deterministic components that involved flow, season and time as predictor variables. Time entered into the models as harmonic regression terms. Of the 32 models fitted to finfish catch data, 19 were found to account for more than 70% of the historical variation. The models were than used to forecast finfish catches a year in advance and comparisons were made to actual data. Usually the confidence intervals associated with the forecasts encompassed most of the observed data. The technique can provide the basis for intervention analysis in future impact assessments
Time-series modeling of long-term weight self-monitoring data.
Helander, Elina; Pavel, Misha; Jimison, Holly; Korhonen, Ilkka
2015-08-01
Long-term self-monitoring of weight is beneficial for weight maintenance, especially after weight loss. Connected weight scales accumulate time series information over long term and hence enable time series analysis of the data. The analysis can reveal individual patterns, provide more sensitive detection of significant weight trends, and enable more accurate and timely prediction of weight outcomes. However, long term self-weighing data has several challenges which complicate the analysis. Especially, irregular sampling, missing data, and existence of periodic (e.g. diurnal and weekly) patterns are common. In this study, we apply time series modeling approach on daily weight time series from two individuals and describe information that can be extracted from this kind of data. We study the properties of weight time series data, missing data and its link to individuals behavior, periodic patterns and weight series segmentation. Being able to understand behavior through weight data and give relevant feedback is desired to lead to positive intervention on health behaviors.
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.
Time-series models on somatic cell score improve detection of matistis
DEFF Research Database (Denmark)
Norberg, E; Korsgaard, I R; Sloth, K H M N
2008-01-01
In-line detection of mastitis using frequent milk sampling was studied in 241 cows in a Danish research herd. Somatic cell scores obtained at a daily basis were analyzed using a mixture of four time-series models. Probabilities were assigned to each model for the observations to belong to a normal...... "steady-state" development, change in "level", change of "slope" or "outlier". Mastitis was indicated from the sum of probabilities for the "level" and "slope" models. Time-series models were based on the Kalman filter. Reference data was obtained from veterinary assessment of health status combined...... with bacteriological findings. At a sensitivity of 90% the corresponding specificity was 68%, which increased to 83% using a one-step back smoothing. It is concluded that mixture models based on Kalman filters are efficient in handling in-line sensor data for detection of mastitis and may be useful for similar...
Generation of Natural Runoff Monthly Series at Ungauged Sites Using a Regional Regressive Model
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Dario Pumo
2016-05-01
Full Text Available Many hydrologic applications require reliable estimates of runoff in river basins to face the widespread lack of data, both in time and in space. A regional method for the reconstruction of monthly runoff series is here developed and applied to Sicily (Italy. A simple modeling structure is adopted, consisting of a regression-based rainfall–runoff model with four model parameters, calibrated through a two-step procedure. Monthly runoff estimates are based on precipitation, temperature, and exploiting the autocorrelation with runoff at the previous month. Model parameters are assessed by specific regional equations as a function of easily measurable physical and climate basin descriptors. The first calibration step is aimed at the identification of a set of parameters optimizing model performances at the level of single basin. Such “optimal” sets are used at the second step, part of a regional regression analysis, to establish the regional equations for model parameters assessment as a function of basin attributes. All the gauged watersheds across the region have been analyzed, selecting 53 basins for model calibration and using the other six basins exclusively for validation. Performances, quantitatively evaluated by different statistical indexes, demonstrate relevant model ability in reproducing the observed hydrological time-series at both the monthly and coarser time resolutions. The methodology, which is easily transferable to other arid and semi-arid areas, provides a reliable tool for filling/reconstructing runoff time series at any gauged or ungauged basin of a region.
Time series modeling by a regression approach based on a latent process.
Chamroukhi, Faicel; Samé, Allou; Govaert, Gérard; Aknin, Patrice
2009-01-01
Time series are used in many domains including finance, engineering, economics and bioinformatics generally to represent the change of a measurement over time. Modeling techniques may then be used to give a synthetic representation of such data. A new approach for time series modeling is proposed in this paper. It consists of a regression model incorporating a discrete hidden logistic process allowing for activating smoothly or abruptly different polynomial regression models. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. The M step of the EM algorithm uses a multi-class Iterative Reweighted Least-Squares (IRLS) algorithm to estimate the hidden process parameters. To evaluate the proposed approach, an experimental study on simulated data and real world data was performed using two alternative approaches: a heteroskedastic piecewise regression model using a global optimization algorithm based on dynamic programming, and a Hidden Markov Regression Model whose parameters are estimated by the Baum-Welch algorithm. Finally, in the context of the remote monitoring of components of the French railway infrastructure, and more particularly the switch mechanism, the proposed approach has been applied to modeling and classifying time series representing the condition measurements acquired during switch operations.
Transfer function modeling of the monthly accumulated rainfall series over the Iberian Peninsula
Energy Technology Data Exchange (ETDEWEB)
Mateos, Vidal L.; Garcia, Jose A.; Serrano, Antonio; De la Cruz Gallego, Maria [Departamento de Fisica, Universidad de Extremadura, Badajoz (Spain)
2002-10-01
In order to improve the results given by Autoregressive Moving-Average (ARMA) modeling for the monthly accumulated rainfall series taken at 19 observatories of the Iberian Peninsula, a Discrete Linear Transfer Function Noise (DLTFN) model was applied taking the local pressure series (LP), North Atlantic sea level pressure series (SLP) and North Atlantic sea surface temperature (SST) as input variables, and the rainfall series as the output series. In all cases, the performance of the DLTFN models, measured by the explained variance of the rainfall series, is better than the performance given by the ARMA modeling. The best performance is given by the models that take the local pressure as the input variable, followed by the sea level pressure models and the sea surface temperature models. Geographically speaking, the models fitted to those observatories located in the west of the Iberian Peninsula work better than those on the north and east of the Peninsula. Also, it was found that there is a region located between 0 N and 20 N, which shows the highest cross-correlation between SST and the peninsula rainfalls. This region moves to the west and northwest off the Peninsula when the SLP series are used. [Spanish] Con el objeto de mejorar los resultados porporcionados por los modelos Autorregresivo Media Movil (ARMA) ajustados a las precipitaciones mensuales acumuladas registradas en 19 observatorios de la Peninsula Iberica se han usado modelos de funcion de transferencia (DLTFN) en los que se han empleado como variable independiente la presion local (LP), la presion a nivel del mar (SLP) o la temperatura de agua del mar (SST) en el Atlantico Norte. En todos los casos analizados, los resultados obtenidos con los modelos DLTFN, medidos mediante la varianza explicada por el modelo, han sido mejores que los resultados proporcionados por los modelos ARMA. Los mejores resultados han sido dados por aquellos modelos que usan la presion local como variable de entrada, seguidos
DEFF Research Database (Denmark)
Kamel, S.; Jurado, F.; Chen, Zhe
2015-01-01
This paper presents an implicit modeling of Static Synchronous Series Compensator (SSSC) in Newton–Raphson load flow method. The algorithm of load flow is based on the revised current injection formulation. The developed model of SSSC is depended on the current injection approach. In this model...... will be in the mismatches vector. Finally, this modeling solves the problem that happens when the SSSC is only connected between two areas. Numerical examples on the WSCC 9-bus, IEEE 30-bus system, and IEEE 118-bus system are used to illustrate the feasibility of the developed SSSC model and performance of the Newton–Raphson...
Studies in astronomical time series analysis. I - Modeling random processes in the time domain
Scargle, J. D.
1981-01-01
Several random process models in the time domain are defined and discussed. Attention is given to the moving average model, the autoregressive model, and relationships between and combinations of these models. Consideration is then given to methods for investigating pulse structure, procedures of model construction, computational methods, and numerical experiments. A FORTRAN algorithm of time series analysis has been developed which is relatively stable numerically. Results of test cases are given to study the effect of adding noise and of different distributions for the pulse amplitudes. A preliminary analysis of the light curve of the quasar 3C 272 is considered as an example.
Data on copula modeling of mixed discrete and continuous neural time series.
Hu, Meng; Li, Mingyao; Li, Wu; Liang, Hualou
2016-06-01
Copula is an important tool for modeling neural dependence. Recent work on copula has been expanded to jointly model mixed time series in neuroscience ("Hu et al., 2016, Joint Analysis of Spikes and Local Field Potentials using Copula" [1]). Here we present further data for joint analysis of spike and local field potential (LFP) with copula modeling. In particular, the details of different model orders and the influence of possible spike contamination in LFP data from the same and different electrode recordings are presented. To further facilitate the use of our copula model for the analysis of mixed data, we provide the Matlab codes, together with example data.
Modeling the impact of forecast-based regime switches on macroeconomic time series
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
76 FR 36390 - Airworthiness Directives; The Boeing Company Model 747SP Series Airplanes
2011-06-22
... power control modules (PCM). This proposed AD was prompted by a report of a rudder hard-over event on a... rudder PCM manifold, which could result in a hard-over of the rudder surface leading to an increase in... of a Model 747-400 series airplane of a lower rudder hard-over event caused by a lower rudder PCM...
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
A robust interrupted time series model for analyzing complex health care intervention data
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
Molenaar, P.C.M.
1987-01-01
Outlines a frequency domain analysis of the dynamic factor model and proposes a solution to the problem of constructing a causal filter of lagged factor loadings. The method is illustrated with applications to simulated and real multivariate time series. The latter applications involve topographic
ShapeSelectForest: a new r package for modeling landsat time series
Mary Meyer; Xiyue Liao; Gretchen Moisen; Elizabeth Freeman
2015-01-01
We present a new R package called ShapeSelectForest recently posted to the Comprehensive R Archival Network. The package was developed to fit nonparametric shape-restricted regression splines to time series of Landsat imagery for the purpose of modeling, mapping, and monitoring annual forest disturbance dynamics over nearly three decades. For each pixel and spectral...
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...
A Model for Mental Health Programming in Schools and Communities: Introduction to the Mini-Series.
Nastasi, Bonnie K.
1998-01-01
Describes conceptual framework for mini-series on mental health programming. Model includes five components considered to be critical for comprehensive and effective programming. Components include: action research, ecological perspective of program development, collaborative/participatory approach, prevention to treatment via mental health…
76 FR 6584 - Airworthiness Directives; Bombardier, Inc. Model DHC-8-400 Series Airplanes
2011-02-07
.... Model DHC-8-400 Series Airplanes AGENCY: Federal Aviation Administration (FAA), DOT. ACTION: Notice of... area on the rib at Yw-42.000 to ensure adequate electrical bonding, installing spiral wrap on certain cable assemblies where existing spiral wrap does not extend 4 inches past the tie mounts, applying a...
A robust interrupted time series model for analyzing complex health care intervention data
Cruz, Maricela; Bender, Miriam; Ombao, Hernando
2017-01-01
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
Evaluation of the autoregression time-series model for analysis of a noisy signal
International Nuclear Information System (INIS)
Allen, J.W.
1977-01-01
The autoregression (AR) time-series model of a continuous noisy signal was statistically evaluated to determine quantitatively the uncertainties of the model order, the model parameters, and the model's power spectral density (PSD). The result of such a statistical evaluation enables an experimenter to decide whether an AR model can adequately represent a continuous noisy signal and be consistent with the signal's frequency spectrum, and whether it can be used for on-line monitoring. Although evaluations of other types of signals have been reported in the literature, no direct reference has been found to AR model's uncertainties for continuous noisy signals; yet the evaluation is necessary to decide the usefulness of AR models of typical reactor signals (e.g., neutron detector output or thermocouple output) and the potential of AR models for on-line monitoring applications. AR and other time-series models for noisy data representation are being investigated by others since such models require fewer parameters than the traditional PSD model. For this study, the AR model was selected for its simplicity and conduciveness to uncertainty analysis, and controlled laboratory bench signals were used for continuous noisy data. (author)
Modeling pollen time series using seasonal-trend decomposition procedure based on LOESS smoothing.
Rojo, Jesús; Rivero, Rosario; Romero-Morte, Jorge; Fernández-González, Federico; Pérez-Badia, Rosa
2017-02-01
Analysis of airborne pollen concentrations provides valuable information on plant phenology and is thus a useful tool in agriculture-for predicting harvests in crops such as the olive and for deciding when to apply phytosanitary treatments-as well as in medicine and the environmental sciences. Variations in airborne pollen concentrations, moreover, are indicators of changing plant life cycles. By modeling pollen time series, we can not only identify the variables influencing pollen levels but also predict future pollen concentrations. In this study, airborne pollen time series were modeled using a seasonal-trend decomposition procedure based on LOcally wEighted Scatterplot Smoothing (LOESS) smoothing (STL). The data series-daily Poaceae pollen concentrations over the period 2006-2014-was broken up into seasonal and residual (stochastic) components. The seasonal component was compared with data on Poaceae flowering phenology obtained by field sampling. Residuals were fitted to a model generated from daily temperature and rainfall values, and daily pollen concentrations, using partial least squares regression (PLSR). This method was then applied to predict daily pollen concentrations for 2014 (independent validation data) using results for the seasonal component of the time series and estimates of the residual component for the period 2006-2013. Correlation between predicted and observed values was r = 0.79 (correlation coefficient) for the pre-peak period (i.e., the period prior to the peak pollen concentration) and r = 0.63 for the post-peak period. Separate analysis of each of the components of the pollen data series enables the sources of variability to be identified more accurately than by analysis of the original non-decomposed data series, and for this reason, this procedure has proved to be a suitable technique for analyzing the main environmental factors influencing airborne pollen concentrations.
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.
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.
Analysis of Data from a Series of Events by a Geometric Process Model
Institute of Scientific and Technical Information of China (English)
Yeh Lam; Li-xing Zhu; Jennifer S. K. Chan; Qun Liu
2004-01-01
Geometric process was first introduced by Lam[10,11]. A stochastic process {Xi, i = 1, 2,…} is called a geometric process (GP) if, for some a > 0, {ai-1Xi, i = 1, 2,…} forms a renewal process. In thispaper, the GP is used to analyze the data from a series of events. A nonparametric method is introduced forthe estimation of the three parameters in the GP. The limiting distributions of the three estimators are studied.Through the analysis of some real data sets, the GP model is compared with other three homogeneous andnonhomogeneous Poisson models. It seems that on average the GP model is the best model among these fourmodels in analyzing the data from a series of events.
A regional and nonstationary model for partial duration series of extreme rainfall
DEFF Research Database (Denmark)
Gregersen, Ida Bülow; Madsen, Henrik; Rosbjerg, Dan
2017-01-01
as the explanatory variables in the regional and temporal domain, respectively. Further analysis of partial duration series with nonstationary and regional thresholds shows that the mean exceedances also exhibit a significant variation in space and time for some rainfall durations, while the shape parameter is found...... of extreme rainfall. The framework is built on a partial duration series approach with a nonstationary, regional threshold value. The model is based on generalized linear regression solved by generalized estimation equations. It allows a spatial correlation between the stations in the network and accounts...... furthermore for variable observation periods at each station and in each year. Marginal regional and temporal regression models solved by generalized least squares are used to validate and discuss the results of the full spatiotemporal model. The model is applied on data from a large Danish rain gauge network...
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
Automated Bayesian model development for frequency detection in biological time series.
Granqvist, Emma; Oldroyd, Giles E D; Morris, Richard J
2011-06-24
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. 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. 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 the requirement for uniformly sampled data. Biological time
Vector Control Using Series Iron Loss Model of Induction, Motors and Power Loss Minimization
Kheldoun Aissa; Khodja Djalal Eddine
2009-01-01
The iron loss is a source of detuning in vector controlled induction motor drives if the classical rotor vector controller is used for decoupling. In fact, the field orientation will not be satisfied and the output torque will not truck the reference torque mostly used by Loss Model Controllers (LMCs). In addition, this component of loss, among others, may be excessive if the vector controlled induction motor is driving light loads. In this paper, the series iron loss model ...
Lohani, A. K.; Kumar, Rakesh; Singh, R. D.
2012-06-01
SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.
Multi-Step Time Series Forecasting with an Ensemble of Varied Length Mixture Models.
Ouyang, Yicun; Yin, Hujun
2018-05-01
Many real-world problems require modeling and forecasting of time series, such as weather temperature, electricity demand, stock prices and foreign exchange (FX) rates. Often, the tasks involve predicting over a long-term period, e.g. several weeks or months. Most existing time series models are inheritably for one-step prediction, that is, predicting one time point ahead. Multi-step or long-term prediction is difficult and challenging due to the lack of information and uncertainty or error accumulation. The main existing approaches, iterative and independent, either use one-step model recursively or treat the multi-step task as an independent model. They generally perform poorly in practical applications. In this paper, as an extension of the self-organizing mixture autoregressive (AR) model, the varied length mixture (VLM) models are proposed to model and forecast time series over multi-steps. The key idea is to preserve the dependencies between the time points within the prediction horizon. Training data are segmented to various lengths corresponding to various forecasting horizons, and the VLM models are trained in a self-organizing fashion on these segments to capture these dependencies in its component AR models of various predicting horizons. The VLM models form a probabilistic mixture of these varied length models. A combination of short and long VLM models and an ensemble of them are proposed to further enhance the prediction performance. The effectiveness of the proposed methods and their marked improvements over the existing methods are demonstrated through a number of experiments on synthetic data, real-world FX rates and weather temperatures.
75 FR 16685 - Airworthiness Directives; Airbus Model A300-600 and A310 Airplanes
2010-04-02
... leads to degradation of the handling qualities and reduces the controllability of the aeroplane. The... integrity of the rudder. The loss of the rudder leads to degradation of the handling qualities and reduces...; equipped with carbon fiber reinforced plastic rudders having part numbers and serial numbers listed in...
PSO-MISMO modeling strategy for multistep-ahead time series prediction.
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.
DEFF Research Database (Denmark)
Ørregård Nielsen, Morten
This paper proves consistency and asymptotic normality for the conditional-sum-of-squares estimator, which is equivalent to the conditional maximum likelihood estimator, in multivariate fractional time series models. The model is parametric and quite general, and, in particular, encompasses...... the multivariate non-cointegrated fractional ARIMA model. The novelty of the consistency result, in particular, is that it applies to a multivariate model and to an arbitrarily large set of admissible parameter values, for which the objective function does not converge uniformly in probablity, thus making...
Time series models of environmental exposures: Good predictions or good understanding.
Barnett, Adrian G; Stephen, Dimity; Huang, Cunrui; Wolkewitz, Martin
2017-04-01
Time series data are popular in environmental epidemiology as they make use of the natural experiment of how changes in exposure over time might impact on disease. Many published time series papers have used parameter-heavy models that fully explained the second order patterns in disease to give residuals that have no short-term autocorrelation or seasonality. This is often achieved by including predictors of past disease counts (autoregression) or seasonal splines with many degrees of freedom. These approaches give great residuals, but add little to our understanding of cause and effect. We argue that modelling approaches should rely more on good epidemiology and less on statistical tests. This includes thinking about causal pathways, making potential confounders explicit, fitting a limited number of models, and not over-fitting at the cost of under-estimating the true association between exposure and disease. Copyright © 2017 Elsevier Inc. All rights reserved.
Statistical models and time series forecasting of sulfur dioxide: a case study Tehran.
Hassanzadeh, S; Hosseinibalam, F; Alizadeh, R
2009-08-01
This study performed a time-series analysis, frequency distribution and prediction of SO(2) levels for five stations (Pardisan, Vila, Azadi, Gholhak and Bahman) in Tehran for the period of 2000-2005. Most sites show a quite similar characteristic with highest pollution in autumn-winter time and least pollution in spring-summer. The frequency distributions show higher peaks at two residential sites. The potential for SO(2) problems is high because of high emissions and the close geographical proximity of the major industrial and urban centers. The ACF and PACF are nonzero for several lags, indicating a mixed (ARMA) model, then at Bahman station an ARMA model was used for forecasting SO(2). The partial autocorrelations become close to 0 after about 5 lags while the autocorrelations remain strong through all the lags shown. The results proved that ARMA (2,2) model can provides reliable, satisfactory predictions for time series.
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.
Li, Qiongge; Chan, Maria F
2017-01-01
Over half of cancer patients receive radiotherapy (RT) as partial or full cancer treatment. Daily quality assurance (QA) of RT in cancer treatment closely monitors the performance of the medical linear accelerator (Linac) and is critical for continuous improvement of patient safety and quality of care. Cumulative longitudinal QA measurements are valuable for understanding the behavior of the Linac and allow physicists to identify trends in the output and take preventive actions. In this study, artificial neural networks (ANNs) and autoregressive moving average (ARMA) time-series prediction modeling techniques were both applied to 5-year daily Linac QA data. Verification tests and other evaluations were then performed for all models. Preliminary results showed that ANN time-series predictive modeling has more advantages over ARMA techniques for accurate and effective applicability in the dosimetry and QA field. © 2016 New York Academy of Sciences.
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.
The Gaussian Graphical Model in Cross-Sectional and Time-Series Data.
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.
Faes, Luca; Nollo, Giandomenico
2010-11-01
The Partial Directed Coherence (PDC) and its generalized formulation (gPDC) are popular tools for investigating, in the frequency domain, the concept of Granger causality among multivariate (MV) time series. PDC and gPDC are formalized in terms of the coefficients of an MV autoregressive (MVAR) model which describes only the lagged effects among the time series and forsakes instantaneous effects. However, instantaneous effects are known to affect linear parametric modeling, and are likely to occur in experimental time series. In this study, we investigate the impact on the assessment of frequency domain causality of excluding instantaneous effects from the model underlying PDC evaluation. Moreover, we propose the utilization of an extended MVAR model including both instantaneous and lagged effects. This model is used to assess PDC either in accordance with the definition of Granger causality when considering only lagged effects (iPDC), or with an extended form of causality, when we consider both instantaneous and lagged effects (ePDC). The approach is first evaluated on three theoretical examples of MVAR processes, which show that the presence of instantaneous correlations may produce misleading profiles of PDC and gPDC, while ePDC and iPDC derived from the extended model provide here a correct interpretation of extended and lagged causality. It is then applied to representative examples of cardiorespiratory and EEG MV time series. They suggest that ePDC and iPDC are better interpretable than PDC and gPDC in terms of the known cardiovascular and neural physiologies.
A Bayesian Approach for Summarizing and Modeling Time-Series Exposure Data with Left Censoring.
