LIS/OTD 2.5 DEGREE LOW RESOLUTION MONTHLY TIME SERIES (LRMTS) V2.3.2013

Data.gov (United States)

National Aeronautics and Space Administration — The product is a 2.5 deg x 2.5 deg gridded composite of total (IC+CG) lightning bulk production, expressed as a flash rate density (fl/km2/yr) Monthly Time Series....

LIS/OTD 2.5 DEGREE LOW RESOLUTION ANNUAL CLIMATOLOGY TIME SERIES V2.3.2013

Data.gov (United States)

National Aeronautics and Space Administration — The product is a 2.5 deg x 2.5 deg gridded composite of daily time-series of total (IC+CG) lightning bulk production, expressed as a flash rate density (fl/km2/day),...

Data Rods: High Speed, Time-Series Analysis of Massive Cryospheric Data Sets Using Object-Oriented Database Methods

Science.gov (United States)

Liang, Y.; Gallaher, D. W.; Grant, G.; Lv, Q.

2011-12-01

Change over time, is the central driver of climate change detection. The goal is to diagnose the underlying causes, and make projections into the future. In an effort to optimize this process we have developed the Data Rod model, an object-oriented approach that provides the ability to query grid cell changes and their relationships to neighboring grid cells through time. The time series data is organized in time-centric structures called "data rods." A single data rod can be pictured as the multi-spectral data history at one grid cell: a vertical column of data through time. This resolves the long-standing problem of managing time-series data and opens new possibilities for temporal data analysis. This structure enables rapid time- centric analysis at any grid cell across multiple sensors and satellite platforms. Collections of data rods can be spatially and temporally filtered, statistically analyzed, and aggregated for use with pattern matching algorithms. Likewise, individual image pixels can be extracted to generate multi-spectral imagery at any spatial and temporal location. The Data Rods project has created a series of prototype databases to store and analyze massive datasets containing multi-modality remote sensing data. Using object-oriented technology, this method overcomes the operational limitations of traditional relational databases. To demonstrate the speed and efficiency of time-centric analysis using the Data Rods model, we have developed a sea ice detection algorithm. This application determines the concentration of sea ice in a small spatial region across a long temporal window. If performed using traditional analytical techniques, this task would typically require extensive data downloads and spatial filtering. Using Data Rods databases, the exact spatio-temporal data set is immediately available No extraneous data is downloaded, and all selected data querying occurs transparently on the server side. Moreover, fundamental statistical

Evaluation of Electric Vehicle Charging Controllability for Provision of Time Critical Grid Services

DEFF Research Database (Denmark)

Martinenas, Sergejus; Marinelli, Mattia; Andersen, Peter Bach

2016-01-01

Replacement of conventional generation by more stochastic renewable generation sources leads to reduction of inertia and controllability in the power system. This introduces the need for more dynamic regulation services. These faster services could potentially be provided by the growing number...... of electric vehicles. EVs are a fast responding energy resource with high availability. This work evaluates and experimentally shows the limits of EV charging controllability with the focus on its suitability for providing ancillary grid services. Three different series produced EVs are tested....... The experimental testing is done by using charging current controllability of built-in AC charger to provide a primary frequency regulation service with very dynamic input frequency. The results show that most the controllability of most EVs is more than suitable for providing time critical grid services...

Optimal moving grids for time-dependent partial differential equations

Science.gov (United States)

Wathen, A. J.

1992-01-01

Various adaptive moving grid techniques for the numerical solution of time-dependent partial differential equations were proposed. The precise criterion for grid motion varies, but most techniques will attempt to give grids on which the solution of the partial differential equation can be well represented. Moving grids are investigated on which the solutions of the linear heat conduction and viscous Burgers' equation in one space dimension are optimally approximated. Precisely, the results of numerical calculations of optimal moving grids for piecewise linear finite element approximation of PDE solutions in the least-squares norm are reported.

GPS Position Time Series @ JPL

Science.gov (United States)

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

2013-01-01

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

Time series analysis time series analysis methods and applications

CERN Document Server

Rao, Tata Subba; Rao, C R

2012-01-01

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

Highly comparative time-series analysis: the empirical structure of time series and their methods.

Science.gov (United States)

Fulcher, Ben D; Little, Max A; Jones, Nick S

2013-06-06

The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines.