Houseman, E Andres; Virji, M Abbas
2017-08-01
Direct reading instruments are valuable tools for measuring exposure as they provide real-time measurements for rapid decision making. However, their use is limited to general survey applications in part due to issues related to their performance. Moreover, statistical analysis of real-time data is complicated by autocorrelation among successive measurements, non-stationary time series, and the presence of left-censoring due to limit-of-detection (LOD). A Bayesian framework is proposed that accounts for non-stationary autocorrelation and LOD issues in exposure time-series data in order to model workplace factors that affect exposure and estimate summary statistics for tasks or other covariates of interest. A spline-based approach is used to model non-stationary autocorrelation with relatively few assumptions about autocorrelation structure. Left-censoring is addressed by integrating over the left tail of the distribution. The model is fit using Markov-Chain Monte Carlo within a Bayesian paradigm. The method can flexibly account for hierarchical relationships, random effects and fixed effects of covariates. The method is implemented using the rjags package in R, and is illustrated by applying it to real-time exposure data. Estimates for task means and covariates from the Bayesian model are compared to those from conventional frequentist models including linear regression, mixed-effects, and time-series models with different autocorrelation structures. Simulations studies are also conducted to evaluate method performance. Simulation studies with percent of measurements below the LOD ranging from 0 to 50% showed lowest root mean squared errors for task means and the least biased standard deviations from the Bayesian model compared to the frequentist models across all levels of LOD. In the application, task means from the Bayesian model were similar to means from the frequentist models, while the standard deviations were different. Parameter estimates for covariates
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)
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.
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.
Time series modeling for analysis and control advanced autopilot and monitoring systems
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...
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 3×10^{9} 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.
Evaluating an Automated Number Series Item Generator Using Linear Logistic Test Models
Directory of Open Access Journals (Sweden)
Bao Sheng Loe
2018-04-01
Full Text Available This study investigates the item properties of a newly developed Automatic Number Series Item Generator (ANSIG. The foundation of the ANSIG is based on five hypothesised cognitive operators. Thirteen item models were developed using the numGen R package and eleven were evaluated in this study. The 16-item ICAR (International Cognitive Ability Resource1 short form ability test was used to evaluate construct validity. The Rasch Model and two Linear Logistic Test Model(s (LLTM were employed to estimate and predict the item parameters. Results indicate that a single factor determines the performance on tests composed of items generated by the ANSIG. Under the LLTM approach, all the cognitive operators were significant predictors of item difficulty. Moderate to high correlations were evident between the number series items and the ICAR test scores, with high correlation found for the ICAR Letter-Numeric-Series type items, suggesting adequate nomothetic span. Extended cognitive research is, nevertheless, essential for the automatic generation of an item pool with predictable psychometric properties.
A time series model: First-order integer-valued autoregressive (INAR(1))
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.
Model for the respiratory modulation of the heart beat-to-beat time interval series
Capurro, Alberto; Diambra, Luis; Malta, C. P.
2005-09-01
In this study we present a model for the respiratory modulation of the heart beat-to-beat interval series. The model consists of a set of differential equations used to simulate the membrane potential of a single rabbit sinoatrial node cell, excited with a periodic input signal with added correlated noise. This signal, which simulates the input from the autonomous nervous system to the sinoatrial node, was included in the pacemaker equations as a modulation of the iNaK current pump and the potassium current iK. We focus at modeling the heart beat-to-beat time interval series from normal subjects during meditation of the Kundalini Yoga and Chi techniques. The analysis of the experimental data indicates that while the embedding of pre-meditation and control cases have a roughly circular shape, it acquires a polygonal shape during meditation, triangular for the Kundalini Yoga data and quadrangular in the case of Chi data. The model was used to assess the waveshape of the respiratory signals needed to reproduce the trajectory of the experimental data in the phase space. The embedding of the Chi data could be reproduced using a periodic signal obtained by smoothing a square wave. In the case of Kundalini Yoga data, the embedding was reproduced with a periodic signal obtained by smoothing a triangular wave having a rising branch of longer duration than the decreasing branch. Our study provides an estimation of the respiratory signal using only the heart beat-to-beat time interval series.
Extracting Knowledge From Time Series An Introduction to Nonlinear Empirical Modeling
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.
The string prediction models as an invariants of time series in forex market
Richard Pincak; Marian Repasan
2011-01-01
In this paper we apply a new approach of the string theory to the real financial market. It is direct extension and application of the work [1] into prediction of prices. 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. Brief overview of the results and analysis is given. The first model is ...
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…
A 310-bp minimal promoter mediates smooth muscle cell-specific expression of telokin.
Smith, A F; Bigsby, R M; Word, R A; Herring, B P
1998-05-01
A cell-specific promoter located in an intron of the smooth muscle myosin light chain kinase gene directs transcription of telokin exclusively in smooth muscle cells. Transgenic mice were generated in which a 310-bp rabbit telokin promoter fragment, extending from -163 to +147, was used to drive expression of simian virus 40 large T antigen. Smooth muscle-specific expression of the T-antigen transgene paralleled that of the endogenous telokin gene in all smooth muscle tissues except uterus. The 310-bp promoter fragment resulted in very low levels of transgene expression in uterus; in contrast, a transgene driven by a 2.4-kb fragment (-2250 to +147) resulted in high levels of transgene expression in uterine smooth muscle. Telokin expression levels correlate with the estrogen status of human myometrial tissues, suggesting that deletion of an estrogen response element (ERE) may account for the low levels of transgene expression driven by the 310-bp rabbit telokin promoter in uterine smooth muscle. Experiments in A10 smooth muscle cells directly showed that reporter gene expression driven by the 2.4-kb, but not 310-bp, promoter fragment could be stimulated two- to threefold by estrogen. This stimulation was mediated through an ERE located between -1447 and -1474. Addition of the ERE to the 310-bp fragment restored estrogen responsiveness in A10 cells. These data demonstrate that in addition to a minimal 310-bp proximal promoter at least one distal cis-acting regulatory element is required for telokin expression in uterine smooth muscle. The distal element may include an ERE between -1447 and -1474.
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.
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.
Modeling of plasma chemistry in a corona streamer pulse series in air
International Nuclear Information System (INIS)
Nowakowska, H.; Stanco, J.; Dors, M.; Mizeraczyk, J.
2002-01-01
The aim of this study is to analyse the chemistry in air treated by a series of corona discharge streamers. Attention is focused on the conversion of ozone and nitrogen oxides. In the model it is assumed that the streamer head of relatively small geometrical dimensions propagates from the anode to the cathode, leaving the streamer channel behind. Any elemental gas volume in the streamer path is subjected first to the conditions of the streamer head, and next to those of the streamer channel. The kinetics of plasma-chemical processes occurring in the gas is modeled numerically for a single streamer and a series of streamers. The temporal evolution of 25 chemical compounds initially present or produced in air is calculated. (author)
Time series modelling to forecast prehospital EMS demand for diabetic emergencies.
Villani, Melanie; Earnest, Arul; Nanayakkara, Natalie; Smith, Karen; de Courten, Barbora; Zoungas, Sophia
2017-05-05
Acute diabetic emergencies are often managed by prehospital Emergency Medical Services (EMS). The projected growth in prevalence of diabetes is likely to result in rising demand for prehospital EMS that are already under pressure. The aims of this study were to model the temporal trends and provide forecasts of prehospital attendances for diabetic emergencies. A time series analysis on monthly cases of hypoglycemia and hyperglycemia was conducted using data from the Ambulance Victoria (AV) electronic database between 2009 and 2015. Using the seasonal autoregressive integrated moving average (SARIMA) modelling process, different models were evaluated. The most parsimonious model with the highest accuracy was selected. Forty-one thousand four hundred fifty-four prehospital diabetic emergencies were attended over a seven-year period with an increase in the annual median monthly caseload between 2009 (484.5) and 2015 (549.5). Hypoglycemia (70%) and people with type 1 diabetes (48%) accounted for most attendances. The SARIMA (0,1,0,12) model provided the best fit, with a MAPE of 4.2% and predicts a monthly caseload of approximately 740 by the end of 2017. Prehospital EMS demand for diabetic emergencies is increasing. SARIMA time series models are a valuable tool to allow forecasting of future caseload with high accuracy and predict increasing cases of prehospital diabetic emergencies into the future. The model generated by this study may be used by service providers to allow appropriate planning and resource allocation of EMS for diabetic emergencies.
Prediction of traffic-related nitrogen oxides concentrations using Structural Time-Series models
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.
Applications of soft computing in time series forecasting simulation and modeling techniques
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...
Time Series Modeling of Army Mission Command Communication Networks: An Event-Driven Analysis
2013-06-01
Lehmann, D. R. (1984). How advertising affects sales: Meta- analysis of econometric results. Journal of Marketing Research , 21, 65-74. Barabási, A. L...317-357. Leone, R. P. (1983). Modeling sales-advertising relationships: An integrated time series- econometric approach. Journal of Marketing ... Research , 20, 291-295. McGrath, J. E., & Kravitz, D. A. (1982). Group research. Annual Review of Psychology, 33, 195- 230. Monge, P. R., & Contractor
Modeling multivariate time series on manifolds with skew radial basis functions.
Jamshidi, Arta A; Kirby, Michael J
2011-01-01
We present an approach for constructing nonlinear empirical mappings from high-dimensional domains to multivariate ranges. We employ radial basis functions and skew radial basis functions for constructing a model using data that are potentially scattered or sparse. The algorithm progresses iteratively, adding a new function at each step to refine the model. The placement of the functions is driven by a statistical hypothesis test that accounts for correlation in the multivariate range variables. The test is applied on training and validation data and reveals nonstatistical or geometric structure when it fails. At each step, the added function is fit to data contained in a spatiotemporally defined local region to determine the parameters--in particular, the scale of the local model. The scale of the function is determined by the zero crossings of the autocorrelation function of the residuals. The model parameters and the number of basis functions are determined automatically from the given data, and there is no need to initialize any ad hoc parameters save for the selection of the skew radial basis functions. Compactly supported skew radial basis functions are employed to improve model accuracy, order, and convergence properties. The extension of the algorithm to higher-dimensional ranges produces reduced-order models by exploiting the existence of correlation in the range variable data. Structure is tested not just in a single time series but between all pairs of time series. We illustrate the new methodologies using several illustrative problems, including modeling data on manifolds and the prediction of chaotic time series.
Time series modeling of live-cell shape dynamics for image-based phenotypic profiling.
Gordonov, Simon; Hwang, Mun Kyung; Wells, Alan; Gertler, Frank B; Lauffenburger, Douglas A; Bathe, Mark
2016-01-01
Live-cell imaging can be used to capture spatio-temporal aspects of cellular responses that are not accessible to fixed-cell imaging. As the use of live-cell imaging continues to increase, new computational procedures are needed to characterize and classify the temporal dynamics of individual cells. For this purpose, here we present the general experimental-computational framework SAPHIRE (Stochastic Annotation of Phenotypic Individual-cell Responses) to characterize phenotypic cellular responses from time series imaging datasets. Hidden Markov modeling is used to infer and annotate morphological state and state-switching properties from image-derived cell shape measurements. Time series modeling is performed on each cell individually, making the approach broadly useful for analyzing asynchronous cell populations. Two-color fluorescent cells simultaneously expressing actin and nuclear reporters enabled us to profile temporal changes in cell shape following pharmacological inhibition of cytoskeleton-regulatory signaling pathways. Results are compared with existing approaches conventionally applied to fixed-cell imaging datasets, and indicate that time series modeling captures heterogeneous dynamic cellular responses that can improve drug classification and offer additional important insight into mechanisms of drug action. The software is available at http://saphire-hcs.org.
a Landsat Time-Series Stacks Model for Detection of Cropland Change
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.
2010-02-12
... replacing aging float level switch conduit assemblies, periodically inspecting the external dry bay system... Model 737-700 (IGW) Series Airplanes Equipped With Auxiliary Fuel Tanks Installed in Accordance With... airworthiness directive (AD) for certain Model 737-700 (IGW) series airplanes. This proposed AD would require...
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
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";}
Mukhin, Dmitry; Gavrilov, Andrey; Loskutov, Evgeny; Feigin, Alexander
2016-04-01
We suggest a method for empirical forecast of climate dynamics basing on the reconstruction of reduced dynamical models in a form of random dynamical systems [1,2] derived from observational time series. The construction of proper embedding - the set of variables determining the phase space the model works in - is no doubt the most important step in such a modeling, but this task is non-trivial due to huge dimension of time series of typical climatic fields. Actually, an appropriate expansion of observational time series is needed yielding the number of principal components considered as phase variables, which are to be efficient for the construction of low-dimensional evolution operator. We emphasize two main features the reduced models should have for capturing the main dynamical properties of the system: (i) taking into account time-lagged teleconnections in the atmosphere-ocean system and (ii) reflecting the nonlinear nature of these teleconnections. In accordance to these principles, in this report we present the methodology which includes the combination of a new way for the construction of an embedding by the spatio-temporal data expansion and nonlinear model construction on the basis of artificial neural networks. The methodology is aplied to NCEP/NCAR reanalysis data including fields of sea level pressure, geopotential height, and wind speed, covering Northern Hemisphere. Its efficiency for the interannual forecast of various climate phenomena including ENSO, PDO, NAO and strong blocking event condition over the mid latitudes, is demonstrated. Also, we investigate the ability of the models to reproduce and predict the evolution of qualitative features of the dynamics, such as spectral peaks, critical transitions and statistics of extremes. This research was supported by the Government of the Russian Federation (Agreement No. 14.Z50.31.0033 with the Institute of Applied Physics RAS) [1] Y. I. Molkov, E. M. Loskutov, D. N. Mukhin, and A. M. Feigin, "Random
A stochastic HMM-based forecasting model for fuzzy time series.
Li, Sheng-Tun; Cheng, Yi-Chung
2010-10-01
Recently, fuzzy time series have attracted more academic attention than traditional time series due to their capability of dealing with the uncertainty and vagueness inherent in the data collected. The formulation of fuzzy relations is one of the key issues affecting forecasting results. Most of the present works adopt IF-THEN rules for relationship representation, which leads to higher computational overhead and rule redundancy. Sullivan and Woodall proposed a Markov-based formulation and a forecasting model to reduce computational overhead; however, its applicability is limited to handling one-factor problems. In this paper, we propose a novel forecasting model based on the hidden Markov model by enhancing Sullivan and Woodall's work to allow handling of two-factor forecasting problems. Moreover, in order to make the nature of conjecture and randomness of forecasting more realistic, the Monte Carlo method is adopted to estimate the outcome. To test the effectiveness of the resulting stochastic model, we conduct two experiments and compare the results with those from other models. The first experiment consists of forecasting the daily average temperature and cloud density in Taipei, Taiwan, and the second experiment is based on the Taiwan Weighted Stock Index by forecasting the exchange rate of the New Taiwan dollar against the U.S. dollar. In addition to improving forecasting accuracy, the proposed model adheres to the central limit theorem, and thus, the result statistically approximates to the real mean of the target value being forecast.
A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method.
Yang, Jun-He; Cheng, Ching-Hsue; Chan, Chia-Pan
2017-01-01
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.
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.
Directory of Open Access Journals (Sweden)
Mansoor Ahmed Siddiqui
2017-06-01
Full Text Available This research work is aimed at optimizing the availability of a framework comprising of two units linked together in series configuration utilizing Markov Model and Monte Carlo (MC Simulation techniques. In this article, effort has been made to develop a maintenance model that incorporates three distinct states for each unit, while taking into account their different levels of deterioration. Calculations are carried out using the proposed model for two distinct cases of corrective repair, namely perfect and imperfect repairs, with as well as without opportunistic maintenance. Initially, results are accomplished using an analytical technique i.e., Markov Model. Validation of the results achieved is later carried out with the help of MC Simulation. In addition, MC Simulation based codes also work well for the frameworks that follow non-exponential failure and repair rates, and thus overcome the limitations offered by the Markov Model.
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.
Estimating and Analyzing Savannah Phenology with a Lagged Time Series Model
DEFF Research Database (Denmark)
Boke-Olen, Niklas; Lehsten, Veiko; Ardo, Jonas
2016-01-01
cycle due to their areal coverage and can have an effect on the food security in regions that depend on subsistence farming. In this study we investigate how soil moisture, mean annual precipitation, and day length control savannah phenology by developing a lagged time series model. The model uses...... climate data for 15 flux tower sites across four continents, and normalized difference vegetation index from satellite to optimize a statistical phenological model. We show that all three variables can be used to estimate savannah phenology on a global scale. However, it was not possible to create...... a simplified savannah model that works equally well for all sites on the global scale without inclusion of more site specific parameters. The simplified model showed no bias towards tree cover or between continents and resulted in a cross-validated r2 of 0.6 and root mean squared error of 0.1. We therefore...
The partial duration series method in regional index-flood modeling
DEFF Research Database (Denmark)
Madsen, Henrik; Rosbjerg, Dan
1997-01-01
A regional index-flood method based on the partial duration series model is introduced. The model comprises the assumptions of a Poisson-distributed number of threshold exceedances and generalized Pareto (GP) distributed peak magnitudes. The regional T-year event estimator is based on a regional...... estimator is superior to the at-site estimator even in extremely heterogenous regions, the performance of the regional estimator being relatively better in regions with a negative shape parameter. When the record length increases, the relative performance of the regional estimator decreases, but it is still...
DEFF Research Database (Denmark)
Madsen, Henrik; Rosbjerg, Dan
1997-01-01
parameters is inferred from regional data using generalized least squares (GLS) regression. Two different Bayesian T-year event estimators are introduced: a linear estimator that requires only some moments of the prior distributions to be specified and a parametric estimator that is based on specified......A regional estimation procedure that combines the index-flood concept with an empirical Bayes method for inferring regional information is introduced. The model is based on the partial duration series approach with generalized Pareto (GP) distributed exceedances. The prior information of the model...
Sensor response monitoring in pressurized water reactors using time series modeling
International Nuclear Information System (INIS)
Upadhyaya, B.R.; Kerlin, T.W.
1978-01-01
Random data analysis in nuclear power reactors for purposes of process surveillance, pattern recognition and monitoring of temperature, pressure, flow and neutron sensors has gained increasing attention in view of their potential for helping to ensure safe plant operation. In this study, application of autoregressive moving-average (ARMA) time series modeling for monitoring temperature sensor response characteristrics is presented. The ARMA model is used to estimate the step and ramp response of the sensors and the related time constant and ramp delay time. The ARMA parameters are estimated by a two-stage algorithm in the spectral domain. Results of sensor testing for an operating pressurized water reactor are presented. 16 refs
Nguyen, Hien D; Ullmann, Jeremy F P; McLachlan, Geoffrey J; Voleti, Venkatakaushik; Li, Wenze; Hillman, Elizabeth M C; Reutens, David C; Janke, Andrew L
2018-02-01
Calcium is a ubiquitous messenger in neural signaling events. An increasing number of techniques are enabling visualization of neurological activity in animal models via luminescent proteins that bind to calcium ions. These techniques generate large volumes of spatially correlated time series. A model-based functional data analysis methodology via Gaussian mixtures is suggested for the clustering of data from such visualizations is proposed. The methodology is theoretically justified and a computationally efficient approach to estimation is suggested. An example analysis of a zebrafish imaging experiment is presented.
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.
Directory of Open Access Journals (Sweden)
Jia Chaolong
2013-01-01
Full Text Available Good track geometry state ensures the safe operation of the railway passenger service and freight service. Railway transportation plays an important role in the Chinese economic and social development. This paper studies track irregularity standard deviation time series data and focuses on the characteristics and trend changes of track state by applying clustering analysis. Linear recursive model and linear-ARMA model based on wavelet decomposition reconstruction are proposed, and all they offer supports for the safe management of railway transportation.
A dynamic model to explain hydration behaviour along the lanthanide series
International Nuclear Information System (INIS)
Duvail, M.; Spezia, R.; Vitorge, P.
2008-01-01
An understanding of the hydration structure of heavy atoms, such as transition metals, lanthanides and actinides, in aqueous solution is of fundamental importance in order to address their solvation properties and chemical reactivity. Herein we present a systematic molecular dynamics study of Ln 3+ hydration in bulk water that can be used as reference for experimental and theoretical research in this and related fields. Our study of hydration structure and dynamics along the entire Ln 3+ series provides a dynamic picture of the CN behavioural change from light (CN=9 predominating) to heavy (CN=8 predominating) lanthanides consistent with the exchange mechanism proposed by Helm, Merbach and co-workers. This scenario is summarized in this work. The hydrated light lanthanides are stable TTP structures containing two kinds of water molecules: six molecules forming the trigonal prism and three in the centre triangle. Towards the middle of the series both ionic radii and polarizabilities decrease, such that first-shell water-water repulsion increases and water-cation attraction decreases. This mainly applies for molecules of the centre triangle of the nine-fold structure. Thus, one of these molecules stay in the second hydration sphere of the lanthanide for longer average times, as one progresses along the lanthanide series. The interchange between predominantly CN=9 and CN=8 is found between Tb and Dy. Therefore, we propose a model that determines the properties governing the change in the first-shell coordination number across the series, confirming the basic hypothesis proposed by Helm and Merbach. We show that it is not a sudden change in behaviour, but rather that it results from a statistical predominance of one first hydration shell structure containing nine water molecules over one containing eight. This is observed progressively across the series. (O.M.)
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)
Bernaola-Galván, Pedro A.; Gómez-Extremera, Manuel; Romance, A. Ramón; Carpena, Pedro
2017-09-01
The correlation properties of the magnitude of a time series are associated with nonlinear and multifractal properties and have been applied in a great variety of fields. Here we have obtained the analytical expression of the autocorrelation of the magnitude series (C|x |) of a linear Gaussian noise as a function of its autocorrelation (Cx). For both, models and natural signals, the deviation of C|x | from its expectation in linear Gaussian noises can be used as an index of nonlinearity that can be applied to relatively short records and does not require the presence of scaling in the time series under study. In a model of artificial Gaussian multifractal signal we use this approach to analyze the relation between nonlinearity and multifractallity and show that the former implies the latter but the reverse is not true. We also apply this approach to analyze experimental data: heart-beat records during rest and moderate exercise. For each individual subject, we observe higher nonlinearities during rest. This behavior is also achieved on average for the analyzed set of 10 semiprofessional soccer players. This result agrees with the fact that other measures of complexity are dramatically reduced during exercise and can shed light on its relationship with the withdrawal of parasympathetic tone and/or the activation of sympathetic activity during physical activity.
2010-08-05
... Airworthiness Directives; McDonnell Douglas Corporation Model DC- 9-10 Series Airplanes, DC-9-30 Series... existing airworthiness directive (AD), which applies to all McDonnell Douglas Model DC-9-10 series..., 2010). That AD applies to all McDonnell Douglas Corporation Model DC-9-10 series airplanes, DC-9-30...
Modeling associations between latent event processes governing time series of pulsing hormones.