Time-domain analysis of planar microstrip devices using a generalized Yee-algorithm based on unstructured grids

Science.gov (United States)

Gedney, Stephen D.; Lansing, Faiza

1993-01-01

The generalized Yee-algorithm is presented for the temporal full-wave analysis of planar microstrip devices. This algorithm has the significant advantage over the traditional Yee-algorithm in that it is based on unstructured and irregular grids. The robustness of the generalized Yee-algorithm is that structures that contain curved conductors or complex three-dimensional geometries can be more accurately, and much more conveniently modeled using standard automatic grid generation techniques. This generalized Yee-algorithm is based on the the time-marching solution of the discrete form of Maxwell's equations in their integral form. To this end, the electric and magnetic fields are discretized over a dual, irregular, and unstructured grid. The primary grid is assumed to be composed of general fitted polyhedra distributed throughout the volume. The secondary grid (or dual grid) is built up of the closed polyhedra whose edges connect the centroid's of adjacent primary cells, penetrating shared faces. Faraday's law and Ampere's law are used to update the fields normal to the primary and secondary grid faces, respectively. Subsequently, a correction scheme is introduced to project the normal fields onto the grid edges. It is shown that this scheme is stable, maintains second-order accuracy, and preserves the divergenceless nature of the flux densities. Finally, for computational efficiency the algorithm is structured as a series of sparse matrix-vector multiplications. Based on this scheme, the generalized Yee-algorithm has been implemented on vector and parallel high performance computers in a highly efficient manner.

Flexible DWDM Grid Manipulation Using Four Wave Mixing-based Time Lenses

DEFF Research Database (Denmark)

Røge, Kasper Meldgaard; Guan, Pengyu; Mulvad, Hans Christian Hansen

2014-01-01

An experimental demonstration of dense wavelength-division multiplexing (DWDM) grid manipulation is carried out using two time lenses. A DWDM spectrum is compressed from a 100-GHz to a 28-GHz grid with error-free performance.......An experimental demonstration of dense wavelength-division multiplexing (DWDM) grid manipulation is carried out using two time lenses. A DWDM spectrum is compressed from a 100-GHz to a 28-GHz grid with error-free performance....

Introduction to Time Series Modeling

CERN Document Server

Kitagawa, Genshiro

2010-01-01

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

LTE delay assessment for real-time management of future smart grids

NARCIS (Netherlands)

Jorguseski, L.; Zhang, H.; Chrysalos, M.; Golinski, M.; Toh, Y.

2017-01-01

This study investigates the feasibility of using Long Term Evolution (LTE), for the real-time state estimation of the smart grids. This enables monitoring and control of future smart grids. The smart grid state estimation requires measurement reports from different nodes in the smart grid and

Grid refinement in Cartesian coordinates for groundwater flow models using the divergence theorem and Taylor's series.

Science.gov (United States)

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.

International Work-Conference on Time Series

CERN Document Server

Pomares, Héctor; Valenzuela, Olga

2017-01-01

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

Improvements of the two-dimensional FDTD method for the simulation of normal- and superconducting planar waveguides using time series analysis

International Nuclear Information System (INIS)

Hofschen, S.; Wolff, I.

1996-01-01

Time-domain simulation results of two-dimensional (2-D) planar waveguide finite-difference time-domain (FDTD) analysis are normally analyzed using Fourier transform. The introduced method of time series analysis to extract propagation and attenuation constants reduces the desired computation time drastically. Additionally, a nonequidistant discretization together with an adequate excitation technique is used to reduce the number of spatial grid points. Therefore, it is possible to reduce the number of spatial grid points. Therefore, it is possible to simulate normal- and superconducting planar waveguide structures with very thin conductors and small dimensions, as they are used in MMIC technology. The simulation results are compared with measurements and show good agreement

Improvements of the two-dimensional FDTD method for the simulation of normal- and superconducting planar waveguides using time series analysis

Energy Technology Data Exchange (ETDEWEB)

Hofschen, S.; Wolff, I. [Gerhard Mercator Univ. of Duisburg (Germany). Dept. of Electrical Engineering

1996-08-01

Time-domain simulation results of two-dimensional (2-D) planar waveguide finite-difference time-domain (FDTD) analysis are normally analyzed using Fourier transform. The introduced method of time series analysis to extract propagation and attenuation constants reduces the desired computation time drastically. Additionally, a nonequidistant discretization together with an adequate excitation technique is used to reduce the number of spatial grid points. Therefore, it is possible to reduce the number of spatial grid points. Therefore, it is possible to simulate normal- and superconducting planar waveguide structures with very thin conductors and small dimensions, as they are used in MMIC technology. The simulation results are compared with measurements and show good agreement.