Liu, Huayu; Carlson, Nichole E; Grunwald, Gary K; Polotsky, Alex J
2017-10-31
This work is motivated by a desire to quantify relationships between two time series of pulsing hormone concentrations. The locations of pulses are not directly observed and may be considered latent event processes. The latent event processes of pulsing hormones are often associated. It is this joint relationship we model. Current approaches to jointly modeling pulsing hormone data generally assume that a pulse in one hormone is coupled with a pulse in another hormone (one-to-one association). However, pulse coupling is often imperfect. Existing joint models are not flexible enough for imperfect systems. In this article, we develop a more flexible class of pulse association models that incorporate parameters quantifying imperfect pulse associations. We propose a novel use of the Cox process model as a model of how pulse events co-occur in time. We embed the Cox process model into a hormone concentration model. Hormone concentration is the observed data. Spatial birth and death Markov chain Monte Carlo is used for estimation. Simulations show the joint model works well for quantifying both perfect and imperfect associations and offers estimation improvements over single hormone analyses. We apply this model to luteinizing hormone (LH) and follicle stimulating hormone (FSH), two reproductive hormones. Use of our joint model results in an ability to investigate novel hypotheses regarding associations between LH and FSH secretion in obese and non-obese women. © 2017, The International Biometric Society.
Time series segmentation: a new approach based on Genetic Algorithm and Hidden Markov Model
Toreti, A.; Kuglitsch, F. G.; Xoplaki, E.; Luterbacher, J.
2009-04-01
The subdivision of a time series into homogeneous segments has been performed using various methods applied to different disciplines. In climatology, for example, it is accompanied by the well-known homogenization problem and the detection of artificial change points. In this context, we present a new method (GAMM) based on Hidden Markov Model (HMM) and Genetic Algorithm (GA), applicable to series of independent observations (and easily adaptable to autoregressive processes). A left-to-right hidden Markov model, estimating the parameters and the best-state sequence, respectively, with the Baum-Welch and Viterbi algorithms, was applied. In order to avoid the well-known dependence of the Baum-Welch algorithm on the initial condition, a Genetic Algorithm was developed. This algorithm is characterized by mutation, elitism and a crossover procedure implemented with some restrictive rules. Moreover the function to be minimized was derived following the approach of Kehagias (2004), i.e. it is the so-called complete log-likelihood. The number of states was determined applying a two-fold cross-validation procedure (Celeux and Durand, 2008). Being aware that the last issue is complex, and it influences all the analysis, a Multi Response Permutation Procedure (MRPP; Mielke et al., 1981) was inserted. It tests the model with K+1 states (where K is the state number of the best model) if its likelihood is close to K-state model. Finally, an evaluation of the GAMM performances, applied as a break detection method in the field of climate time series homogenization, is shown. 1. G. Celeux and J.B. Durand, Comput Stat 2008. 2. A. Kehagias, Stoch Envir Res 2004. 3. P.W. Mielke, K.J. Berry, G.W. Brier, Monthly Wea Rev 1981.
Patient specific dynamic geometric models from sequential volumetric time series image data.
Cameron, B M; Robb, R A
2004-01-01
Generating patient specific dynamic models is complicated by the complexity of the motion intrinsic and extrinsic to the anatomic structures being modeled. Using a physics-based sequentially deforming algorithm, an anatomically accurate dynamic four-dimensional model can be created from a sequence of 3-D volumetric time series data sets. While such algorithms may accurately track the cyclic non-linear motion of the heart, they generally fail to accurately track extrinsic structural and non-cyclic motion. To accurately model these motions, we have modified a physics-based deformation algorithm to use a meta-surface defining the temporal and spatial maxima of the anatomic structure as the base reference surface. A mass-spring physics-based deformable model, which can expand or shrink with the local intrinsic motion, is applied to the metasurface, deforming this base reference surface to the volumetric data at each time point. As the meta-surface encompasses the temporal maxima of the structure, any extrinsic motion is inherently encoded into the base reference surface and allows the computation of the time point surfaces to be performed in parallel. The resultant 4-D model can be interactively transformed and viewed from different angles, showing the spatial and temporal motion of the anatomic structure. Using texture maps and per-vertex coloring, additional data such as physiological and/or biomechanical variables (e.g., mapping electrical activation sequences onto contracting myocardial surfaces) can be associated with the dynamic model, producing a 5-D model. For acquisition systems that may capture only limited time series data (e.g., only images at end-diastole/end-systole or inhalation/exhalation), this algorithm can provide useful interpolated surfaces between the time points. Such models help minimize the number of time points required to usefully depict the motion of anatomic structures for quantitative assessment of regional dynamics.
Cheng, C. M.; Peng, Z. K.; Zhang, W. M.; Meng, G.
2017-03-01
Nonlinear problems have drawn great interest and extensive attention from engineers, physicists and mathematicians and many other scientists because most real systems are inherently nonlinear in nature. To model and analyze nonlinear systems, many mathematical theories and methods have been developed, including Volterra series. In this paper, the basic definition of the Volterra series is recapitulated, together with some frequency domain concepts which are derived from the Volterra series, including the general frequency response function (GFRF), the nonlinear output frequency response function (NOFRF), output frequency response function (OFRF) and associated frequency response function (AFRF). The relationship between the Volterra series and other nonlinear system models and nonlinear problem solving methods are discussed, including the Taylor series, Wiener series, NARMAX model, Hammerstein model, Wiener model, Wiener-Hammerstein model, harmonic balance method, perturbation method and Adomian decomposition. The challenging problems and their state of arts in the series convergence study and the kernel identification study are comprehensively introduced. In addition, a detailed review is then given on the applications of Volterra series in mechanical engineering, aeroelasticity problem, control engineering, electronic and electrical engineering.
Analysis of hohlraum energetics of the SG series and the NIF experiments with energy balance model
Directory of Open Access Journals (Sweden)
Guoli Ren
2017-01-01
Full Text Available The basic energy balance model is applied to analyze the hohlraum energetics data from the Shenguang (SG series laser facilities and the National Ignition Facility (NIF experiments published in the past few years. The analysis shows that the overall hohlraum energetics data are in agreement with the energy balance model within 20% deviation. The 20% deviation might be caused by the diversity in hohlraum parameters, such as material, laser pulse, gas filling density, etc. In addition, the NIF's ignition target designs and our ignition target designs given by simulations are also in accordance with the energy balance model. This work confirms the value of the energy balance model for ignition target design and experimental data assessment, and demonstrates that the NIF energy is enough to achieve ignition if a 1D spherical radiation drive could be created, meanwhile both the laser plasma instabilities and hydrodynamic instabilities could be suppressed.
Scargle, Jeffrey D.
1990-01-01
While chaos arises only in nonlinear systems, standard linear time series models are nevertheless useful for analyzing data from chaotic processes. This paper introduces such a model, the chaotic moving average. This time-domain model is based on the theorem that any chaotic process can be represented as the convolution of a linear filter with an uncorrelated process called the chaotic innovation. A technique, minimum phase-volume deconvolution, is introduced to estimate the filter and innovation. The algorithm measures the quality of a model using the volume covered by the phase-portrait of the innovation process. Experiments on synthetic data demonstrate that the algorithm accurately recovers the parameters of simple chaotic processes. Though tailored for chaos, the algorithm can detect both chaos and randomness, distinguish them from each other, and separate them if both are present. It can also recover nonminimum-delay pulse shapes in non-Gaussian processes, both random and chaotic.
Studies in astronomical time series analysis: Modeling random processes in the time domain
Scargle, J. D.
1979-01-01
Random process models phased in the time domain are used to analyze astrophysical time series data produced by random processes. A moving average (MA) model represents the data as a sequence of pulses occurring randomly in time, with random amplitudes. An autoregressive (AR) model represents the correlations in the process in terms of a linear function of past values. The best AR model is determined from sampled data and transformed to an MA for interpretation. The randomness of the pulse amplitudes is maximized by a FORTRAN algorithm which is relatively stable numerically. Results of test cases are given to study the effects of adding noise and of different distributions for the pulse amplitudes. A preliminary analysis of the optical light curve of the quasar 3C 273 is given.
Weber, Juliane; Zachow, Christopher; Witthaut, Dirk
2018-03-01
Wind power generation exhibits a strong temporal variability, which is crucial for system integration in highly renewable power systems. Different methods exist to simulate wind power generation but they often cannot represent the crucial temporal fluctuations properly. We apply the concept of additive binary Markov chains to model a wind generation time series consisting of two states: periods of high and low wind generation. The only input parameter for this model is the empirical autocorrelation function. The two-state model is readily extended to stochastically reproduce the actual generation per period. To evaluate the additive binary Markov chain method, we introduce a coarse model of the electric power system to derive backup and storage needs. We find that the temporal correlations of wind power generation, the backup need as a function of the storage capacity, and the resting time distribution of high and low wind events for different shares of wind generation can be reconstructed.
Weber, Juliane; Zachow, Christopher; Witthaut, Dirk
2018-03-01
Wind power generation exhibits a strong temporal variability, which is crucial for system integration in highly renewable power systems. Different methods exist to simulate wind power generation but they often cannot represent the crucial temporal fluctuations properly. We apply the concept of additive binary Markov chains to model a wind generation time series consisting of two states: periods of high and low wind generation. The only input parameter for this model is the empirical autocorrelation function. The two-state model is readily extended to stochastically reproduce the actual generation per period. To evaluate the additive binary Markov chain method, we introduce a coarse model of the electric power system to derive backup and storage needs. We find that the temporal correlations of wind power generation, the backup need as a function of the storage capacity, and the resting time distribution of high and low wind events for different shares of wind generation can be reconstructed.
Febrian Umbara, Rian; Tarwidi, Dede; Budi Setiawan, Erwin
2018-03-01
The paper discusses the prediction of Jakarta Composite Index (JCI) in Indonesia Stock Exchange. The study is based on JCI historical data for 1286 days to predict the value of JCI one day ahead. This paper proposes predictions done in two stages., The first stage using Fuzzy Time Series (FTS) to predict values of ten technical indicators, and the second stage using Support Vector Regression (SVR) to predict the value of JCI one day ahead, resulting in a hybrid prediction model FTS-SVR. The performance of this combined prediction model is compared with the performance of the single stage prediction model using SVR only. Ten technical indicators are used as input for each model.
Forecast models for suicide: Time-series analysis with data from Italy.
Preti, Antonio; Lentini, Gianluca
2016-01-01
The prediction of suicidal behavior is a complex task. To fine-tune targeted preventative interventions, predictive analytics (i.e. forecasting future risk of suicide) is more important than exploratory data analysis (pattern recognition, e.g. detection of seasonality in suicide time series). This study sets out to investigate the accuracy of forecasting models of suicide for men and women. A total of 101 499 male suicides and of 39 681 female suicides - occurred in Italy from 1969 to 2003 - were investigated. In order to apply the forecasting model and test its accuracy, the time series were split into a training set (1969 to 1996; 336 months) and a test set (1997 to 2003; 84 months). The main outcome was the accuracy of forecasting models on the monthly number of suicides. These measures of accuracy were used: mean absolute error; root mean squared error; mean absolute percentage error; mean absolute scaled error. In both male and female suicides a change in the trend pattern was observed, with an increase from 1969 onwards to reach a maximum around 1990 and decrease thereafter. The variances attributable to the seasonal and trend components were, respectively, 24% and 64% in male suicides, and 28% and 41% in female ones. Both annual and seasonal historical trends of monthly data contributed to forecast future trends of suicide with a margin of error around 10%. The finding is clearer in male than in female time series of suicide. The main conclusion of the study is that models taking seasonality into account seem to be able to derive information on deviation from the mean when this occurs as a zenith, but they fail to reproduce it when it occurs as a nadir. Preventative efforts should concentrate on the factors that influence the occurrence of increases above the main trend in both seasonal and cyclic patterns of suicides.
Directory of Open Access Journals (Sweden)
Kansuporn eSriyudthsak
2016-05-01
Full Text Available 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.
Hensman, James; Lawrence, Neil D; Rattray, Magnus
2013-08-20
Time course data from microarrays and high-throughput sequencing experiments require simple, computationally efficient and powerful statistical models to extract meaningful biological signal, and for tasks such as data fusion and clustering. Existing methodologies fail to capture either the temporal or replicated nature of the experiments, and often impose constraints on the data collection process, such as regularly spaced samples, or similar sampling schema across replications. We propose hierarchical Gaussian processes as a general model of gene expression time-series, with application to a variety of problems. In particular, we illustrate the method's capacity for missing data imputation, data fusion and clustering.The method can impute data which is missing both systematically and at random: in a hold-out test on real data, performance is significantly better than commonly used imputation methods. The method's ability to model inter- and intra-cluster variance leads to more biologically meaningful clusters. The approach removes the necessity for evenly spaced samples, an advantage illustrated on a developmental Drosophila dataset with irregular replications. The hierarchical Gaussian process model provides an excellent statistical basis for several gene-expression time-series tasks. It has only a few additional parameters over a regular GP, has negligible additional complexity, is easily implemented and can be integrated into several existing algorithms. Our experiments were implemented in python, and are available from the authors' website: http://staffwww.dcs.shef.ac.uk/people/J.Hensman/.
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)
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.
Fractality of profit landscapes and validation of time series models for stock prices
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.
Bakker, Mark
2010-08-01
A new analytic solution approach is presented for the modeling of steady flow to pumping wells near rivers in strip aquifers; all boundaries of the river and strip aquifer may be curved. The river penetrates the aquifer only partially and has a leaky stream bed. The water level in the river may vary spatially. Flow in the aquifer below the river is semi-confined while flow in the aquifer adjacent to the river is confined or unconfined and may be subject to areal recharge. Analytic solutions are obtained through superposition of analytic elements and Fourier series. Boundary conditions are specified at collocation points along the boundaries. The number of collocation points is larger than the number of coefficients in the Fourier series and a solution is obtained in the least squares sense. The solution is analytic while boundary conditions are met approximately. Very accurate solutions are obtained when enough terms are used in the series. Several examples are presented for domains with straight and curved boundaries, including a well pumping near a meandering river with a varying water level. The area of the river bottom where water infiltrates into the aquifer is delineated and the fraction of river water in the well water is computed for several cases.
Loos, Martin; Krauss, Martin; Fenner, Kathrin
2012-09-18
Formation of soil nonextractable residues (NER) is central to the fate and persistence of pesticides. To investigate pools and extent of NER formation, an established inverse modeling approach for pesticide soil degradation time series was evaluated with a Monte Carlo Markov Chain (MCMC) sampling procedure. It was found that only half of 73 pesticide degradation time series from a homogeneous soil source allowed for well-behaved identification of kinetic parameters with a four-pool model containing a parent compound, a metabolite, a volatile, and a NER pool. A subsequent simulation indeed confirmed distinct parameter combinations of low identifiability. Taking the resulting uncertainties into account, several conclusions regarding NER formation and its impact on persistence assessment could nonetheless be drawn. First, rate constants for transformation of parent compounds to metabolites were correlated to those for transformation of parent compounds to NER, leading to degradation half-lives (DegT50) typically not being larger than disappearance half-lives (DT50) by more than a factor of 2. Second, estimated rate constants were used to evaluate NER formation over time. This showed that NER formation, particularly through the metabolite pool, may be grossly underestimated when using standard incubation periods. It further showed that amounts and uncertainties in (i) total NER, (ii) NER formed from the parent pool, and (iii) NER formed from the metabolite pool vary considerably among data sets at t→∞, with no clear dominance between (ii) and (iii). However, compounds containing aromatic amine moieties were found to form significantly more total NER when extrapolating to t→∞ than the other compounds studied. Overall, our study stresses the general need for assessing uncertainties, identifiability issues, and resulting biases when using inverse modeling of degradation time series for evaluating persistence and NER formation.
Non-linear auto-regressive models for cross-frequency coupling in neural time series
Tallot, Lucille; Grabot, Laetitia; Doyère, Valérie; Grenier, Yves; Gramfort, Alexandre
2017-01-01
We address the issue of reliably detecting and quantifying cross-frequency coupling (CFC) in neural time series. Based on non-linear auto-regressive models, the proposed method provides a generative and parametric model of the time-varying spectral content of the signals. As this method models the entire spectrum simultaneously, it avoids the pitfalls related to incorrect filtering or the use of the Hilbert transform on wide-band signals. As the model is probabilistic, it also provides a score of the model “goodness of fit” via the likelihood, enabling easy and legitimate model selection and parameter comparison; this data-driven feature is unique to our model-based approach. Using three datasets obtained with invasive neurophysiological recordings in humans and rodents, we demonstrate that these models are able to replicate previous results obtained with other metrics, but also reveal new insights such as the influence of the amplitude of the slow oscillation. Using simulations, we demonstrate that our parametric method can reveal neural couplings with shorter signals than non-parametric methods. We also show how the likelihood can be used to find optimal filtering parameters, suggesting new properties on the spectrum of the driving signal, but also to estimate the optimal delay between the coupled signals, enabling a directionality estimation in the coupling. PMID:29227989
Xing, X.; Yuan, Z.; Chen, L. F.; Yu, X. Y.; Xiao, L.
2018-04-01
The stability control is one of the major technical difficulties in the field of highway subgrade construction engineering. Building deformation model is a crucial step for InSAR time series deformation monitoring. Most of the InSAR deformation models for deformation monitoring are pure empirical mathematical models, without considering the physical mechanism of the monitored object. In this study, we take rheology into consideration, inducing rheological parameters into traditional InSAR deformation models. To assess the feasibility and accuracy for our new model, both simulation and real deformation data over Lungui highway (a typical highway built on soft clay subgrade in Guangdong province, China) are investigated with TerraSAR-X satellite imagery. In order to solve the unknows of the non-linear rheological model, three algorithms: Gauss-Newton (GN), Levenberg-Marquarat (LM), and Genetic Algorithm (GA), are utilized and compared to estimate the unknown parameters. Considering both the calculation efficiency and accuracy, GA is chosen as the final choice for the new model in our case study. Preliminary real data experiment is conducted with use of 17 TerraSAR-X Stripmap images (with a 3-m resolution). With the new deformation model and GA aforementioned, the unknown rheological parameters over all the high coherence points are obtained and the LOS deformation (the low-pass component) sequences are generated.
Time series modeling of soil moisture dynamics on a steep mountainous hillside
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.
Multidimensional k-nearest neighbor model based on EEMD for financial time series forecasting
Zhang, Ningning; Lin, Aijing; Shang, Pengjian
2017-07-01
In this paper, we propose a new two-stage methodology that combines the ensemble empirical mode decomposition (EEMD) with multidimensional k-nearest neighbor model (MKNN) in order to forecast the closing price and high price of the stocks simultaneously. The modified algorithm of k-nearest neighbors (KNN) has an increasingly wide application in the prediction of all fields. Empirical mode decomposition (EMD) decomposes a nonlinear and non-stationary signal into a series of intrinsic mode functions (IMFs), however, it cannot reveal characteristic information of the signal with much accuracy as a result of mode mixing. So ensemble empirical mode decomposition (EEMD), an improved method of EMD, is presented to resolve the weaknesses of EMD by adding white noise to the original data. With EEMD, the components with true physical meaning can be extracted from the time series. Utilizing the advantage of EEMD and MKNN, the new proposed ensemble empirical mode decomposition combined with multidimensional k-nearest neighbor model (EEMD-MKNN) has high predictive precision for short-term forecasting. Moreover, we extend this methodology to the case of two-dimensions to forecast the closing price and high price of the four stocks (NAS, S&P500, DJI and STI stock indices) at the same time. The results indicate that the proposed EEMD-MKNN model has a higher forecast precision than EMD-KNN, KNN method and ARIMA.
Time Series Modeling of Human Operator Dynamics in Manual Control Tasks
Biezad, D. J.; Schmidt, D. K.
1984-01-01
A time-series technique is presented for identifying the dynamic characteristics of the human operator in manual control tasks from relatively short records of experimental data. Control of system excitation signals used in the identification is not required. The approach is a multi-channel identification technique for modeling multi-input/multi-output situations. The method presented includes statistical tests for validity, is designed for digital computation, and yields estimates for the frequency response of the human operator. A comprehensive relative power analysis may also be performed for validated models. This method is applied to several sets of experimental data; the results are discussed and shown to compare favorably with previous research findings. New results are also presented for a multi-input task that was previously modeled to demonstrate the strengths of the method.
Applications and Comparisons of Four Time Series Models in Epidemiological Surveillance Data
Young, Alistair A.; Li, Xiaosong
2014-01-01
Public health surveillance systems provide valuable data for reliable predication of future epidemic events. This paper describes a study that used nine types of infectious disease data collected through a national public health surveillance system in mainland China to evaluate and compare the performances of four time series methods, namely, two decomposition methods (regression and exponential smoothing), autoregressive integrated moving average (ARIMA) and support vector machine (SVM). The data obtained from 2005 to 2011 and in 2012 were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The accuracy of the statistical models in forecasting future epidemic disease proved their effectiveness in epidemiological surveillance. Although the comparisons found that no single method is completely superior to the others, the present study indeed highlighted that the SVMs outperforms the ARIMA model and decomposition methods in most cases. PMID:24505382
Mining Gene Regulatory Networks by Neural Modeling of Expression Time-Series.
Rubiolo, Mariano; Milone, Diego H; Stegmayer, Georgina
2015-01-01
Discovering gene regulatory networks from data is one of the most studied topics in recent years. Neural networks can be successfully used to infer an underlying gene network by modeling expression profiles as times series. This work proposes a novel method based on a pool of neural networks for obtaining a gene regulatory network from a gene expression dataset. They are used for modeling each possible interaction between pairs of genes in the dataset, and a set of mining rules is applied to accurately detect the subjacent relations among genes. The results obtained on artificial and real datasets confirm the method effectiveness for discovering regulatory networks from a proper modeling of the temporal dynamics of gene expression profiles.
Agarwal, G. C.; Osafo-Charles, F.; Oneill, W. D.; Gottlieb, G. L.
1982-01-01
Time series analysis is applied to model human operator dynamics in pursuit and compensatory tracking modes. The normalized residual criterion is used as a one-step analytical tool to encompass the processes of identification, estimation, and diagnostic checking. A parameter constraining technique is introduced to develop more reliable models of human operator dynamics. The human operator is adequately modeled by a second order dynamic system both in pursuit and compensatory tracking modes. In comparing the data sampling rates, 100 msec between samples is adequate and is shown to provide better results than 200 msec sampling. The residual power spectrum and eigenvalue analysis show that the human operator is not a generator of periodic characteristics.
Development of New Loan Payment Models with Piecewise Geometric Gradient Series
Directory of Open Access Journals (Sweden)
Erdal Aydemir
2014-12-01
Full Text Available Engineering economics plays an important role in decision making. Also, the cash flows, time value of money and interest rates are the most important research fields in mathematical finance. Generalized formulae obtained from a variety of models with the time value of money and cash flows are inadequate to solve some problems. In this study, a new generalized formulae is considered for the first time and derived from a loan payment model which is a certain number of payment amount determined by customer at the beginning of payment period and the other repayments with piecewise linear gradient series. As a result, some numerical examples with solutions are given for the developed models.
Model for the heart beat-to-beat time series during meditation
Capurro, A.; Diambra, L.; Malta, C. P.
2003-09-01
We present a model for the respiratory modulation of the heart beat-to-beat interval series. The model consists of a pacemaker, that simulates the membrane potential of the sinoatrial node, modulated by a periodic input signal plus correlated noise that simulates the respiratory input. The model was used to assess the waveshape of the respiratory signals needed to reproduce in the phase space the trajectory of experimental heart beat-to-beat interval data. The data sets were recorded during meditation practices of the Chi and Kundalini Yoga techniques. Our study indicates that in the first case the respiratory signal has the shape of a smoothed square wave, and in the second case it has the shape of a smoothed triangular wave.
Kwasniok, Frank
2013-11-01
A time series analysis method for predicting the probability density of a dynamical system is proposed. A nonstationary parametric model of the probability density is estimated from data within a maximum likelihood framework and then extrapolated to forecast the future probability density and explore the system for critical transitions or tipping points. A full systematic account of parameter uncertainty is taken. The technique is generic, independent of the underlying dynamics of the system. The method is verified on simulated data and then applied to prediction of Arctic sea-ice extent.
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.
A Time Series Model for Assessing the Trend and Forecasting the Road Traffic Accident Mortality.
Yousefzadeh-Chabok, Shahrokh; Ranjbar-Taklimie, Fatemeh; Malekpouri, Reza; Razzaghi, Alireza
2016-09-01
Road traffic accident (RTA) is one of the main causes of trauma and known as a growing public health concern worldwide, especially in developing countries. Assessing the trend of fatalities in the past years and forecasting it enables us to make the appropriate planning for prevention and control. This study aimed to assess the trend of RTAs and forecast it in the next years by using time series modeling. In this historical analytical study, the RTA mortalities in Zanjan Province, Iran, were evaluated during 2007 - 2013. The time series analyses including Box-Jenkins models were used to assess the trend of accident fatalities in previous years and forecast it for the next 4 years. The mean age of the victims was 37.22 years (SD = 20.01). From a total of 2571 deaths, 77.5% (n = 1992) were males and 22.5% (n = 579) were females. The study models showed a descending trend of fatalities in the study years. The SARIMA (1, 1, 3) (0, 1, 0) 12 model was recognized as a best fit model in forecasting the trend of fatalities. Forecasting model also showed a descending trend of traffic accident mortalities in the next 4 years. There was a decreasing trend in the study and the future years. It seems that implementation of some interventions in the recent decade has had a positive effect on the decline of RTA fatalities. Nevertheless, there is still a need to pay more attention in order to prevent the occurrence and the mortalities related to traffic accidents.
A Time Series Model for Assessing the Trend and Forecasting the Road Traffic Accident Mortality
Yousefzadeh-Chabok, Shahrokh; Ranjbar-Taklimie, Fatemeh; Malekpouri, Reza; Razzaghi, Alireza
2016-01-01
Background Road traffic accident (RTA) is one of the main causes of trauma and known as a growing public health concern worldwide, especially in developing countries. Assessing the trend of fatalities in the past years and forecasting it enables us to make the appropriate planning for prevention and control. Objectives This study aimed to assess the trend of RTAs and forecast it in the next years by using time series modeling. Materials and Methods In this historical analytical study, the RTA mortalities in Zanjan Province, Iran, were evaluated during 2007 - 2013. The time series analyses including Box-Jenkins models were used to assess the trend of accident fatalities in previous years and forecast it for the next 4 years. Results The mean age of the victims was 37.22 years (SD = 20.01). From a total of 2571 deaths, 77.5% (n = 1992) were males and 22.5% (n = 579) were females. The study models showed a descending trend of fatalities in the study years. The SARIMA (1, 1, 3) (0, 1, 0) 12 model was recognized as a best fit model in forecasting the trend of fatalities. Forecasting model also showed a descending trend of traffic accident mortalities in the next 4 years. Conclusions There was a decreasing trend in the study and the future years. It seems that implementation of some interventions in the recent decade has had a positive effect on the decline of RTA fatalities. Nevertheless, there is still a need to pay more attention in order to prevent the occurrence and the mortalities related to traffic accidents. PMID:27800467
Modeling commodity salam contract between two parties for discrete and continuous time series
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.
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.
Time series modelling of global mean temperature for managerial decision-making.
Romilly, Peter
2005-07-01
Climate change has important implications for business and economic activity. Effective management of climate change impacts will depend on the availability of accurate and cost-effective forecasts. This paper uses univariate time series techniques to model the properties of a global mean temperature dataset in order to develop a parsimonious forecasting model for managerial decision-making over the short-term horizon. Although the model is estimated on global temperature data, the methodology could also be applied to temperature data at more localised levels. The statistical techniques include seasonal and non-seasonal unit root testing with and without structural breaks, as well as ARIMA and GARCH modelling. A forecasting evaluation shows that the chosen model performs well against rival models. The estimation results confirm the findings of a number of previous studies, namely that global mean temperatures increased significantly throughout the 20th century. The use of GARCH modelling also shows the presence of volatility clustering in the temperature data, and a positive association between volatility and global mean temperature.
Modeling time-series count data: the unique challenges facing political communication studies.
Fogarty, Brian J; Monogan, James E
2014-05-01
This paper demonstrates the importance of proper model specification when analyzing time-series count data in political communication studies. It is common for scholars of media and politics to investigate counts of coverage of an issue as it evolves over time. Many scholars rightly consider the issues of time dependence and dynamic causality to be the most important when crafting a model. However, to ignore the count features of the outcome variable overlooks an important feature of the data. This is particularly the case when modeling data with a low number of counts. In this paper, we argue that the Poisson autoregressive model (Brandt and Williams, 2001) accurately meets the needs of many media studies. We replicate the analyses of Flemming et al. (1997), Peake and Eshbaugh-Soha (2008), and Ura (2009) and demonstrate that models missing some of the assumptions of the Poisson autoregressive model often yield invalid inferences. We also demonstrate that the effect of any of these models can be illustrated dynamically with estimates of uncertainty through a simulation procedure. The paper concludes with implications of these findings for the practical researcher. Copyright © 2013 Elsevier Inc. All rights reserved.
Porta, Alberto; Bari, Vlasta; Ranuzzi, Giovanni; De Maria, Beatrice; Baselli, Giuseppe
2017-09-01
We propose a multiscale complexity (MSC) method assessing irregularity in assigned frequency bands and being appropriate for analyzing the short time series. It is grounded on the identification of the coefficients of an autoregressive model, on the computation of the mean position of the poles generating the components of the power spectral density in an assigned frequency band, and on the assessment of its distance from the unit circle in the complex plane. The MSC method was tested on simulations and applied to the short heart period (HP) variability series recorded during graded head-up tilt in 17 subjects (age from 21 to 54 years, median = 28 years, 7 females) and during paced breathing protocols in 19 subjects (age from 27 to 35 years, median = 31 years, 11 females) to assess the contribution of time scales typical of the cardiac autonomic control, namely in low frequency (LF, from 0.04 to 0.15 Hz) and high frequency (HF, from 0.15 to 0.5 Hz) bands to the complexity of the cardiac regulation. The proposed MSC technique was compared to a traditional model-free multiscale method grounded on information theory, i.e., multiscale entropy (MSE). The approach suggests that the reduction of HP variability complexity observed during graded head-up tilt is due to a regularization of the HP fluctuations in LF band via a possible intervention of sympathetic control and the decrement of HP variability complexity observed during slow breathing is the result of the regularization of the HP variations in both LF and HF bands, thus implying the action of physiological mechanisms working at time scales even different from that of respiration. MSE did not distinguish experimental conditions at time scales larger than 1. Over a short time series MSC allows a more insightful association between cardiac control complexity and physiological mechanisms modulating cardiac rhythm compared to a more traditional tool such as MSE.
Engineering report : technical review of the GPSI model G3000 series multi-sensor controller system
Energy Technology Data Exchange (ETDEWEB)
Gibson, S.; Campbell, I. [Gas Protection Systems Inc., Maple Ridge, BC (Canada)
2001-12-01
The Enviro Sentry 24/7 model G3002 remote computer was developed by Gas Production Services Inc. (GPSI). This low cost Universal Detector/Controller consists of a stand-alone, scalable network connected to a daisy-chain topology. M. Collyer reviewed the capabilities of the GPSI model G3000 series multi-sensor controller system from an engineering perspective and presented an independent opinion on its performance and operation. Its evaluation was based on a randomly selected production unit supplied by GPSI. M. Collyer used widely used industry principles, electronic laboratory testing methods, prepared schematic diagrams, and presented operator ratings. The strength of the system's viability in the global market is that its use is not constrained by regulations or standards of any country, province, state or region. It can be used to provide solutions for air quality, protection and energy management. In particular, the G3000 series provides continuous protection and intelligent management in combustible gas detection; seismic risk mitigation; toxic gas early detection; air quality management; and, energy conservation. 1 tab.
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%.
Stochastic modeling for time series InSAR: with emphasis on atmospheric effects
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.
Directory of Open Access Journals (Sweden)
António Manuel Martins de Almeida
2016-06-01
Full Text Available Tourism is the leading economic sector in most islands and for that reason market trends are closely monitored due to the huge impacts of relatively minor changes in the demand patterns. An interesting line of research regarding the analysis of market trends concerns the examination of time series to get an historical overview of the data patterns. The modelling of demand patterns is obviously dependent on data availability, and the measurement of changes in demand patterns is quite often focused on a few decades. In this paper, we use long-term time-series data to analyse the evolution of the main markets in Madeira, by country of origin, in order to re-examine the Butler life cycle model, based on data available from 1946 onwards. This study is an opportunity to document the historical development of the industry in Madeira and to introduce the discussion about the rejuvenation of a mature destination. Tourism development in Madeira has experienced rapid growth until the late 90s, as one of the leading destinations in the European context. However, annual growth rates are not within acceptable ranges, which lead policy-makers and experts to recommend a thoughtfully assessment of the industry prospects.
A Virtual Machine Migration Strategy Based on Time Series Workload Prediction Using Cloud Model
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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.
Bodmer, James E; English, Anthony; Brady, Megan; Blackwell, Ken; Haxhinasto, Kari; Fotedar, Sunaina; Borgman, Kurt; Bai, Er-Wei; Moy, Alan B
2005-09-01
Transendothelial impedance across an endothelial monolayer grown on a microelectrode has previously been modeled as a repeating pattern of disks in which the electrical circuit consists of a resistor and capacitor in series. Although this numerical model breaks down barrier function into measurements of cell-cell adhesion, cell-matrix adhesion, and membrane capacitance, such solution parameters can be inaccurate without understanding model stability and error. In this study, we have evaluated modeling stability and error by using a chi(2) evaluation and Levenberg-Marquardt nonlinear least-squares (LM-NLS) method of the real and/or imaginary data in which the experimental measurement is compared with the calculated measurement derived by the model. Modeling stability and error were dependent on current frequency and the type of experimental data modeled. Solution parameters of cell-matrix adhesion were most susceptible to modeling instability. Furthermore, the LM-NLS method displayed frequency-dependent instability of the solution parameters, regardless of whether the real or imaginary data were analyzed. However, the LM-NLS method identified stable and reproducible solution parameters between all types of experimental data when a defined frequency spectrum of the entire data set was selected on the basis of a criterion of minimizing error. The frequency bandwidth that produced stable solution parameters varied greatly among different data types. Thus a numerical model based on characterizing transendothelial impedance as a resistor and capacitor in series and as a repeating pattern of disks is not sufficient to characterize the entire frequency spectrum of experimental transendothelial impedance.
Cooling load calculation by the radiant time series method - effect of solar radiation models
Energy Technology Data Exchange (ETDEWEB)
Costa, Alexandre M.S. [Universidade Estadual de Maringa (UEM), PR (Brazil)], E-mail: amscosta@uem.br
2010-07-01
In this work was analyzed numerically the effect of three different models for solar radiation on the cooling load calculated by the radiant time series' method. The solar radiation models implemented were clear sky, isotropic sky and anisotropic sky. The radiant time series' method (RTS) was proposed by ASHRAE (2001) for replacing the classical methods of cooling load calculation, such as TETD/TA. The method is based on computing the effect of space thermal energy storage on the instantaneous cooling load. The computing is carried out by splitting the heat gain components in convective and radiant parts. Following the radiant part is transformed using time series, which coefficients are a function of the construction type and heat gain (solar or non-solar). The transformed result is added to the convective part, giving the instantaneous cooling load. The method was applied for investigate the influence for an example room. The location used was - 23 degree S and 51 degree W and the day was 21 of January, a typical summer day in the southern hemisphere. The room was composed of two vertical walls with windows exposed to outdoors with azimuth angles equals to west and east directions. The output of the different models of solar radiation for the two walls in terms of direct and diffuse components as well heat gains were investigated. It was verified that the clear sky exhibited the less conservative (higher values) for the direct component of solar radiation, with the opposite trend for the diffuse component. For the heat gain, the clear sky gives the higher values, three times higher for the peek hours than the other models. Both isotropic and anisotropic models predicted similar magnitude for the heat gain. The same behavior was also verified for the cooling load. The effect of room thermal inertia was decreasing the cooling load during the peak hours. On the other hand the higher thermal inertia values are the greater for the non peak hours. The effect
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.
A Long-Term Prediction Model of Beijing Haze Episodes Using Time Series Analysis
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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.
Scaling symmetry, renormalization, and time series modeling: the case of financial assets dynamics.
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.
Scaling symmetry, renormalization, and time series modeling: The case of financial assets dynamics
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.
Accurate estimation of energy expenditure (EE) in children and adolescents is required for a better understanding of physiological, behavioral, and environmental factors affecting energy balance. Cross-sectional time series (CSTS) models, which account for correlation structure of repeated observati...
Time Series Neural Network Model for Part-of-Speech Tagging Indonesian Language
Tanadi, Theo
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.
Artificial neural networks for modeling time series of beach litter in the southern North Sea.
Schulz, Marcus; Matthies, Michael
2014-07-01
In European marine waters, existing monitoring programs of beach litter need to be improved concerning litter items used as indicators of pollution levels, efficiency, and effectiveness. In order to ease and focus future monitoring of beach litter on few important litter items, feed-forward neural networks consisting of three layers were developed to relate single litter items to general categories of marine litter. The neural networks developed were applied to seven beaches in the southern North Sea and modeled time series of five general categories of marine litter, such as litter from fishing, shipping, and tourism. Results of regression analyses show that general categories were predicted significantly moderately to well. Measured and modeled data were in the same order of magnitude, and minima and maxima overlapped well. Neural networks were found to be eligible tools to deliver reliable predictions of marine litter with low computational effort and little input of information. Copyright © 2014 Elsevier Ltd. All rights reserved.
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.
Ramseyer, Fabian; Kupper, Zeno; Caspar, Franz; Znoj, Hansjörg; Tschacher, Wolfgang
2014-10-01
Processes occurring in the course of psychotherapy are characterized by the simple fact that they unfold in time and that the multiple factors engaged in change processes vary highly between individuals (idiographic phenomena). Previous research, however, has neglected the temporal perspective by its traditional focus on static phenomena, which were mainly assessed at the group level (nomothetic phenomena). To support a temporal approach, the authors introduce time-series panel analysis (TSPA), a statistical methodology explicitly focusing on the quantification of temporal, session-to-session aspects of change in psychotherapy. TSPA-models are initially built at the level of individuals and are subsequently aggregated at the group level, thus allowing the exploration of prototypical models. TSPA is based on vector auto-regression (VAR), an extension of univariate auto-regression models to multivariate time-series data. The application of TSPA is demonstrated in a sample of 87 outpatient psychotherapy patients who were monitored by postsession questionnaires. Prototypical mechanisms of change were derived from the aggregation of individual multivariate models of psychotherapy process. In a 2nd step, the associations between mechanisms of change (TSPA) and pre- to postsymptom change were explored. TSPA allowed a prototypical process pattern to be identified, where patient's alliance and self-efficacy were linked by a temporal feedback-loop. Furthermore, therapist's stability over time in both mastery and clarification interventions was positively associated with better outcomes. TSPA is a statistical tool that sheds new light on temporal mechanisms of change. Through this approach, clinicians may gain insight into prototypical patterns of change in psychotherapy. PsycINFO Database Record (c) 2014 APA, all rights reserved.
Stochastic models in the DORIS position time series: estimates for IDS contribution to ITRF2014
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.
Linden, Ariel
2018-05-11
Interrupted time series analysis (ITSA) is an evaluation methodology in which a single treatment unit's outcome is studied serially over time and the intervention is expected to "interrupt" the level and/or trend of that outcome. ITSA is commonly evaluated using methods which may produce biased results if model assumptions are violated. In this paper, treatment effects are alternatively assessed by using forecasting methods to closely fit the preintervention observations and then forecast the post-intervention trend. A treatment effect may be inferred if the actual post-intervention observations diverge from the forecasts by some specified amount. The forecasting approach is demonstrated using the effect of California's Proposition 99 for reducing cigarette sales. Three forecast models are fit to the preintervention series-linear regression (REG), Holt-Winters (HW) non-seasonal smoothing, and autoregressive moving average (ARIMA)-and forecasts are generated into the post-intervention period. The actual observations are then compared with the forecasts to assess intervention effects. The preintervention data were fit best by HW, followed closely by ARIMA. REG fit the data poorly. The actual post-intervention observations were above the forecasts in HW and ARIMA, suggesting no intervention effect, but below the forecasts in the REG (suggesting a treatment effect), thereby raising doubts about any definitive conclusion of a treatment effect. In a single-group ITSA, treatment effects are likely to be biased if the model is misspecified. Therefore, evaluators should consider using forecast models to accurately fit the preintervention data and generate plausible counterfactual forecasts, thereby improving causal inference of treatment effects in single-group ITSA studies. © 2018 John Wiley & Sons, Ltd.
Optimizing the De-Noise Neural Network Model for GPS Time-Series Monitoring of Structures
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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.
The string prediction models as invariants of time series in the forex market
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.
Assessing the effects of pharmacological agents on respiratory dynamics using time-series modeling.
Wong, Kin Foon Kevin; Gong, Jen J; Cotten, Joseph F; Solt, Ken; Brown, Emery N
2013-04-01
Developing quantitative descriptions of how stimulant and depressant drugs affect the respiratory system is an important focus in medical research. Respiratory variables-respiratory rate, tidal volume, and end tidal carbon dioxide-have prominent temporal dynamics that make it inappropriate to use standard hypothesis-testing methods that assume independent observations to assess the effects of these pharmacological agents. We present a polynomial signal plus autoregressive noise model for analysis of continuously recorded respiratory variables. We use a cyclic descent algorithm to maximize the conditional log likelihood of the parameters and the corrected Akaike's information criterion to choose simultaneously the orders of the polynomial and the autoregressive models. In an analysis of respiratory rates recorded from anesthetized rats before and after administration of the respiratory stimulant methylphenidate, we use the model to construct within-animal z-tests of the drug effect that take account of the time-varying nature of the mean respiratory rate and the serial dependence in rate measurements. We correct for the effect of model lack-of-fit on our inferences by also computing bootstrap confidence intervals for the average difference in respiratory rate pre- and postmethylphenidate treatment. Our time-series modeling quantifies within each animal the substantial increase in mean respiratory rate and respiratory dynamics following methylphenidate administration. This paradigm can be readily adapted to analyze the dynamics of other respiratory variables before and after pharmacologic treatments.
Clustering gene expression time series data using an infinite Gaussian process mixture model.
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.
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.
Historical Streamflow Series Analysis Applied to Furnas HPP Reservoir Watershed Using the SWAT Model
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Viviane de Souza Dias
2018-04-01
Full Text Available Over the last few years, the operation of the Furnas Hydropower Plant (HPP reservoir, located in the Grande River Basin, has been threatened due to a significant reduction in inflow. In the region, hydrological modelling tools are being used and tested to support decision making and water sustainability. In this study, the streamflow was modelled in the area of direct influence of the Furnas HPP reservoir, and the Soil and Water Assessment Tool (SWAT model performance was verified for studies in the region. Analyses of sensitivity and uncertainty were undertaken using the Sequential Uncertainty Fitting algorithm (SUFI-2 with a Calibration Uncertainty Program (SWAT-CUP. The hydrological modelling, at a monthly scale, presented good results in the calibration (NS 0.86, with a slight reduction of the coefficient in the validation period (NS 0.64. The results suggested that this tool could be applied in future hydrological studies in the region of study. With the consideration that special attention should be given to the historical series used in the calibration and validation of the models. It is important to note that this region has high demands for water resources, primarily for agricultural use. Water demands must also be taken into account in future hydrological simulations. The validation of this methodology led to important contributions to the management of water resources in regions with tropical climates, whose climatological and geological reality resembles the one studied here.
Tracer kinetic model-driven registration for dynamic contrast-enhanced MRI time-series data.
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.
Kolokythas, Kostantinos; Vasileios, Salamalikis; Athanassios, Argiriou; Kazantzidis, Andreas
2015-04-01
The wind is a result of complex interactions of numerous mechanisms taking place in small or large scales, so, the better knowledge of its behavior is essential in a variety of applications, especially in the field of power production coming from wind turbines. In the literature there is a considerable number of models, either physical or statistical ones, dealing with the problem of simulation and prediction of wind speed. Among others, Artificial Neural Networks (ANNs) are widely used for the purpose of wind forecasting and, in the great majority of cases, outperform other conventional statistical models. In this study, a number of ANNs with different architectures, which have been created and applied in a dataset of wind time series, are compared to Auto Regressive Integrated Moving Average (ARIMA) statistical models. The data consist of mean hourly wind speeds coming from a wind farm on a hilly Greek region and cover a period of one year (2013). The main goal is to evaluate the models ability to simulate successfully the wind speed at a significant point (target). Goodness-of-fit statistics are performed for the comparison of the different methods. In general, the ANN showed the best performance in the estimation of wind speed prevailing over the ARIMA models.
An advection-based model to increase the temporal resolution of PIV time series.
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
The ab initio model potential method. Second series transition metal elements
International Nuclear Information System (INIS)
Barandiaran, Z.; Seijo, L.; Huzinaga, S.