A grid-voltage-sensorless resistive active power filter with series LC-filter

DEFF Research Database (Denmark)

Bai, Haofeng; Wang, Xiongfei; Blaabjerg, Frede

2017-01-01

Voltage-sensorless control has been investigated for Voltage Source Inverters (VSIs) for many years due to the reduced system cost and potentially improved system reliability. The VSI based Resistive Active Power Filters (R-APFs) are now widely used to prevent the harmonic resonance in power...... distribution network, for which the voltage sensors are needed in order to obtain the current reference. In this paper a grid-voltage-sensorless control strategy is proposed for the R-APF with series LC-filter. Unlike the traditional resistance emulation method, this proposed control method re...

A Grid-Voltage-Sensorless Resistive Active Power Filter with Series LC-Filter

DEFF Research Database (Denmark)

Bai, Haofeng; Wang, Xiongfei; Blaabjerg, Frede

2018-01-01

Voltage-sensorless control has been investigated for Voltage Source Inverters (VSIs) for many years due to the reduced system cost and potentially improved system reliability. The VSI based Resistive Active Power Filters (R-APFs) are now widely used to prevent the harmonic resonance in power...... distribution network, for which the voltage sensors are needed in order to obtain the current reference. In this paper a grid-voltage-sensorless control strategy is proposed for the R-APF with series LC-filter. Unlike the traditional resistance emulation method, this proposed control method re...

From Networks to Time Series

Science.gov (United States)

Shimada, Yutaka; Ikeguchi, Tohru; Shigehara, Takaomi

2012-10-01

In this Letter, we propose a framework to transform a complex network to a time series. The transformation from complex networks to time series is realized by the classical multidimensional scaling. Applying the transformation method to a model proposed by Watts and Strogatz [Nature (London) 393, 440 (1998)], we show that ring lattices are transformed to periodic time series, small-world networks to noisy periodic time series, and random networks to random time series. We also show that these relationships are analytically held by using the circulant-matrix theory and the perturbation theory of linear operators. The results are generalized to several high-dimensional lattices.

Duality between Time Series and Networks

Science.gov (United States)

Campanharo, Andriana S. L. O.; Sirer, M. Irmak; Malmgren, R. Dean; Ramos, Fernando M.; Amaral, Luís A. Nunes.

2011-01-01

Studying the interaction between a system's components and the temporal evolution of the system are two common ways to uncover and characterize its internal workings. Recently, several maps from a time series to a network have been proposed with the intent of using network metrics to characterize time series. Although these maps demonstrate that different time series result in networks with distinct topological properties, it remains unclear how these topological properties relate to the original time series. Here, we propose a map from a time series to a network with an approximate inverse operation, making it possible to use network statistics to characterize time series and time series statistics to characterize networks. As a proof of concept, we generate an ensemble of time series ranging from periodic to random and confirm that application of the proposed map retains much of the information encoded in the original time series (or networks) after application of the map (or its inverse). Our results suggest that network analysis can be used to distinguish different dynamic regimes in time series and, perhaps more importantly, time series analysis can provide a powerful set of tools that augment the traditional network analysis toolkit to quantify networks in new and useful ways. PMID:21858093

Long time series

DEFF Research Database (Denmark)

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

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

Global Fire Emissions Indicators, Grids: 1997-2015

Data.gov (United States)

National Aeronautics and Space Administration — The Global Fire Emissions Indicators, Grids: 1997-2015 contain a time-series of rasters from 1997 to 2015 for total area burned (hectares) and total carbon content...