1990-01-01
The ab initio core method potential model (AIMP) has already been presented in its nonrelativistic version and applied to the main group and first series transition metal elements [J. Chem. Phys. 86, 2132 (1987); 91, 7011 (1989)]. In this paper we extend the AIMP method to include relativistic effects within the Cowan--Griffin approximation and we present relativistic Zn-like core model potentials and valence basis sets, as well as their nonrelativistic Zn-like core and Kr-like core counterparts. The pilot molecular calculations on YO, TcO, AgO, and AgH reveal that the 4p orbital is indeed a core orbital only at the end part of the series, whereas the 4s orbital can be safely frozen from Y to Cd. The all-electron and model potential results agree in 0.01--0.02 A in R e and 25--50 cm -1 in bar ν e if the same type of valence part of the basis set is used. The comparison of the relativistic results on AgH with those of the all-electron Dirac--Fock calculations by Lee and McLean is satisfactory: the absolute value of R e is reproduced within the 0.01 A margin and the relativistic contraction of 0.077 A is also very well reproduced (0.075 A). Finally, the relative magnitude of the effects of the core orbital change, mass--velocity potential, and Darwin potential on the net relativistic effects are analyzed in the four molecules studied
Hidden discriminative features extraction for supervised high-order time series modeling.
Nguyen, Ngoc Anh Thi; Yang, Hyung-Jeong; Kim, Sunhee
2016-11-01
In this paper, an orthogonal Tucker-decomposition-based extraction of high-order discriminative subspaces from a tensor-based time series data structure is presented, named as Tensor Discriminative Feature Extraction (TDFE). TDFE relies on the employment of category information for the maximization of the between-class scatter and the minimization of the within-class scatter to extract optimal hidden discriminative feature subspaces that are simultaneously spanned by every modality for supervised tensor modeling. In this context, the proposed tensor-decomposition method provides the following benefits: i) reduces dimensionality while robustly mining the underlying discriminative features, ii) results in effective interpretable features that lead to an improved classification and visualization, and iii) reduces the processing time during the training stage and the filtering of the projection by solving the generalized eigenvalue issue at each alternation step. Two real third-order tensor-structures of time series datasets (an epilepsy electroencephalogram (EEG) that is modeled as channel×frequency bin×time frame and a microarray data that is modeled as gene×sample×time) were used for the evaluation of the TDFE. The experiment results corroborate the advantages of the proposed method with averages of 98.26% and 89.63% for the classification accuracies of the epilepsy dataset and the microarray dataset, respectively. These performance averages represent an improvement on those of the matrix-based algorithms and recent tensor-based, discriminant-decomposition approaches; this is especially the case considering the small number of samples that are used in practice. Copyright © 2016 Elsevier Ltd. All rights reserved.
Predictive modeling of EEG time series for evaluating surgery targets in epilepsy patients.
Steimer, Andreas; Müller, Michael; Schindler, Kaspar
2017-05-01
During the last 20 years, predictive modeling in epilepsy research has largely been concerned with the prediction of seizure events, whereas the inference of effective brain targets for resective surgery has received surprisingly little attention. In this exploratory pilot study, we describe a distributional clustering framework for the modeling of multivariate time series and use it to predict the effects of brain surgery in epilepsy patients. By analyzing the intracranial EEG, we demonstrate how patients who became seizure free after surgery are clearly distinguished from those who did not. More specifically, for 5 out of 7 patients who obtained seizure freedom (= Engel class I) our method predicts the specific collection of brain areas that got actually resected during surgery to yield a markedly lower posterior probability for the seizure related clusters, when compared to the resection of random or empty collections. Conversely, for 4 out of 5 Engel class III/IV patients who still suffer from postsurgical seizures, performance of the actually resected collection is not significantly better than performances displayed by random or empty collections. As the number of possible collections ranges into billions and more, this is a substantial contribution to a problem that today is still solved by visual EEG inspection. Apart from epilepsy research, our clustering methodology is also of general interest for the analysis of multivariate time series and as a generative model for temporally evolving functional networks in the neurosciences and beyond. Hum Brain Mapp 38:2509-2531, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.
Dynamic Modeling Using MCSim and R (SOT 2016 Biological Modeling Webinar Series)
MCSim is a stand-alone software package for simulating and analyzing dynamic models, with a focus on Bayesian analysis using Markov Chain Monte Carlo. While it is an extremely powerful package, it is somewhat inflexible, and offers only a limited range of analysis options, with n...
Study on Apparent Kinetic Prediction Model of the Smelting Reduction Based on the Time-Series
Directory of Open Access Journals (Sweden)
Guo-feng Fan
2012-01-01
Full Text Available A series of direct smelting reduction experiment has been carried out with high phosphorous iron ore of the different bases by thermogravimetric analyzer. The derivative thermogravimetric (DTG data have been obtained from the experiments. One-step forward local weighted linear (LWL method , one of the most suitable ways of predicting chaotic time-series methods which focus on the errors, is used to predict DTG. In the meanwhile, empirical mode decomposition-autoregressive (EMD-AR, a data mining technique in signal processing, is also used to predict DTG. The results show that (1 EMD-AR(4 is the most appropriate and its error is smaller than the former; (2 root mean square error (RMSE has decreased about two-thirds; (3 standardized root mean square error (NMSE has decreased in an order of magnitude. Finally in this paper, EMD-AR method has been improved by golden section weighting; its error would be smaller than before. Therefore, the improved EMD-AR model is a promising alternative for apparent reaction rate (DTG. The analytical results have been an important reference in the field of industrial control.
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
Energy Technology Data Exchange (ETDEWEB)
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.
Diffusive and subdiffusive dynamics of indoor microclimate: a time series modeling.
Maciejewska, Monika; Szczurek, Andrzej; Sikora, Grzegorz; Wyłomańska, Agnieszka
2012-09-01
The indoor microclimate is an issue in modern society, where people spend about 90% of their time indoors. Temperature and relative humidity are commonly used for its evaluation. In this context, the two parameters are usually considered as behaving in the same manner, just inversely correlated. This opinion comes from observation of the deterministic components of temperature and humidity time series. We focus on the dynamics and the dependency structure of the time series of these parameters, without deterministic components. Here we apply the mean square displacement, the autoregressive integrated moving average (ARIMA), and the methodology for studying anomalous diffusion. The analyzed data originated from five monitoring locations inside a modern office building, covering a period of nearly one week. It was found that the temperature data exhibited a transition between diffusive and subdiffusive behavior, when the building occupancy pattern changed from the weekday to the weekend pattern. At the same time the relative humidity consistently showed diffusive character. Also the structures of the dependencies of the temperature and humidity data sets were different, as shown by the different structures of the ARIMA models which were found appropriate. In the space domain, the dynamics and dependency structure of the particular parameter were preserved. This work proposes an approach to describe the very complex conditions of indoor air and it contributes to the improvement of the representative character of microclimate monitoring.
Knowledge fusion: An approach to time series model selection followed by pattern recognition
International Nuclear Information System (INIS)
Bleasdale, S.A.; Burr, T.L.; Scovel, J.C.; Strittmatter, R.B.
1996-03-01
This report describes work done during FY 95 that was sponsored by the Department of Energy, Office of Nonproliferation and National Security, Knowledge Fusion Project. The project team selected satellite sensor data to use as the one main example for the application of its analysis algorithms. The specific sensor-fusion problem has many generic features, which 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 that define 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. An accompanying report (Ref 1) describes the implementation and application of this 2-step process for separating events from unusual background and applies a suite of forecasting methods followed by a suite of pattern recognition methods. This report goes into more detail about one of the forecasting methods and one of the pattern recognition methods and is applied to the same kind of satellite-sensor data that is described in Ref. 1
Bayesian models of thermal and pluviometric time series in the Fucino plateau
Directory of Open Access Journals (Sweden)
Adriana Trabucco
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.
Chen, Yonghong; Bressler, Steven L.; Knuth, Kevin H.; Truccolo, Wilson A.; Ding, Mingzhou
2006-06-01
In this article we consider the stochastic modeling of neurobiological time series from cognitive experiments. Our starting point is the variable-signal-plus-ongoing-activity model. From this model a differentially variable component analysis strategy is developed from a Bayesian perspective to estimate event-related signals on a single trial basis. After subtracting out the event-related signal from recorded single trial time series, the residual ongoing activity is treated as a piecewise stationary stochastic process and analyzed by an adaptive multivariate autoregressive modeling strategy which yields power, coherence, and Granger causality spectra. Results from applying these methods to local field potential recordings from monkeys performing cognitive tasks are presented.
Autoregressive-model-based missing value estimation for DNA microarray time series data.
Choong, Miew Keen; Charbit, Maurice; Yan, Hong
2009-01-01
Missing value estimation is important in DNA microarray data analysis. A number of algorithms have been developed to solve this problem, but they have several limitations. Most existing algorithms are not able to deal with the situation where a particular time point (column) of the data is missing entirely. In this paper, we present an autoregressive-model-based missing value estimation method (ARLSimpute) that takes into account the dynamic property of microarray temporal data and the local similarity structures in the data. ARLSimpute is especially effective for the situation where a particular time point contains many missing values or where the entire time point is missing. Experiment results suggest that our proposed algorithm is an accurate missing value estimator in comparison with other imputation methods on simulated as well as real microarray time series datasets.
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
Directory of Open Access Journals (Sweden)
Tri-Vien Vu
2014-10-01
Full Text Available This study applied a model predictive control (MPC framework to solve the cruising control problem of a series hydraulic hybrid vehicle (SHHV. The controller not only regulates vehicle velocity, but also engine torque, engine speed, and accumulator pressure to their corresponding reference values. At each time step, a quadratic programming problem is solved within a predictive horizon to obtain the optimal control inputs. The objective is to minimize the output error. This approach ensures that the components operate at high efficiency thereby improving the total efficiency of the system. The proposed SHHV control system was evaluated under urban and highway driving conditions. By handling constraints and input-output interactions, the MPC-based control system ensures that the system operates safely and efficiently. The fuel economy of the proposed control scheme shows a noticeable improvement in comparison with the PID-based system, in which three Proportional-Integral-Derivative (PID controllers are used for cruising control.
The air forces on a systematic series of biplane and triplane cellule models
Munk, Max M
1927-01-01
The air forces on a systematic series of biplane and triplane cellule models are the subject of this report. The test consist in the determination of the lift, drag, and moment of each individual airfoil in each cellule, mostly with the same wing section. The magnitude of the gap and of the stagger is systematically varied; not, however, the decalage, which is zero throughout the tests. Certain check tests with a second wing section make the tests more complete and conclusions more convincing. The results give evidence that the present army and navy specifications for the relative lifts of biplanes are good. They furnish material for improving such specifications for the relative lifts of triplanes. A larger number of factors can now be prescribed to take care of different cases.
Time-Elastic Generative Model for Acceleration Time Series in Human Activity Recognition.
Munoz-Organero, Mario; Ruiz-Blazquez, Ramona
2017-02-08
Body-worn sensors in general and accelerometers in particular have been widely used in order to detect human movements and activities. The execution of each type of movement by each particular individual generates sequences of time series of sensed data from which specific movement related patterns can be assessed. Several machine learning algorithms have been used over windowed segments of sensed data in order to detect such patterns in activity recognition based on intermediate features (either hand-crafted or automatically learned from data). The underlying assumption is that the computed features will capture statistical differences that can properly classify different movements and activities after a training phase based on sensed data. In order to achieve high accuracy and recall rates (and guarantee the generalization of the system to new users), the training data have to contain enough information to characterize all possible ways of executing the activity or movement to be detected. This could imply large amounts of data and a complex and time-consuming training phase, which has been shown to be even more relevant when automatically learning the optimal features to be used. In this paper, we present a novel generative model that is able to generate sequences of time series for characterizing a particular movement based on the time elasticity properties of the sensed data. The model is used to train a stack of auto-encoders in order to learn the particular features able to detect human movements. The results of movement detection using a newly generated database with information on five users performing six different movements are presented. The generalization of results using an existing database is also presented in the paper. The results show that the proposed mechanism is able to obtain acceptable recognition rates ( F = 0.77) even in the case of using different people executing a different sequence of movements and using different hardware.
Time-Elastic Generative Model for Acceleration Time Series in Human Activity Recognition
Directory of Open Access Journals (Sweden)
Mario Munoz-Organero
2017-02-01
Full Text Available Body-worn sensors in general and accelerometers in particular have been widely used in order to detect human movements and activities. The execution of each type of movement by each particular individual generates sequences of time series of sensed data from which specific movement related patterns can be assessed. Several machine learning algorithms have been used over windowed segments of sensed data in order to detect such patterns in activity recognition based on intermediate features (either hand-crafted or automatically learned from data. The underlying assumption is that the computed features will capture statistical differences that can properly classify different movements and activities after a training phase based on sensed data. In order to achieve high accuracy and recall rates (and guarantee the generalization of the system to new users, the training data have to contain enough information to characterize all possible ways of executing the activity or movement to be detected. This could imply large amounts of data and a complex and time-consuming training phase, which has been shown to be even more relevant when automatically learning the optimal features to be used. In this paper, we present a novel generative model that is able to generate sequences of time series for characterizing a particular movement based on the time elasticity properties of the sensed data. The model is used to train a stack of auto-encoders in order to learn the particular features able to detect human movements. The results of movement detection using a newly generated database with information on five users performing six different movements are presented. The generalization of results using an existing database is also presented in the paper. The results show that the proposed mechanism is able to obtain acceptable recognition rates (F = 0.77 even in the case of using different people executing a different sequence of movements and using different
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.
Fast and Scalable Gaussian Process Modeling with Applications to Astronomical Time Series
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.
PENGEMBANGAN FOIL NACA SERI 2412 SEBAGAI SISTEM PENYELAMAN MODEL KAPAL SELAM
Directory of Open Access Journals (Sweden)
Ali Munazid
2015-06-01
Full Text Available Bentuk foil menghasilkan gaya angkat (lift force ketika foil dilewati oleh aliran fluida karena adanya pengaruh interaksi antara aliran fluida dengan permukaan foil yang mengakibatkan tekanan permukaan atas lebih kecil dari permukaan bawah. Bagaimana mengaplikasikan teori foil pada hydroplane kapal selam sebagai system penyelaman, dengan membalik foil maka lift force tersebut menjadi gaya ke bawah, dengan demikian memungkinkan kapal selam dapat menyelam, melayang dan bermanouver di bawah air, seperti halnya gerak pesawat terbang yang terbang dan melayang dengan menggunakan sayap. Dilakukan penelitian dan pengamatan terhadap kemampuan penyelaman (diving plan dari foil NACA seri 2412 pada model kapal selam, dengan mencari nilai Cl (coefisien lift di Laboratorium, serta mendesain bentuk badan kapal selam dan analisa gaya-gaya yang bekerja pada model kapal selam, jumlah gaya-gaya yang bekerja keatas lebih rendah dari gaya-gaya ke bawah maka kapal selam mampu menyelam. Penerapan Hydroplane sebagai diving plane dapat diterapkan, kemampuan penyelaman dipengaruhi oleh sudut flip Hydroplane dan kecepatan model, semakin besar kecepatan dan sudut flip maka semakin besar kedalaman penyelaman yang dapat dilakukan.
Comparison of Co-Temporal Modeling Algorithms on Sparse Experimental Time Series Data Sets.
Allen, Edward E; Norris, James L; John, David J; Thomas, Stan J; Turkett, William H; Fetrow, Jacquelyn S
2010-01-01
Multiple approaches for reverse-engineering biological networks from time-series data have been proposed in the computational biology literature. These approaches can be classified by their underlying mathematical algorithms, such as Bayesian or algebraic techniques, as well as by their time paradigm, which includes next-state and co-temporal modeling. The types of biological relationships, such as parent-child or siblings, discovered by these algorithms are quite varied. It is important to understand the strengths and weaknesses of the various algorithms and time paradigms on actual experimental data. We assess how well the co-temporal implementations of three algorithms, continuous Bayesian, discrete Bayesian, and computational algebraic, can 1) identify two types of entity relationships, parent and sibling, between biological entities, 2) deal with experimental sparse time course data, and 3) handle experimental noise seen in replicate data sets. These algorithms are evaluated, using the shuffle index metric, for how well the resulting models match literature models in terms of siblings and parent relationships. Results indicate that all three co-temporal algorithms perform well, at a statistically significant level, at finding sibling relationships, but perform relatively poorly in finding parent relationships.
Galka, Andreas; Siniatchkin, Michael; Stephani, Ulrich; Groening, Kristina; Wolff, Stephan; Bosch-Bayard, Jorge; Ozaki, Tohru
2010-12-01
The analysis of time series obtained by functional magnetic resonance imaging (fMRI) may be approached by fitting predictive parametric models, such as nearest-neighbor autoregressive models with exogeneous input (NNARX). As a part of the modeling procedure, it is possible to apply instantaneous linear transformations to the data. Spatial smoothing, a common preprocessing step, may be interpreted as such a transformation. The autoregressive parameters may be constrained, such that they provide a response behavior that corresponds to the canonical haemodynamic response function (HRF). We present an algorithm for estimating the parameters of the linear transformations and of the HRF within a rigorous maximum-likelihood framework. Using this approach, an optimal amount of both the spatial smoothing and the HRF can be estimated simultaneously for a given fMRI data set. An example from a motor-task experiment is discussed. It is found that, for this data set, weak, but non-zero, spatial smoothing is optimal. Furthermore, it is demonstrated that activated regions can be estimated within the maximum-likelihood framework.
Discrete series representations for sl(2|1), Meixner polynomials and oscillator models
International Nuclear Information System (INIS)
Jafarov, E I; Van der Jeugt, J
2012-01-01
We explore a model for a one-dimensional quantum oscillator based on the Lie superalgebra sl(2|1). For this purpose, a class of discrete series representations of sl(2|1) is constructed, each representation characterized by a real number β > 0. In this model, the position and momentum operators of the oscillator are odd elements of sl(2|1) and their expressions involve an arbitrary parameter γ. In each representation, the spectrum of the Hamiltonian is the same as that of a canonical oscillator. The spectrum of a position operator can be continuous or infinite discrete, depending on the value of γ. We determine the position wavefunctions both in the continuous and the discrete case and discuss their properties. In the discrete case, these wavefunctions are given in terms of Meixner polynomials. From the embedding osp(1|2) subset of sl(2|1), it can be seen why the case γ = 1 corresponds to a paraboson oscillator. Consequently, taking the values (β, γ) = (1/2, 1) in the sl(2|1) model yields a canonical oscillator. (paper)
Shao, Chenxi; Liu, Qingqing; Wang, Tingting; Yin, Peifeng; Wang, Binghong
2013-09-01
Time series is widely exploited to study the innate character of the complex chaotic system. Existing chaotic models are weak in modeling accuracy because of adopting either error minimization strategy or an acceptable error to end the modeling process. Instead, interpolation can be very useful for solving differential equations with a small modeling error, but it is also very difficult to deal with arbitrary-dimensional series. In this paper, geometric theory is considered to reduce the modeling error, and a high-precision framework called Series-NonUniform Rational B-Spline (S-NURBS) model is developed to deal with arbitrary-dimensional series. The capability of the interpolation framework is proved in the validation part. Besides, we verify its reliability by interpolating Musa dataset. The main improvement of the proposed framework is that we are able to reduce the interpolation error by properly adjusting weights series step by step if more information is given. Meanwhile, these experiments also demonstrate that studying the physical system from a geometric perspective is feasible.
Giassi, Pedro; Okida, Sergio; Oliveira, Maurício G; Moraes, Raimes
2013-11-01
Short-term cardiovascular regulation mediated by the sympathetic and parasympathetic branches of the autonomic nervous system has been investigated by multivariate autoregressive (MVAR) modeling, providing insightful analysis. MVAR models employ, as inputs, heart rate (HR), systolic blood pressure (SBP) and respiratory waveforms. ECG (from which HR series is obtained) and respiratory flow waveform (RFW) can be easily sampled from the patients. Nevertheless, the available methods for acquisition of beat-to-beat SBP measurements during exams hamper the wider use of MVAR models in clinical research. Recent studies show an inverse correlation between pulse wave transit time (PWTT) series and SBP fluctuations. PWTT is the time interval between the ECG R-wave peak and photoplethysmography waveform (PPG) base point within the same cardiac cycle. This study investigates the feasibility of using inverse PWTT (IPWTT) series as an alternative input to SBP for MVAR modeling of the cardiovascular regulation. For that, HR, RFW, and IPWTT series acquired from volunteers during postural changes and autonomic blockade were used as input of MVAR models. Obtained results show that IPWTT series can be used as input of MVAR models, replacing SBP measurements in order to overcome practical difficulties related to the continuous sampling of the SBP during clinical exams.
Cox, C.; Au, A.; Klosko, S.; Chao, B.; Smith, David E. (Technical Monitor)
2001-01-01
The upcoming GRACE mission promises to open a window on details of the global mass budget that will have remarkable clarity, but it will not directly answer the question of what the state of the Earth's mass budget is over the critical last quarter of the 20th century. To address that problem we must draw upon existing technologies such as SLR, DORIS, and GPS, and climate modeling runs in order to improve our understanding. Analysis of long-period geopotential changes based on SLR and DORIS tracking has shown that addition of post 1996 satellite tracking data has a significant impact on the recovered zonal rates and long-period tides. Interannual effects such as those causing the post 1996 anomalies must be better characterized before refined estimates of the decadal period changes in the geopotential can be derived from the historical database of satellite tracking. A possible cause of this anomaly is variations in ocean mass distribution, perhaps associated with the recent large El Nino/La Nina. In this study, a low-degree spherical harmonic gravity time series derived from satellite tracking is compared with a TOPEX/POSEIDON-derived sea surface height time series. Corrections for atmospheric mass effects, continental hydrology, snowfall accumulation, and ocean steric model predictions will be considered.
Remote sensing-based time series models for malaria early warning in the highlands of Ethiopia
Directory of Open Access Journals (Sweden)
Midekisa Alemayehu
2012-05-01
Full Text Available Abstract Background Malaria is one of the leading public health problems in most of sub-Saharan Africa, particularly in Ethiopia. Almost all demographic groups are at risk of malaria because of seasonal and unstable transmission of the disease. Therefore, there is a need to develop malaria early-warning systems to enhance public health decision making for control and prevention of malaria epidemics. Data from orbiting earth-observing sensors can monitor environmental risk factors that trigger malaria epidemics. Remotely sensed environmental indicators were used to examine the influences of climatic and environmental variability on temporal patterns of malaria cases in the Amhara region of Ethiopia. Methods In this study seasonal autoregressive integrated moving average (SARIMA models were used to quantify the relationship between malaria cases and remotely sensed environmental variables, including rainfall, land-surface temperature (LST, vegetation indices (NDVI and EVI, and actual evapotranspiration (ETa with lags ranging from one to three months. Predictions from the best model with environmental variables were compared to the actual observations from the last 12 months of the time series. Results Malaria cases exhibited positive associations with LST at a lag of one month and positive associations with indicators of moisture (rainfall, EVI and ETa at lags from one to three months. SARIMA models that included these environmental covariates had better fits and more accurate predictions, as evidenced by lower AIC and RMSE values, than models without environmental covariates. Conclusions Malaria risk indicators such as satellite-based rainfall estimates, LST, EVI, and ETa exhibited significant lagged associations with malaria cases in the Amhara region and improved model fit and prediction accuracy. These variables can be monitored frequently and extensively across large geographic areas using data from earth-observing sensors to support public
Pan-Arctic TV Series on Inuit wellness: a northern model of communication for social change?
Johnson, Rhonda; Morales, Robin; Leavitt, Doreen; Carry, Catherine; Kinnon, Dianne; Rideout, Denise; Clarida, Kath
2011-06-01
This paper provides highlights of a utilization-focused evaluation of a collaborative Pan-Arctic Inuit Wellness TV Series that was broadcast live in Alaska and Canada in May 2009. This International Polar Year (IPY) communication and outreach project intended to (1) share information on International Polar Year research progress, disseminate findings and explore questions with Inuit in Alaska, Canada and Greenland; (2) provide a forum for Inuit in Alaska, Canada and Greenland to showcase innovative health and wellness projects; (3) ensure Inuit youth and adult engagement throughout; and (4) document and reflect on the overall experience for the purposes of developing and "testing" a participatory communication model. Utilization-focused formative evaluation of the project, with a focus on overall objectives, key messages and lessons learned to facilitate program improvement. Participant observation, surveys, key informant interviews, document review and website tracking. Promising community programs related to 3 themes - men's wellness, maternity care and youth resilience - in diverse circumpolar regions were highlighted, as were current and stillevolving findings from ongoing Arctic research. Multiple media methods were used to effectively deliver and receive key messages determined by both community and academic experts. Local capacity and new regional networks were strengthened. Evidence-based resources for health education and community action were archived in digital formats (websites and DVDs), increasing accessibility to otherwise isolated individuals and remote communities. The Pan-Arctic Inuit Wellness TV Series was an innovative, multi-dimensional communication project that raised both interest and awareness about complex health conditions in the North and stimulated community dialogue and potential for increased collaborative action. Consistent with a communication for social change approach, the project created new networks, increased motivation to act
Statistical modeling of isoform splicing dynamics from RNA-seq time series data.
Huang, Yuanhua; Sanguinetti, Guido
2016-10-01
Isoform quantification is an important goal of RNA-seq experiments, yet it remains problematic for genes with low expression or several isoforms. These difficulties may in principle be ameliorated by exploiting correlated experimental designs, such as time series or dosage response experiments. Time series RNA-seq experiments, in particular, are becoming increasingly popular, yet there are no methods that explicitly leverage the experimental design to improve isoform quantification. Here, we present DICEseq, the first isoform quantification method tailored to correlated RNA-seq experiments. DICEseq explicitly models the correlations between different RNA-seq experiments to aid the quantification of isoforms across experiments. Numerical experiments on simulated datasets show that DICEseq yields more accurate results than state-of-the-art methods, an advantage that can become considerable at low coverage levels. On real datasets, our results show that DICEseq provides substantially more reproducible and robust quantifications, increasing the correlation of estimates from replicate datasets by up to 10% on genes with low or moderate expression levels (bottom third of all genes). Furthermore, DICEseq permits to quantify the trade-off between temporal sampling of RNA and depth of sequencing, frequently an important choice when planning experiments. Our results have strong implications for the design of RNA-seq experiments, and offer a novel tool for improved analysis of such datasets. Python code is freely available at http://diceseq.sf.net G.Sanguinetti@ed.ac.uk Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Linear time series modeling of GPS-derived TEC observations over the Indo-Thailand region
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.
Beyond Rating Curves: Time Series Models for in-Stream Turbidity Prediction
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
Assessment and prediction of road accident injuries trend using time-series models in Kurdistan.
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
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 veg...
2013-12-17
...-0899; Notice No. 25-13-15-SC] Special Conditions: Airbus, Model A350-900 Series Airplane; Control... of proposed special conditions. SUMMARY: This action proposes special conditions for the Airbus Model..., data, or views. The most helpful comments reference a specific portion of the proposed special...
Mansour, M M; Spink, A E F
2013-01-01
Grid refinement is introduced in a numerical groundwater model to increase the accuracy of the solution over local areas without compromising the run time of the model. Numerical methods developed for grid refinement suffered certain drawbacks, for example, deficiencies in the implemented interpolation technique; the non-reciprocity in head calculations or flow calculations; lack of accuracy resulting from high truncation errors, and numerical problems resulting from the construction of elongated meshes. A refinement scheme based on the divergence theorem and Taylor's expansions is presented in this article. This scheme is based on the work of De Marsily (1986) but includes more terms of the Taylor's series to improve the numerical solution. In this scheme, flow reciprocity is maintained and high order of refinement was achievable. The new numerical method is applied to simulate groundwater flows in homogeneous and heterogeneous confined aquifers. It produced results with acceptable degrees of accuracy. This method shows the potential for its application to solving groundwater heads over nested meshes with irregular shapes. © 2012, British Geological Survey © NERC 2012. Ground Water © 2012, National GroundWater Association.
Computer models of dipole magnets of a series 'VULCAN' for the ALICE experiment
International Nuclear Information System (INIS)
Vodop'yanov, A.S.; Shishov, Yu.A.; Yuldasheva, M.B.; Yuldashev, O.I.
1998-01-01
The paper is devoted to a construction of computer models for three magnets of the 'VULCAN' series in the framework of a differential approach for two scalar potentials. The distinctive property of these magnets is that they are 'warm' and their coils are of conic saddle shape. The algorithm of creating a computer model for the coils is suggested. The coil field is computed by Biot-Savart law and a part of the integrals is calculated with the help of analytical formulas. To compute three-dimensional magnetic fields by the finite element method with a local accuracy control, two new algorithms are suggested. The former is based on a comparison of the fields computed by means of linear and quadratic shape functions. The latter is based on a comparison of the field computed with the help of linear shape functions and a local classical solution. The distributions of the local accuracy control characteristics within a working part of the third magnet and the other results of the computations are presented
Institute of Scientific and Technical Information of China (English)
L(U) Wei-cai; XU Shao-quan
2004-01-01
Using similar single-difference methodology(SSDM) to solve the deformation values of the monitoring points, there is unstability of the deformation information series, at sometimes.In order to overcome this shortcoming, Kalman filtering algorithm for this series is established,and its correctness and validity are verified with the test data obtained on the movable platform in plane. The results show that Kalman filtering can improve the correctness, reliability and stability of the deformation information series.
Advanced methods for modeling water-levels and estimating drawdowns with SeriesSEE, an Excel add-in
Halford, Keith; Garcia, C. Amanda; Fenelon, Joe; Mirus, Benjamin B.
2012-12-21
Water-level modeling is used for multiple-well aquifer tests to reliably differentiate pumping responses from natural water-level changes in wells, or “environmental fluctuations.” Synthetic water levels are created during water-level modeling and represent the summation of multiple component fluctuations, including those caused by environmental forcing and pumping. Pumping signals are modeled by transforming step-wise pumping records into water-level changes by using superimposed Theis functions. Water-levels can be modeled robustly with this Theis-transform approach because environmental fluctuations and pumping signals are simulated simultaneously. Water-level modeling with Theis transforms has been implemented in the program SeriesSEE, which is a Microsoft® Excel add-in. Moving average, Theis, pneumatic-lag, and gamma functions transform time series of measured values into water-level model components in SeriesSEE. Earth tides and step transforms are additional computed water-level model components. Water-level models are calibrated by minimizing a sum-of-squares objective function where singular value decomposition and Tikhonov regularization stabilize results. Drawdown estimates from a water-level model are the summation of all Theis transforms minus residual differences between synthetic and measured water levels. The accuracy of drawdown estimates is limited primarily by noise in the data sets, not the Theis-transform approach. Drawdowns much smaller than environmental fluctuations have been detected across major fault structures, at distances of more than 1 mile from the pumping well, and with limited pre-pumping and recovery data at sites across the United States. In addition to water-level modeling, utilities exist in SeriesSEE for viewing, cleaning, manipulating, and analyzing time-series data.
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 two and six months of age, used hidden Markov modeling, 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 4-state model for the dyadic responses to a two-month inoculation whereas a 6-state model best described the dyadic process at six months. Two of the states at two months and three of the states at six months suggested a progression from high intensity crying to no crying with parents using vestibular and auditory soothing methods. The use of feeding and/or pacifying to soothe the infant characterized one two-month state and two six-month states. These data indicate that with maturation and experience, the mother-infant dyad is becoming more organized around the soothing interaction. Using hidden Markov modeling to describe individual differences, as well as normative processes, is also presented and discussed.
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.
Li, Yue; Jha, Devesh K; Ray, Asok; Wettergren, Thomas A; Yue Li; Jha, Devesh K; Ray, Asok; Wettergren, Thomas A; Wettergren, Thomas A; Li, Yue; Ray, Asok; Jha, Devesh K
2018-06-01
This paper presents information-theoretic performance analysis of passive sensor networks for detection of moving targets. The proposed method falls largely under the category of data-level information fusion in sensor networks. To this end, a measure of information contribution for sensors is formulated in a symbolic dynamics framework. The network information state is approximately represented as the largest principal component of the time series collected across the network. To quantify each sensor's contribution for generation of the information content, Markov machine models as well as x-Markov (pronounced as cross-Markov) machine models, conditioned on the network information state, are constructed; the difference between the conditional entropies of these machines is then treated as an approximate measure of information contribution by the respective sensors. The x-Markov models represent the conditional temporal statistics given the network information state. The proposed method has been validated on experimental data collected from a local area network of passive sensors for target detection, where the statistical characteristics of environmental disturbances are similar to those of the target signal in the sense of time scale and texture. A distinctive feature of the proposed algorithm is that the network decisions are independent of the behavior and identity of the individual sensors, which is desirable from computational perspectives. Results are presented to demonstrate the proposed method's efficacy to correctly identify the presence of a target with very low false-alarm rates. The performance of the underlying algorithm is compared with that of a recent data-driven, feature-level information fusion algorithm. It is shown that the proposed algorithm outperforms the other algorithm.
Investigating Access Performance of Long Time Series with Restructured Big Model Data
Shen, S.; Ostrenga, D.; Vollmer, B.; Meyer, D. J.
2017-12-01
Data sets generated by models are substantially increasing in volume, due to increases in spatial and temporal resolution, and the number of output variables. Many users wish to download subsetted data in preferred data formats and structures, as it is getting increasingly difficult to handle the original full-size data files. For example, application research users, such as those involved with wind or solar energy, or extreme weather events, are likely only interested in daily or hourly model data at a single point or for a small area for a long time period, and prefer to have the data downloaded in a single file. With native model file structures, such as hourly data from NASA Modern-Era Retrospective analysis for Research and Applications Version-2 (MERRA-2), it may take over 10 hours for the extraction of interested parameters at a single point for 30 years. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is exploring methods to address this particular user need. One approach is to create value-added data by reconstructing the data files. Taking MERRA-2 data as an example, we have tested converting hourly data from one-day-per-file into different data cubes, such as one-month, one-year, or whole-mission. Performance are compared for reading local data files and accessing data through interoperable service, such as OPeNDAP. Results show that, compared to the original file structure, the new data cubes offer much better performance for accessing long time series. We have noticed that performance is associated with the cube size and structure, the compression method, and how the data are accessed. An optimized data cube structure will not only improve data access, but also may enable better online analytic services.
High-order fuzzy time-series based on multi-period adaptation model for forecasting stock markets
Chen, Tai-Liang; Cheng, Ching-Hsue; Teoh, Hia-Jong
2008-02-01
Stock investors usually make their short-term investment decisions according to recent stock information such as the late market news, technical analysis reports, and price fluctuations. To reflect these short-term factors which impact stock price, this paper proposes a comprehensive fuzzy time-series, which factors linear relationships between recent periods of stock prices and fuzzy logical relationships (nonlinear relationships) mined from time-series into forecasting processes. In empirical analysis, the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) and HSI (Heng Seng Index) are employed as experimental datasets, and four recent fuzzy time-series models, Chen’s (1996), Yu’s (2005), Cheng’s (2006) and Chen’s (2007), are used as comparison models. Besides, to compare with conventional statistic method, the method of least squares is utilized to estimate the auto-regressive models of the testing periods within the databases. From analysis results, the performance comparisons indicate that the multi-period adaptation model, proposed in this paper, can effectively improve the forecasting performance of conventional fuzzy time-series models which only factor fuzzy logical relationships in forecasting processes. From the empirical study, the traditional statistic method and the proposed model both reveal that stock price patterns in the Taiwan stock and Hong Kong stock markets are short-term.
Numerical modelling of series-parallel cooling systems in power plant
Directory of Open Access Journals (Sweden)
Regucki Paweł
2017-01-01
Full Text Available The paper presents a mathematical model allowing one to study series-parallel hydraulic systems like, e.g., the cooling system of a power boiler's auxiliary devices or a closed cooling system including condensers and cooling towers. The analytical approach is based on a set of non-linear algebraic equations solved using numerical techniques. As a result of the iterative process, a set of volumetric flow rates of water through all the branches of the investigated hydraulic system is obtained. The calculations indicate the influence of changes in the pipeline's geometrical parameters on the total cooling water flow rate in the analysed installation. Such an approach makes it possible to analyse different variants of the modernization of the studied systems, as well as allowing for the indication of its critical elements. Basing on these results, an investor can choose the optimal variant of the reconstruction of the installation from the economic point of view. As examples of such a calculation, two hydraulic installations are described. One is a boiler auxiliary cooling installation including two screw ash coolers. The other is a closed cooling system consisting of cooling towers and condensers.
Testing Homeopathy in Mouse Emotional Response Models: Pooled Data Analysis of Two Series of Studies
Directory of Open Access Journals (Sweden)
Paolo Bellavite
2012-01-01
Full Text Available Two previous investigations were performed to assess the activity of Gelsemium sempervirens (Gelsemium s. in mice, using emotional response models. These two series are pooled and analysed here. Gelsemium s. in various homeopathic centesimal dilutions/dynamizations (4C, 5C, 7C, 9C, and 30C, a placebo (solvent vehicle, and the reference drugs diazepam (1 mg/kg body weight or buspirone (5 mg/kg body weight were delivered intraperitoneally to groups of albino CD1 mice, and their effects on animal behaviour were assessed by the light-dark (LD choice test and the open-field (OF exploration test. Up to 14 separate replications were carried out in fully blind and randomised conditions. Pooled analysis demonstrated highly significant effects of Gelsemium s. 5C, 7C, and 30C on the OF parameter “time spent in central area” and of Gelsemium s. 5C, 9C, and 30C on the LD parameters “time spent in lit area” and “number of light-dark transitions,” without any sedative action or adverse effects on locomotion. This pooled data analysis confirms and reinforces the evidence that Gelsemium s. regulates emotional responses and behaviour of laboratory mice in a nonlinear fashion with dilution/dynamization.
Low-derivative operators of the Standard Model effective field theory via Hilbert series methods
Energy Technology Data Exchange (ETDEWEB)
Lehman, Landon; Martin, Adam [Department of Physics, University of Notre Dame,Nieuwland Science Hall, Notre Dame, IN 46556 (United States)
2016-02-12
In this work, we explore an extension of Hilbert series techniques to count operators that include derivatives. For sufficiently low-derivative operators, we conjecture an algorithm that gives the number of invariant operators, properly accounting for redundancies due to the equations of motion and integration by parts. Specifically, the conjectured technique can be applied whenever there is only one Lorentz invariant for a given partitioning of derivatives among the fields. At higher numbers of derivatives, equation of motion redundancies can be removed, but the increased number of Lorentz contractions spoils the subtraction of integration by parts redundancies. While restricted, this technique is sufficient to automatically recreate the complete set of invariant operators of the Standard Model effective field theory for dimensions 6 and 7 (for arbitrary numbers of flavors). At dimension 8, the algorithm does not automatically generate the complete operator set; however, it suffices for all but five classes of operators. For these remaining classes, there is a well defined procedure to manually determine the number of invariants. Assuming our method is correct, we derive a set of 535 dimension-8 N{sub f}=1 operators.
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.
Bivariate autoregressive state-space modeling of psychophysiological time series data.
Smith, Daniel M; Abtahi, Mohammadreza; Amiri, Amir Mohammad; Mankodiya, Kunal
2016-08-01
Heart rate (HR) and electrodermal activity (EDA) are often used as physiological measures of psychological arousal in various neuropsychology experiments. In this exploratory study, we analyze HR and EDA data collected from four participants, each with a history of suicidal tendencies, during a cognitive task known as the Paced Auditory Serial Addition Test (PASAT). A central aim of this investigation is to guide future research by assessing heterogeneity in the population of individuals with suicidal tendencies. Using a state-space modeling approach to time series analysis, we evaluate the effect of an exogenous input, i.e., the stimulus presentation rate which was increased systematically during the experimental task. Participants differed in several parameters characterizing the way in which psychological arousal was experienced during the task. Increasing the stimulus presentation rate was associated with an increase in EDA in participants 2 and 4. The effect on HR was positive for participant 2 and negative for participants 3 and 4. We discuss future directions in light of the heterogeneity in the population indicated by these findings.
Low-derivative operators of the Standard Model effective field theory via Hilbert series methods
International Nuclear Information System (INIS)
Lehman, Landon; Martin, Adam
2016-01-01
In this work, we explore an extension of Hilbert series techniques to count operators that include derivatives. For sufficiently low-derivative operators, we conjecture an algorithm that gives the number of invariant operators, properly accounting for redundancies due to the equations of motion and integration by parts. Specifically, the conjectured technique can be applied whenever there is only one Lorentz invariant for a given partitioning of derivatives among the fields. At higher numbers of derivatives, equation of motion redundancies can be removed, but the increased number of Lorentz contractions spoils the subtraction of integration by parts redundancies. While restricted, this technique is sufficient to automatically recreate the complete set of invariant operators of the Standard Model effective field theory for dimensions 6 and 7 (for arbitrary numbers of flavors). At dimension 8, the algorithm does not automatically generate the complete operator set; however, it suffices for all but five classes of operators. For these remaining classes, there is a well defined procedure to manually determine the number of invariants. Assuming our method is correct, we derive a set of 535 dimension-8 N_f=1 operators.
Directory of Open Access Journals (Sweden)
Horacio Fernández Castaño
2010-01-01
Full Text Available En la modelación de las volatilidades con cambios súbitos, es imperativo usar modelos que permitan describir y analizar el dinamismo de la volatilidad, ya que los inversionistas, entre otras cosas, pueden estar interesados en estimar la tasa de retorno y la volatilidad de un instrumento financiero u otros derivados, sólo durante el período de tenencia. En este artículo, que constituye la primera de dos entregas, se hace una evaluación del modelo asimétrico EGARCH que resulta ser muy útil para estudiar la dinámica del Índice General de la Bolsa de valores de Colombia (IGBC y de su volatilidad, pues inicia haciendo una breve revisión del modelo GARCH, resaltando su importancia en la modelación de series de tiempo financieras, e identificando sus debilidades en cuanto a su propiedad de simetría para las distribuciones de colas gruesas y que pueden generar errores de pronóstico. Luego se muestra la importancia del modelo EGARCH para la modelación de algunos hechos que no se logran capturar con los modelos GARCHIn the modeling of volatility with rapid changes, it is imperative to use models to describe and analyze the dynamics of volatility, as investors, among other things, may be interested in estimating the rate of return and volatility of an instrument financial or other derivatives, only during the holding period. This article contains an evaluation of asymmetric EGARCH model that proves to be very useful to study the dynamics of the General Index of the Stock Exchange of Colombia (IGBC and its volatility, since, as will be shown, the results suggest they could be more useful for capture the stylized facts of the Colombian market behavior. It is really significant to evidence the importance of asymmetric models to estimate the volatility of financial series is intended here as a model for identifying, in the best way to estimate the volatility of daily returns of the IGBC.
Energy Technology Data Exchange (ETDEWEB)
Louis, J.P.
2004-07-01
The modeling of a system to be automatized is a key step for the determination of the control laws because these laws are based on inverse models deduced from direct models. The ideal example is the DC actuator, the simpleness of which allows to directly shift from the modeling to the control law. For AC actuators, the modeling tools are based on the classical hypotheses: linearity, first harmonics, symmetry. They lead to very efficient models which allow to study the properties in dynamical and permanent regime of the most important actuators: synchronous motors, asynchronous motors, voltage inverters. Some extensions to other kind of machines which does not fulfill the classical hypotheses are also proposed: synchronous machines with non-sinusoidal field distribution and asynchronous machines in saturated regime. (J.S.)
Yuan, Y.; Meng, Y.; Chen, Y. X.; Jiang, C.; Yue, A. Z.
2018-04-01
In this study, we proposed a method to map urban encroachment onto farmland using satellite image time series (SITS) based on the hierarchical hidden Markov model (HHMM). In this method, the farmland change process is decomposed into three hierarchical levels, i.e., the land cover level, the vegetation phenology level, and the SITS level. Then a three-level HHMM is constructed to model the multi-level semantic structure of farmland change process. Once the HHMM is established, a change from farmland to built-up could be detected by inferring the underlying state sequence that is most likely to generate the input time series. The performance of the method is evaluated on MODIS time series in Beijing. Results on both simulated and real datasets demonstrate that our method improves the change detection accuracy compared with the HMM-based method.
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.
Test performance of the QSE series of 5 cm aperture quadrupole model magnets
International Nuclear Information System (INIS)
Archer, B.; Bein, D.; Cunningham, G.; DiMarco, J.; Gathright, T.; Jayakumar, J.; LaBarge, A.; Li, W.; Lambert, D.; Scott, M.
1994-01-01
A 5 cm aperture quadrupole design, the QSE series of magnets were the first to be tested in the Short Magnet and Cable Test Laboratory (SMCTL) at the SSCL. Test performance of the first two magnets of the series are presented, including quench performance, quench localization, strain gage readings, and magnetic measurements. Both magnets behaved reasonably well with no quenches below the collider operating current, four training quenches to plateau, and good training memory between thermal cycles. Future magnets in the QSE series will be used to reduce the initial training and to tune out unwanted magnetic harmonics
Test performance of the QSE series of 5 cm aperture quadrupole model magnets
International Nuclear Information System (INIS)
Archer, B.; Bein, D.; Cunningham, G.; DiMarco, J.; Gathright, T.; Jayakumar, J.; Labarge, A.; Li, W.; Lambert, D.; Scott, M.; Snitchler, G.; Zeigler, R.
1993-04-01
A 5 cm aperture quadrupole design, the QSE series of magnets were the first to be tested in the Short Magnet and Cable Test Laboratory (SMCTL) at the SSCL. Test performance of the first two magnets of the series are presented, including quench performance, quench localization, strain gage readings, and magnetic measurements.Both magnets behaved reasonably well with no quenches below the collider operating current, four training quenches to plateau, and good training memory between thermal cycles. Future magnets in the QSE series will be used to reduce the initial training and to tune out unwanted magnetic harmonics
Almog, Assaf; Garlaschelli, Diego
2014-09-01
The dynamics of complex systems, from financial markets to the brain, can be monitored in terms of multiple time series of activity of the constituent units, such as stocks or neurons, respectively. While the main focus of time series analysis is on the magnitude of temporal increments, a significant piece of information is encoded into the binary projection (i.e. the sign) of such increments. In this paper we provide further evidence of this by showing strong nonlinear relations between binary and non-binary properties of financial time series. These relations are a novel quantification of the fact that extreme price increments occur more often when most stocks move in the same direction. We then introduce an information-theoretic approach to the analysis of the binary signature of single and multiple time series. Through the definition of maximum-entropy ensembles of binary matrices and their mapping to spin models in statistical physics, we quantify the information encoded into the simplest binary properties of real time series and identify the most informative property given a set of measurements. Our formalism is able to accurately replicate, and mathematically characterize, the observed binary/non-binary relations. We also obtain a phase diagram allowing us to identify, based only on the instantaneous aggregate return of a set of multiple time series, a regime where the so-called ‘market mode’ has an optimal interpretation in terms of collective (endogenous) effects, a regime where it is parsimoniously explained by pure noise, and a regime where it can be regarded as a combination of endogenous and exogenous factors. Our approach allows us to connect spin models, simple stochastic processes, and ensembles of time series inferred from partial information.
International Nuclear Information System (INIS)
Almog, Assaf; Garlaschelli, Diego
2014-01-01
The dynamics of complex systems, from financial markets to the brain, can be monitored in terms of multiple time series of activity of the constituent units, such as stocks or neurons, respectively. While the main focus of time series analysis is on the magnitude of temporal increments, a significant piece of information is encoded into the binary projection (i.e. the sign) of such increments. In this paper we provide further evidence of this by showing strong nonlinear relations between binary and non-binary properties of financial time series. These relations are a novel quantification of the fact that extreme price increments occur more often when most stocks move in the same direction. We then introduce an information-theoretic approach to the analysis of the binary signature of single and multiple time series. Through the definition of maximum-entropy ensembles of binary matrices and their mapping to spin models in statistical physics, we quantify the information encoded into the simplest binary properties of real time series and identify the most informative property given a set of measurements. Our formalism is able to accurately replicate, and mathematically characterize, the observed binary/non-binary relations. We also obtain a phase diagram allowing us to identify, based only on the instantaneous aggregate return of a set of multiple time series, a regime where the so-called ‘market mode’ has an optimal interpretation in terms of collective (endogenous) effects, a regime where it is parsimoniously explained by pure noise, and a regime where it can be regarded as a combination of endogenous and exogenous factors. Our approach allows us to connect spin models, simple stochastic processes, and ensembles of time series inferred from partial information. (paper)
International Nuclear Information System (INIS)
Schulze-Halberg, Axel; Wang, Jie
2015-01-01
We obtain series solutions, the discrete spectrum, and supersymmetric partners for a quantum double-oscillator system. Its potential features a superposition of the one-parameter Mathews-Lakshmanan interaction and a one-parameter harmonic or inverse harmonic oscillator contribution. Furthermore, our results are transferred to a generalized Pöschl-Teller model that is isospectral to the double-oscillator system
Energy Technology Data Exchange (ETDEWEB)
Schulze-Halberg, Axel, E-mail: axgeschu@iun.edu, E-mail: xbataxel@gmail.com [Department of Mathematics and Actuarial Science and Department of Physics, Indiana University Northwest, 3400 Broadway, Gary, Indiana 46408 (United States); Wang, Jie, E-mail: wangjie@iun.edu [Department of Computer Information Systems, Indiana University Northwest, 3400 Broadway, Gary, Indiana 46408 (United States)
2015-07-15
We obtain series solutions, the discrete spectrum, and supersymmetric partners for a quantum double-oscillator system. Its potential features a superposition of the one-parameter Mathews-Lakshmanan interaction and a one-parameter harmonic or inverse harmonic oscillator contribution. Furthermore, our results are transferred to a generalized Pöschl-Teller model that is isospectral to the double-oscillator system.
2013-08-22
... passengers. The Model 777-200, -300, and -300ER series airplanes have fly-by-wire controls, fully software... lead acid batteries and nickel cadmium batteries. These special conditions provide an equivalent level... characteristics that differ significantly from those of the nickel cadmium and lead acid rechargeable batteries...
Romaguera, M.; Vaughan, R. G.; Ettema, J.; Izquierdo-Verdiguier, E.; Hecker, C. A.; van der Meer, F. D.
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
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
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
2013-12-17
... the comment (or signing the comment for an association, business, labor union, etc.). DOT's complete... design feature: The digital systems architecture for the Airbus Model A350-900 series airplanes is composed of several connected networks. This proposed network architecture is used for a diverse set of...
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.
International Nuclear Information System (INIS)
Pérez-Sánchez, D.; Thorne, M.C.
2014-01-01
In a previous paper, a mathematical model for the behaviour of 79 Se in soils and plants was described. Subsequently, a review has been published relating to the behaviour of 238 U-series radionuclides in soils and plants. Here, we bring together those two strands of work to describe a new mathematical model of the behaviour of 238 U-series radionuclides entering soils in solution and their uptake by plants. Initial studies with the model that are reported here demonstrate that it is a powerful tool for exploring the behaviour of this decay chain or subcomponents of it in soil-plant systems under different hydrological regimes. In particular, it permits studies of the degree to which secular equilibrium assumptions are appropriate when modelling this decay chain. Further studies will be undertaken and reported separately examining sensitivities of model results to input parameter values and also applying the model to sites contaminated with 238 U-series radionuclides. - Highlights: • Kinetic model of radionuclide transport in soils and uptake by plants. • Takes soil hydrology and redox conditions into account. • Applicable to the whole U-238 chain, including Rn-222, Pb-210 and Po-210. • Demonstrates intra-season and inter-season variability on timescales up to thousands of years
Liu, Siwei; Molenaar, Peter C M
2014-12-01
This article introduces iVAR, an R program for imputing missing data in multivariate time series on the basis of vector autoregressive (VAR) models. We conducted a simulation study to compare iVAR with three methods for handling missing data: listwise deletion, imputation with sample means and variances, and multiple imputation ignoring time dependency. The results showed that iVAR produces better estimates for the cross-lagged coefficients than do the other three methods. We demonstrate the use of iVAR with an empirical example of time series electrodermal activity data and discuss the advantages and limitations of the program.
International Nuclear Information System (INIS)
Fernandez Nunez, J.; Garcia Fuertes, W.; Perelomov, A.M.
2005-01-01
We reexpress the quantum Calogero-Sutherland model for the Lie algebra E 7 and the particular value of the coupling constant κ=1 by using the fundamental irreducible characters of the algebra as dynamical variables. For that, we need to develop a systematic procedure to obtain all the Clebsch-Gordan series required to perform the change of variables. We describe how the resulting quantum Hamiltonian operator can be used to compute more characters and Clebsch-Gordan series for this exceptional algebra
Sriyudthsak, Kansuporn; Iwata, Michio; Hirai, Masami Yokota; Shiraishi, Fumihide
2014-06-01
The availability of large-scale datasets has led to more effort being made to understand characteristics of metabolic reaction networks. However, because the large-scale data are semi-quantitative, and may contain biological variations and/or analytical errors, it remains a challenge to construct a mathematical model with precise parameters using only these data. The present work proposes a simple method, referred to as PENDISC (Parameter Estimation in a N on- DImensionalized S-system with Constraints), to assist the complex process of parameter estimation in the construction of a mathematical model for a given metabolic reaction system. The PENDISC method was evaluated using two simple mathematical models: a linear metabolic pathway model with inhibition and a branched metabolic pathway model with inhibition and activation. The results indicate that a smaller number of data points and rate constant parameters enhances the agreement between calculated values and time-series data of metabolite concentrations, and leads to faster convergence when the same initial estimates are used for the fitting. This method is also shown to be applicable to noisy time-series data and to unmeasurable metabolite concentrations in a network, and to have a potential to handle metabolome data of a relatively large-scale metabolic reaction system. Furthermore, it was applied to aspartate-derived amino acid biosynthesis in Arabidopsis thaliana plant. The result provides confirmation that the mathematical model constructed satisfactorily agrees with the time-series datasets of seven metabolite concentrations.
Pérez-Sánchez, D; Thorne, M C
2014-05-01
In a previous paper, a mathematical model for the behaviour of (79)Se in soils and plants was described. Subsequently, a review has been published relating to the behaviour of (238)U-series radionuclides in soils and plants. Here, we bring together those two strands of work to describe a new mathematical model of the behaviour of (238)U-series radionuclides entering soils in solution and their uptake by plants. Initial studies with the model that are reported here demonstrate that it is a powerful tool for exploring the behaviour of this decay chain or subcomponents of it in soil-plant systems under different hydrological regimes. In particular, it permits studies of the degree to which secular equilibrium assumptions are appropriate when modelling this decay chain. Further studies will be undertaken and reported separately examining sensitivities of model results to input parameter values and also applying the model to sites contaminated with (238)U-series radionuclides. Copyright © 2013 Elsevier Ltd. All rights reserved.
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.
Bendel, David; Beck, Ferdinand; Dittmer, Ulrich
2013-01-01
In the presented study climate change impacts on combined sewer overflows (CSOs) in Baden-Wuerttemberg, Southern Germany, were assessed based on continuous long-term rainfall-runoff simulations. As input data, synthetic rainfall time series were used. The applied precipitation generator NiedSim-Klima accounts for climate change effects on precipitation patterns. Time series for the past (1961-1990) and future (2041-2050) were generated for various locations. Comparing the simulated CSO activity of both periods we observe significantly higher overflow frequencies for the future. Changes in overflow volume and overflow duration depend on the type of overflow structure. Both values will increase at simple CSO structures that merely divide the flow, whereas they will decrease when the CSO structure is combined with a storage tank. However, there is a wide variation between the results of different precipitation time series (representative for different locations).
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.
Spaeder, M C; Fackler, J C
2012-04-01
Respiratory syncytial virus (RSV) is the most common cause of documented viral respiratory infections, and the leading cause of hospitalization, in young children. We performed a retrospective time-series analysis of all patients aged Forecasting models of weekly RSV incidence for the local community, inpatient paediatric hospital and paediatric intensive-care unit (PICU) were created. Ninety-five percent confidence intervals calculated around our models' 2-week forecasts were accurate to ±9·3, ±7·5 and ±1·5 cases/week for the local community, inpatient hospital and PICU, respectively. Our results suggest that time-series models may be useful tools in forecasting the burden of RSV infection at the local and institutional levels, helping communities and institutions to optimize distribution of resources based on the changing burden and severity of illness in their respective communities.
Strakova, Eva; Zikova, Alice; Vohradsky, Jiri
2014-01-01
A computational model of gene expression was applied to a novel test set of microarray time series measurements to reveal regulatory interactions between transcriptional regulators represented by 45 sigma factors and the genes expressed during germination of a prokaryote Streptomyces coelicolor. Using microarrays, the first 5.5 h of the process was recorded in 13 time points, which provided a database of gene expression time series on genome-wide scale. The computational modeling of the kinetic relations between the sigma factors, individual genes and genes clustered according to the similarity of their expression kinetics identified kinetically plausible sigma factor-controlled networks. Using genome sequence annotations, functional groups of genes that were predominantly controlled by specific sigma factors were identified. Using external binding data complementing the modeling approach, specific genes involved in the control of the studied process were identified and their function suggested.
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.
Karpušenkaitė, Aistė; Ruzgas, Tomas; Denafas, Gintaras
2018-05-01
The aim of the study was to create a hybrid forecasting method that could produce higher accuracy forecasts than previously used 'pure' time series methods. Mentioned methods were already tested with total automotive waste, hazardous automotive waste, and total medical waste generation, but demonstrated at least a 6% error rate in different cases and efforts were made to decrease it even more. Newly developed hybrid models used a random start generation method to incorporate different time-series advantages and it helped to increase the accuracy of forecasts by 3%-4% in hazardous automotive waste and total medical waste generation cases; the new model did not increase the accuracy of total automotive waste generation forecasts. Developed models' abilities to forecast short- and mid-term forecasts were tested using prediction horizon.
Modeling Taylor series approximations for prompt neutron kinetics with lab view simulations
International Nuclear Information System (INIS)
Adzri, E. P.
2012-09-01
The reactor point kinetics equations have been subjected to intense research in an effort to find simple yet accurate numerical solutions methods. The equations are very stiff numerically, meaning that there is a wide variation in the decay constants, so that using a particular time step in the numerical solution may provide sufficient accuracy for the group, but not for another. Several solutions techniques have been presented on the point kinetics equations with varying degrees of complexity. These include Power Series Solutions, CORE, PCA, Genapol and Taylor series methods. In this research, algorithms were developed based on the first and second order Taylor series expansion and simulated in LabVIEW to solve the Reactor Point Kinetics equations using block diagram nodes implemented within stacked sequences. The algorithms developed were fast,accurate and simple to code. Several reactivity insertions were used to simulate the change in neutron population with time. The LabVIEW- Taylor series solutions were compared with other solution techniques such as Power Series Solutions, CORE, PCA, Genapol and McMahon and Pierson's Taylor series approximation. The results of LabVIEW-Taylor series technique used by McMahon and Pearson The LabVIEW-implemented techniques were found to agree very well with these other methods. At 1x10 -8 s the neutron population was 1.000220 neutrons / cm 3 , at 1 x 10 -2 s it was 2.007681 neutrons / cm 3 and at 1x10 -1 s it was 2.075317 neutrons / cm 3 ; same results reported by Genapol for a fast reactor, it produced good and accurate results and compared very favorably with other methods found in the literature. Using much smaller time steps to the order or 10 -8 s commensurate with fast reactor parameters also produced very satisfactory results, indicating that the LabVIEW-based Taylor series technique is suitable for simulating the kinetics of fast reactors as well as thermal reactors. Algorithms developed that included second order terms
Forecasting Inflation Using Interest-Rate and Time-Series Models: Some International Evidence.
Hafer, R W; Hein, Scott E
1990-01-01
It has been suggested that inflation forecasts derived from short-term interest rates are as accurate as time-series forecasts. Previous analyses of this notion have focused on U.S. data, providing mixed results. In this article, the authors extend previous work by testing the hypothesis using data taken from the United States and five other countries. Using monthly Eurocurrency rates and the consumer price index for the period 1967-86, their results indicate that time-series forecasts of inf...
Energy Technology Data Exchange (ETDEWEB)
Zhang Yu, E-mail: yuzhang@xmu.edu.cn [Key Laboratory of Underwater Acoustic Communication and Marine Information Technology of the Ministry of Education, Xiamen University, Xiamen Fujian 361005 (China); Sprecher, Alicia J. [Department of Surgery, Division of Otolaryngology - Head and Neck Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792-7375 (United States); Zhao Zongxi [Key Laboratory of Underwater Acoustic Communication and Marine Information Technology of the Ministry of Education, Xiamen University, Xiamen Fujian 361005 (China); Jiang, Jack J. [Department of Surgery, Division of Otolaryngology - Head and Neck Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792-7375 (United States)
2011-09-15
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.
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.
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
Modeling of U-series Radionuclide Transport Through Soil at Pena Blanca, Chihuahua, Mexico
Pekar, K. E.; Goodell, P. C.; Walton, J. C.; Anthony, E. Y.; Ren, M.
2007-05-01
The Nopal I uranium deposit is located at Pena Blanca in Chihuahua, Mexico. Mining of high-grade uranium ore occurred in the early 1980s, with the ore stockpiled nearby. The stockpile was mostly cleared in the 1990s; however, some of the high-grade boulders have remained there, creating localized sources of radioactivity for a period of 25-30 years. This provides a unique opportunity to study radionuclide transport, because the study area did not have any uranium contamination predating the stockpile in the 1980s. One high-grade boulder was selected for study based upon its shape, location, and high activity. The presumed drip-line off of the boulder was marked, samples from the boulder surface were taken, and then the boulder was moved several feet away. Soil samples were taken from directly beneath the boulder, around the drip-line, and down slope. Eight of these samples were collected in a vertical profile directly beneath the boulder. Visible flakes of boulder material were removed from the surficial soil samples, because they would have higher concentrations of U-series radionuclides and cause the activities in the soil samples to be excessively high. The vertical sampling profile used 2-inch thicknesses for each sample. The soil samples were packaged into thin plastic containers to minimize the attenuation and to standardize sample geometry, and then they were analyzed by gamma-ray spectroscopy with a Ge(Li) detector for Th-234, Pa-234, U-234, Th-230, Ra-226, Pb-214, Bi-214, and Pb-210. The raw counts were corrected for self-attenuation and normalized using BL-5, a uranium standard from Beaverlodge, Saskatchewan. BL-5 allowed the counts obtained on the Ge(Li) to be referenced to a known concentration or activity, which was then applied to the soil unknowns for a reliable calculation of their concentrations. Gamma ray spectra of five soil samples from the vertical profile exhibit decreasing activities with increasing depth for the selected radionuclides
Modeling Magma Mixing: Evidence from U-series age dating and Numerical Simulations
Philipp, R.; Cooper, K. M.; Bergantz, G. W.
2007-12-01
-pairs, heterogeneity of particle populations is large. After one overturn event, even the "thin section scale" can contain phenocrysts that derive from the entire magmatic system. We combine these results with time scale information from U-series plagioclase age dating. Apparent crystal residence times from the most evolved and therefore least hybridized rocks for the 1846/47 and 1932 eruptions of Volcan Quizapu are about 5000 and about 3000 yrs, respectively. Based on whole rock chemistry as well as textural and crystal-chemical data, both eruptions tapped the same reservoir and therefore should record similar crystal residence times. Instead, the discordance of these two ages can be explained by magma mixing as modeled above, if some young plagioclase derived from the andesitic recharge magma which triggered the 1846/47 AD eruption got mixed into the dacite remaining in the reservoir after eruption, thus lowering the apparent crystal residence time for magma that was evacuated from the reservoir in 1932.
Directory of Open Access Journals (Sweden)
A. Karsenty
2013-01-01
Full Text Available Ultrathin body (UTB and nanoscale body (NSB SOI-MOSFET devices, sharing a similar W/L but with a channel thickness of 46 nm and lower than 5 nm, respectively, were fabricated using a selective “gate-recessed” process on the same silicon wafer. Their current-voltage characteristics measured at room temperature were found to be surprisingly different by several orders of magnitude. We analyzed this result by considering the severe mobility degradation and the influence of a huge series resistance and found that the last one seems more coherent. Then the electrical characteristics of the NSB can be analytically derived by integrating a gate voltage-dependent drain source series resistance. In this paper, the influence of the channel thickness on the series resistance is reported for the first time. This influence is integrated to the analytical model in order to describe the trends of the saturation current with the channel thickness. This modeling approach may be useful to interpret anomalous electrical behavior of other nanodevices in which series resistance and/or mobility degradation is of a great concern.
Sebastian, Tunny; Jeyaseelan, Visalakshi; Jeyaseelan, Lakshmanan; Anandan, Shalini; George, Sebastian; Bangdiwala, Shrikant I
2018-01-01
Hidden Markov models are stochastic models in which the observations are assumed to follow a mixture distribution, but the parameters of the components are governed by a Markov chain which is unobservable. The issues related to the estimation of Poisson-hidden Markov models in which the observations are coming from mixture of Poisson distributions and the parameters of the component Poisson distributions are governed by an m-state Markov chain with an unknown transition probability matrix are explained here. These methods were applied to the data on Vibrio cholerae counts reported every month for 11-year span at Christian Medical College, Vellore, India. Using Viterbi algorithm, the best estimate of the state sequence was obtained and hence the transition probability matrix. The mean passage time between the states were estimated. The 95% confidence interval for the mean passage time was estimated via Monte Carlo simulation. The three hidden states of the estimated Markov chain are labelled as 'Low', 'Moderate' and 'High' with the mean counts of 1.4, 6.6 and 20.2 and the estimated average duration of stay of 3, 3 and 4 months, respectively. Environmental risk factors were studied using Markov ordinal logistic regression analysis. No significant association was found between disease severity levels and climate components.
75 FR 35622 - Airworthiness Directives; Bombardier, Inc. Model DHC-8-400 Series Airplanes
2010-06-23
... the loss of directional control could adversely affect the aircraft during take off or landing. [[Page...-service incidents have been reported on DHC-8 Series 400 aircraft in which the nose landing gear (NLG... during take off or landing. * * * * * We are issuing this AD to require actions to correct the unsafe...
Harmonic analysis of dense time series of landsat imagery for modeling change in forest conditions
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...
Forecsting of Hydrological Time Series Data with Lag-one Markov Chain Model
Directory of Open Access Journals (Sweden)
M. A Malek
2014-06-01
the original series. This method was especially useful in the absence of long-term recorded data, a rampant phenomenon in Malaysia. Comparison of statistics in the generated synthetic rainfall data against those of the observed data revealed that reasonable levels of acceptability were achieved.
Zin, Wan Zawiah Wan; Shinyie, Wendy Ling; Jemain, Abdul Aziz
2015-02-01
In this study, two series of data for extreme rainfall events are generated based on Annual Maximum and Partial Duration Methods, derived from 102 rain-gauge stations in Peninsular from 1982-2012. To determine the optimal threshold for each station, several requirements must be satisfied and Adapted Hill estimator is employed for this purpose. A semi-parametric bootstrap is then used to estimate the mean square error (MSE) of the estimator at each threshold and the optimal threshold is selected based on the smallest MSE. The mean annual frequency is also checked to ensure that it lies in the range of one to five and the resulting data is also de-clustered to ensure independence. The two data series are then fitted to Generalized Extreme Value and Generalized Pareto distributions for annual maximum and partial duration series, respectively. The parameter estimation methods used are the Maximum Likelihood and the L-moment methods. Two goodness of fit tests are then used to evaluate the best-fitted distribution. The results showed that the Partial Duration series with Generalized Pareto distribution and Maximum Likelihood parameter estimation provides the best representation for extreme rainfall events in Peninsular Malaysia for majority of the stations studied. Based on these findings, several return values are also derived and spatial mapping are constructed to identify the distribution characteristic of extreme rainfall in Peninsular Malaysia.
A normalized model for the half-bridge series resonant converter
King, R.; Stuart, T. A.
1981-01-01
Closed-form steady-state equations are derived for the half-bridge series resonant converter with a rectified (dc) load. Normalized curves for various currents and voltages are then plotted as a function of the circuit parameters. Experimental results based on a 10-kHz converter are presented for comparison with the calculations.
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.
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.
Fang, Xin; Li, Runkui; Kan, Haidong; Bottai, Matteo; Fang, Fang; Cao, Yang
2016-08-16
To demonstrate an application of Bayesian model averaging (BMA) with generalised additive mixed models (GAMM) and provide a novel modelling technique to assess the association between inhalable coarse particles (PM10) and respiratory mortality in time-series studies. A time-series study using regional death registry between 2009 and 2010. 8 districts in a large metropolitan area in Northern China. 9559 permanent residents of the 8 districts who died of respiratory diseases between 2009 and 2010. Per cent increase in daily respiratory mortality rate (MR) per interquartile range (IQR) increase of PM10 concentration and corresponding 95% confidence interval (CI) in single-pollutant and multipollutant (including NOx, CO) models. The Bayesian model averaged GAMM (GAMM+BMA) and the optimal GAMM of PM10, multipollutants and principal components (PCs) of multipollutants showed comparable results for the effect of PM10 on daily respiratory MR, that is, one IQR increase in PM10 concentration corresponded to 1.38% vs 1.39%, 1.81% vs 1.83% and 0.87% vs 0.88% increase, respectively, in daily respiratory MR. However, GAMM+BMA gave slightly but noticeable wider CIs for the single-pollutant model (-1.09 to 4.28 vs -1.08 to 3.93) and the PCs-based model (-2.23 to 4.07 vs -2.03 vs 3.88). The CIs of the multiple-pollutant model from two methods are similar, that is, -1.12 to 4.85 versus -1.11 versus 4.83. The BMA method may represent a useful tool for modelling uncertainty in time-series studies when evaluating the effect of air pollution on fatal health outcomes. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/
A scalable database model for multiparametric time series: a volcano observatory case study
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.
DEFF Research Database (Denmark)
He, Changli; Kang, Jian; Terasvirta, Timo
In this paper we introduce an autoregressive model with seasonal dummy variables in which coefficients of seasonal dummies vary smoothly and deterministically over time. The error variance of the model is seasonally heteroskedastic and multiplicatively decomposed, the decomposition being similar ...... temperature series. More specifically, the idea is to find out in which way and by how much the monthly temperatures are varying over time during the period of more than 240 years, if they do. Misspecification tests are applied to the estimated model and the findings discussed....
Mayaud, C; Wagner, T; Benischke, R; Birk, S
2014-04-16
The Lurbach karst system (Styria, Austria) is drained by two major springs and replenished by both autogenic recharge from the karst massif itself and a sinking stream that originates in low permeable schists (allogenic recharge). Detailed data from two events recorded during a tracer experiment in 2008 demonstrate that an overflow from one of the sub-catchments to the other is activated if the discharge of the main spring exceeds a certain threshold. Time series analysis (autocorrelation and cross-correlation) was applied to examine to what extent the various available methods support the identification of the transient inter-catchment flow observed in this binary karst system. As inter-catchment flow is found to be intermittent, the evaluation was focused on single events. In order to support the interpretation of the results from the time series analysis a simplified groundwater flow model was built using MODFLOW. The groundwater model is based on the current conceptual understanding of the karst system and represents a synthetic karst aquifer for which the same methods were applied. Using the wetting capability package of MODFLOW, the model simulated an overflow similar to what has been observed during the tracer experiment. Various intensities of allogenic recharge were employed to generate synthetic discharge data for the time series analysis. In addition, geometric and hydraulic properties of the karst system were varied in several model scenarios. This approach helps to identify effects of allogenic recharge and aquifer properties in the results from the time series analysis. Comparing the results from the time series analysis of the observed data with those of the synthetic data a good agreement was found. For instance, the cross-correlograms show similar patterns with respect to time lags and maximum cross-correlation coefficients if appropriate hydraulic parameters are assigned to the groundwater model. The comparable behaviors of the real and the
Directory of Open Access Journals (Sweden)
Farshad Fathian
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
DEFF Research Database (Denmark)
Cavaliere, Giuseppe; Nielsen, Morten Ørregaard; Taylor, Robert
We consider the problem of conducting estimation and inference on the parameters of univariate heteroskedastic fractionally integrated time series models. We first extend existing results in the literature, developed for conditional sum-of squares estimators in the context of parametric fractional...... time series models driven by conditionally homoskedastic shocks, to allow for conditional and unconditional heteroskedasticity both of a quite general and unknown form. Global consistency and asymptotic normality are shown to still obtain; however, the covariance matrix of the limiting distribution...... 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...
DEFF Research Database (Denmark)
Wiechowski, Wojciech Tomasz; Lykkegaard, Jan; Bak, Claus Leth
2007-01-01
In this paper two methods of validation of transmission network harmonic models are introduced. The methods were developed as a result of the work presented in [1]. The first method allows calculating the transfer harmonic impedance between two nodes of a network. Switching a linear, series network......, as for example a transmission line. Both methods require that harmonic measurements performed at two ends of the disconnected element are precisely synchronized....... are used for calculation of the transfer harmonic impedance between the nodes. The determined transfer harmonic impedance can be used to validate a computer model of the network. The second method is an extension of the fist one. It allows switching a series element that contains a shunt branch...
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.
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.
Soni, Kirti; Parmar, Kulwinder Singh; Kapoor, Sangeeta; Kumar, Nishant
2016-05-15
A lot of studies in the literature of Aerosol Optical Depth (AOD) done by using Moderate Resolution Imaging Spectroradiometer (MODIS) derived data, but the accuracy of satellite data in comparison to ground data derived from ARrosol Robotic NETwork (AERONET) has been always questionable. So to overcome from this situation, comparative study of a comprehensive ground based and satellite data for the period of 2001-2012 is modeled. The time series model is used for the accurate prediction of AOD and statistical variability is compared to assess the performance of the model in both cases. Root mean square error (RMSE), mean absolute percentage error (MAPE), stationary R-squared, R-squared, maximum absolute percentage error (MAPE), normalized Bayesian information criterion (NBIC) and Ljung-Box methods are used to check the applicability and validity of the developed ARIMA models revealing significant precision in the model performance. It was found that, it is possible to predict the AOD by statistical modeling using time series obtained from past data of MODIS and AERONET as input data. Moreover, the result shows that MODIS data can be formed from AERONET data by adding 0.251627 ± 0.133589 and vice-versa by subtracting. From the forecast available for AODs for the next four years (2013-2017) by using the developed ARIMA model, it is concluded that the forecasted ground AOD has increased trend. Copyright © 2016 Elsevier B.V. All rights reserved.
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.
Tolstov, Georgi P
1962-01-01
Richard A. Silverman's series of translations of outstanding Russian textbooks and monographs is well-known to people in the fields of mathematics, physics, and engineering. The present book is another excellent text from this series, a valuable addition to the English-language literature on Fourier series.This edition is organized into nine well-defined chapters: Trigonometric Fourier Series, Orthogonal Systems, Convergence of Trigonometric Fourier Series, Trigonometric Series with Decreasing Coefficients, Operations on Fourier Series, Summation of Trigonometric Fourier Series, Double Fourie
Modelling and Simulation of Single-Phase Series Active Compensator for Power Quality Improvement
Verma, Arun Kumar; Mathuria, Kirti; Singh, Bhim; Bhuvaneshwari, G.
2017-10-01
A single-phase active series compensator is proposed in this work to reduce harmonic currents at the ac mains and to regulate the dc link voltage of a diode bridge rectifier (DBR) that acts as the front end converter for a voltage source inverter feeding an ac motor. This ac motor drive is used in any of the domestic, commercial or industrial appliances. Under fluctuating ac mains voltages, the dc link voltage of the DBR depicts wide variations and hence the ac motor is used at reduced rating as compared to its name-plate rating. The active series compensator proposed here provides dual functions of improving the power quality at the ac mains and regulating the dc link voltage thus averting the need for derating of the ac motor.
Directory of Open Access Journals (Sweden)
Ozge Cagcag Yolcu
2013-01-01
Full Text Available Particularly in recent years, artificial intelligence optimization techniques have been used to make fuzzy time series approaches more systematic and improve forecasting performance. Besides, some fuzzy clustering methods and artificial neural networks with different structures are used in the fuzzification of observations and determination of fuzzy relationships, respectively. In approaches considering the membership values, the membership values are determined subjectively or fuzzy outputs of the system are obtained by considering that there is a relation between membership values in identification of relation. This necessitates defuzzification step and increases the model error. In this study, membership values were obtained more systematically by using Gustafson-Kessel fuzzy clustering technique. The use of artificial neural network with single multiplicative neuron model in identification of fuzzy relation eliminated the architecture selection problem as well as the necessity for defuzzification step by constituting target values from real observations of time series. The training of artificial neural network with single multiplicative neuron model which is used for identification of fuzzy relation step is carried out with particle swarm optimization. The proposed method is implemented using various time series and the results are compared with those of previous studies to demonstrate the performance of the proposed method.
Matsunaga, Y.; Sugita, Y.
2018-06-01
A data-driven modeling scheme is proposed for conformational dynamics of biomolecules based on molecular dynamics (MD) simulations and experimental measurements. In this scheme, an initial Markov State Model (MSM) is constructed from MD simulation trajectories, and then, the MSM parameters are refined using experimental measurements through machine learning techniques. The second step can reduce the bias of MD simulation results due to inaccurate force-field parameters. Either time-series trajectories or ensemble-averaged data are available as a training data set in the scheme. Using a coarse-grained model of a dye-labeled polyproline-20, we compare the performance of machine learning estimations from the two types of training data sets. Machine learning from time-series data could provide the equilibrium populations of conformational states as well as their transition probabilities. It estimates hidden conformational states in more robust ways compared to that from ensemble-averaged data although there are limitations in estimating the transition probabilities between minor states. We discuss how to use the machine learning scheme for various experimental measurements including single-molecule time-series trajectories.
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.
2010-07-01
... Airworthiness Directives; McDonnell Douglas Corporation Model DC- 9-10 Series Airplanes, DC-9-30 Series... previously to all known U.S. owners and operators of the McDonnell Douglas Corporation airplanes identified... INFORMATION: On July 15, 2009, we issued AD 2009-15-16, which applies to all McDonnell Douglas Model DC-9-10...
2013-12-12
... Inc., Models BD-500-1A10 and BD- 500-1A11 Series Airplanes; Electronic Flight Control System: Control... Inc. Models BD-500-1A10 and BD-500-1A11 series airplanes. These airplanes will have a novel or unusual... comments, data, or views. The most helpful comments reference a specific portion of the special conditions...
Directory of Open Access Journals (Sweden)
Mahammad A. Hannan
2017-09-01
Full Text Available This study aims to develop an accurate model of a charge equalization controller (CEC that manages individual cell monitoring and equalizing by charging and discharging series-connected lithium-ion (Li-ion battery cells. In this concept, an intelligent control algorithm is developed to activate bidirectional cell switches and control direct current (DC–DC converter switches along with pulse width modulation (PWM generation. Individual models of an electric vehicle (EV-sustainable Li-ion battery, optimal power rating, a bidirectional flyback DC–DC converter, and charging and discharging controllers are integrated to develop a small-scale CEC model that can be implemented for 10 series-connected Li-ion battery cells. Results show that the charge equalization controller operates at 91% efficiency and performs well in equalizing both overdischarged and overcharged cells on time. Moreover, the outputs of the CEC model show that the desired balancing level occurs at 2% of state of charge difference and that all cells are operated within a normal range. The configuration, execution, control, power loss, cost, size, and efficiency of the developed CEC model are compared with those of existing controllers. The proposed model is proven suitable for high-tech storage systems toward the advancement of sustainable EV technologies and renewable source of applications.
2011-01-03
... radius of the rework is less than 20.0 mm (0.787 inch), before further flight, increase the radius and do... this AD. (B) If cracking is found and the radius of the rework is 20.0 mm (0.787 inch) or more, before... cracking is found and the radius of the rework is 20.0 mm (0.787 inch) or more. Paragraph (g)(3)(i)(B) of...
2011-05-11
..., 2008. (A) If cracking is found and the radius of the rework is less than 20.0 mm (0.787 inch), before... paragraph (g)(6) of this AD. (B) If cracking is found and the radius of the rework is 20.0 mm (0.787 inch... information do not specify a corrective action if cracking is found and the radius of the rework is 20.0 mm (0...
75 FR 8551 - Airworthiness Directives; Airbus Model A300, A300-600, and A310 Airplanes
2010-02-25
... 2008- 0166. An updated list of suspect check valves with P/N 2S2794-1 has now been issued by Crissair... actions. (1) At the applicable compliance time specified in Table 1 of this AD: For Crissair check valves... Affected check valve installation, identified by FIN (functional item Compliance time number) (i) Airplanes...
Wang, Zidong; Liu, Xiaohui; Liu, Yurong; Liang, Jinling; Vinciotti, Veronica
2009-01-01
In this paper, the extended Kalman filter (EKF) algorithm is applied to model the gene regulatory network from gene time series data. The gene regulatory network is considered as a nonlinear dynamic stochastic model that consists of the gene measurement equation and the gene regulation equation. After specifying the model structure, we apply the EKF algorithm for identifying both the model parameters and the actual value of gene expression levels. It is shown that the EKF algorithm is an online estimation algorithm that can identify a large number of parameters (including parameters of nonlinear functions) through iterative procedure by using a small number of observations. Four real-world gene expression data sets are employed to demonstrate the effectiveness of the EKF algorithm, and the obtained models are evaluated from the viewpoint of bioinformatics.
Dalla Valle, Nicolas; Wutzler, Thomas; Meyer, Stefanie; Potthast, Karin; Michalzik, Beate
2017-04-01
Dual-permeability type models are widely used to simulate water fluxes and solute transport in structured soils. These models contain two spatially overlapping flow domains with different parameterizations or even entirely different conceptual descriptions of flow processes. They are usually able to capture preferential flow phenomena, but a large set of parameters is needed, which are very laborious to obtain or cannot be measured at all. Therefore, model inversions are often used to derive the necessary parameters. Although these require sufficient input data themselves, they can use measurements of state variables instead, which are often easier to obtain and can be monitored by automated measurement systems. In this work we show a method to estimate soil hydraulic parameters from high frequency soil moisture time series data gathered at two different measurement depths by inversion of a simple one dimensional dual-permeability model. The model uses an advection equation based on the kinematic wave theory to describe the flow in the fracture domain and a Richards equation for the flow in the matrix domain. The soil moisture time series data were measured in mesocosms during sprinkling experiments. The inversion consists of three consecutive steps: First, the parameters of the water retention function were assessed using vertical soil moisture profiles in hydraulic equilibrium. This was done using two different exponential retention functions and the Campbell function. Second, the soil sorptivity and diffusivity functions were estimated from Boltzmann-transformed soil moisture data, which allowed the calculation of the hydraulic conductivity function. Third, the parameters governing flow in the fracture domain were determined using the whole soil moisture time series. The resulting retention functions were within the range of values predicted by pedotransfer functions apart from very dry conditions, where all retention functions predicted lower matrix potentials
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)
About the Modeling of Radio Source Time Series as Linear Splines
Karbon, Maria; Heinkelmann, Robert; Mora-Diaz, Julian; Xu, Minghui; Nilsson, Tobias; Schuh, Harald
2016-12-01
Many of the time series of radio sources observed in geodetic VLBI show variations, caused mainly by changes in source structure. However, until now it has been common practice to consider source positions as invariant, or to exclude known misbehaving sources from the datum conditions. This may lead to a degradation of the estimated parameters, as unmodeled apparent source position variations can propagate to the other parameters through the least squares adjustment. In this paper we will introduce an automated algorithm capable of parameterizing the radio source coordinates as linear splines.
The benefit of modeled ozone data for the reconstruction of a 99-year UV radiation time series
Junk, J.; Feister, U.; Helbig, A.; GöRgen, K.; Rozanov, E.; KrzyśCin, J. W.; Hoffmann, L.
2012-08-01
Solar erythemal UV radiation (UVER) is highly relevant for numerous biological processes that affect plants, animals, and human health. Nevertheless, long-term UVER records are scarce. As significant declines in the column ozone concentration were observed in the past and a recovery of the stratospheric ozone layer is anticipated by the middle of the 21st century, there is a strong interest in the temporal variation of UVERtime series. Therefore, we combined ground-based measurements of different meteorological variables with modeled ozone data sets to reconstruct time series of daily totals of UVER at the Meteorological Observatory, Potsdam, Germany. Artificial neural networks were trained with measured UVER, sunshine duration, the day of year, measured and modeled total column ozone, as well as the minimum solar zenith angle. This allows for the reconstruction of daily totals of UVERfor the period from 1901 to 1999. Additionally, analyses of the long-term variations from 1901 until 1999 of the reconstructed, new UVER data set are presented. The time series of monthly and annual totals of UVERprovide a long-term meteorological basis for epidemiological investigations in human health and occupational medicine for the region of Potsdam and Berlin. A strong benefit of our ANN-approach is the fact that it can be easily adapted to different geographical locations, as successfully tested in the framework of the COSTAction 726.
Model-free prediction of noisy chaotic time series by deep learning
Yeo, Kyongmin
2017-01-01
We present a deep neural network for a model-free prediction of a chaotic dynamical system from noisy observations. The proposed deep learning model aims to predict the conditional probability distribution of a state variable. The Long Short-Term Memory network (LSTM) is employed to model the nonlinear dynamics and a softmax layer is used to approximate a probability distribution. The LSTM model is trained by minimizing a regularized cross-entropy function. The LSTM model is validated against...
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.
Klos, A.; Bogusz, J.; Moreaux, G.
2017-12-01
This research focuses on the investigation of the deterministic and stochastic parts of the DORIS (Doppler Orbitography and Radiopositioning Integrated by Satellite) weekly coordinate time series from the IDS contribution to the ITRF2014A set of 90 stations was divided into three groups depending on when the data was 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 (these three sum up to produce the Polynomial Trend Model) and a stochastic part, all being resolved with Maximum Likelihood Estimation (MLE). We proved that the values of the parameters delivered for DORIS data are strictly correlated with the time span of the observations, meaning that the most recent data are the most reliable ones. Not only did the seasonal amplitudes decrease over the years, but also, and most importantly, the noise level and its type changed significantly. We examined five different noise models to be applied to the stochastic part of the DORIS time series: a pure white noise (WN), a pure power-law noise (PL), a combination of white and power-law noise (WNPL), an autoregressive process of first order (AR(1)) and a Generalized Gauss Markov model (GGM). From our study it arises that the PL 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 AR(1) to pure PL with few stations characterized by a positive spectral index.
Directory of Open Access Journals (Sweden)
Kuchar Leszek
2017-01-01
Full Text Available A new simulation of daily flow for Kaczawa River, south-west Poland for extra long series of generated meteorological data (comparing to previous research and selected climate change scenarios are presented. The Representative Concentration Pathways (RCPs scenarios vs. SRES are introduced for simulations. The flow simulation in the river catchment is made using MIKE SHE hydrological model while the multisite data are generated by spatial weather generator SWGEN. Simulations are done for 2040 and 2060 while the simulations for the year 2000 are used as a background. The large number of new simulated series determined by the lead time, three climate change scenarios (RCP2.6 RCP4.5 and RCP6.0, and number of generated years (1000 for each case is equal to 7000 for a single station. Finally, Pdf function for flow is presented as well probability of exceedance of maximum flow.
Lu, Yi; Haverkort, Maurits W.
2017-12-01
We present a nonperturbative, divergence-free series expansion of Green's functions using effective operators. The method is especially suited for computing correlators of complex operators as a series of correlation functions of simpler forms. We apply the method to study low-energy excitations in resonant inelastic x-ray scattering (RIXS) in doped one- and two-dimensional single-band Hubbard models. The RIXS operator is expanded into polynomials of spin, density, and current operators weighted by fundamental x-ray spectral functions. These operators couple to different polarization channels resulting in simple selection rules. The incident photon energy dependent coefficients help to pinpoint main RIXS contributions from different degrees of freedom. We show in particular that, with parameters pertaining to cuprate superconductors, local spin excitation dominates the RIXS spectral weight over a wide doping range in the cross-polarization channel.
Suhartono, Lee, Muhammad Hisyam; Prastyo, Dedy Dwi
2015-12-01
The aim of this research is to develop a calendar variation model for forecasting retail sales data with the Eid ul-Fitr effect. The proposed model is based on two methods, namely two levels ARIMAX and regression methods. Two levels ARIMAX and regression models are built by using ARIMAX for the first level and regression for the second level. Monthly men's jeans and women's trousers sales in a retail company for the period January 2002 to September 2009 are used as case study. In general, two levels of calendar variation model yields two models, namely the first model to reconstruct the sales pattern that already occurred, and the second model to forecast the effect of increasing sales due to Eid ul-Fitr that affected sales at the same and the previous months. The results show that the proposed two level calendar variation model based on ARIMAX and regression methods yields better forecast compared to the seasonal ARIMA model and Neural Networks.
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...
First and second order Markov chain models for synthetic generation of wind speed time series
International Nuclear Information System (INIS)
Shamshad, A.; Bawadi, M.A.; Wan Hussin, W.M.A.; Majid, T.A.; Sanusi, S.A.M.
2005-01-01
Hourly wind speed time series data of two meteorological stations in Malaysia have been used for stochastic generation of wind speed data using the transition matrix approach of the Markov chain process. The transition probability matrices have been formed using two different approaches: the first approach involves the use of the first order transition probability matrix of a Markov chain, and the second involves the use of a second order transition probability matrix that uses the current and preceding values to describe the next wind speed value. The algorithm to generate the wind speed time series from the transition probability matrices is described. Uniform random number generators have been used for transition between successive time states and within state wind speed values. The ability of each approach to retain the statistical properties of the generated speed is compared with the observed ones. The main statistical properties used for this purpose are mean, standard deviation, median, percentiles, Weibull distribution parameters, autocorrelations and spectral density of wind speed values. The comparison of the observed wind speed and the synthetically generated ones shows that the statistical characteristics are satisfactorily preserved
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.