Real-Time Business Intelligence in the MIRABEL Smart Grid System

DEFF Research Database (Denmark)

Fischer, Ulrike; Kaulakiene, Dalia; Khalefa, Mohamed

2012-01-01

of energy related data, and must be able to react rapidly (but intelligently) when conditions change, leading to substantial real-time business intelligence challenges. This paper discusses these challenges and presents data management solutions in the European smart grid project MIRABEL. These solutions......) data. Experimental studies show that the proposed solutions support important real-time business intelligence tasks in a smart grid system....

A Course in Time Series Analysis

CERN Document Server

Peña, Daniel; Tsay, Ruey S

2011-01-01

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

Analysis on the Operation Strategy of the 220kV External Transmission Channel for the Nujiang Power Grid after the Installation of Series Compensation

Science.gov (United States)

Liu, Xiaoxin; Feng, Peilei; Jan, Lisheng; Dai, Xiaozhong; Cai, Pengcheng

2018-01-01

In recent years, Nujiang Prefecture vigorously develop hydropower, the grid structure in the northwest of Yunnan Province is not perfect, which leads to the research and construction of the power grid lags behind the development of the hydropower. In 2015, the company in view of the nu river hydropower dilemma decided to change outside the nu river to send out a passage with series compensation device in order to improve the transmission capacity, the company to the main problems related to the system plan, but not too much in the region distribution network and detailed study. Nujiang power grid has unique structure and properties of the nujiang power grid after respectively, a whole rack respectively into two parts, namely power delivery channels, load power supply, the whole grid occurred fundamental changes, the original strategy of power network is not applicable, especially noteworthy is the main failure after network of independent operation problem, how to avoid the local series, emergency problem is more urgent, very tolerance test area power grid, this paper aims at the analysis of existing data, simulation, provide a reference for respectively after the operation for the stable operation of the power grid.

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

Science.gov (United States)

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

2013-08-01

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

Kolmogorov Space in Time Series Data

OpenAIRE

Kanjamapornkul, K.; Pinčák, R.

2016-01-01

We provide the proof that the space of time series data is a Kolmogorov space with $T_{0}$-separation axiom using the loop space of time series data. In our approach we define a cyclic coordinate of intrinsic time scale of time series data after empirical mode decomposition. A spinor field of time series data comes from the rotation of data around price and time axis by defining a new extradimension to time series data. We show that there exist hidden eight dimensions in Kolmogorov space for ...

On the Use of Running Trends as Summary Statistics for Univariate Time Series and Time Series Association

OpenAIRE

Trottini, Mario; Vigo, Isabel; Belda, Santiago

2015-01-01

Given a time series, running trends analysis (RTA) involves evaluating least squares trends over overlapping time windows of L consecutive time points, with overlap by all but one observation. This produces a new series called the “running trends series,” which is used as summary statistics of the original series for further analysis. In recent years, RTA has been widely used in climate applied research as summary statistics for time series and time series association. There is no doubt that ...

Multiple Indicator Stationary Time Series Models.

Science.gov (United States)

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…

Time Series Momentum

DEFF Research Database (Denmark)

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

2012-01-01

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

International Work-Conference on Time Series

CERN Document Server

Pomares, Héctor

2016-01-01

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

Real Time Monitor of Grid job executions

International Nuclear Information System (INIS)

Colling, D J; Martyniak, J; McGough, A S; Krenek, A; Sitera, J; Mulac, M; Dvorak, F

2010-01-01

In this paper we describe the architecture and operation of the Real Time Monitor (RTM), developed by the Grid team in the HEP group at Imperial College London. This is arguably the most popular dissemination tool within the EGEE [1] Grid. Having been used, on many occasions including GridFest and LHC inauguration events held at CERN in October 2008. The RTM gathers information from EGEE sites hosting Logging and Bookkeeping (LB) services. Information is cached locally at a dedicated server at Imperial College London and made available for clients to use in near real time. The system consists of three main components: the RTM server, enquirer and an apache Web Server which is queried by clients. The RTM server queries the LB servers at fixed time intervals, collecting job related information and storing this in a local database. Job related data includes not only job state (i.e. Scheduled, Waiting, Running or Done) along with timing information but also other attributes such as Virtual Organization and Computing Element (CE) queue - if known. The job data stored in the RTM database is read by the enquirer every minute and converted to an XML format which is stored on a Web Server. This decouples the RTM server database from the client removing the bottleneck problem caused by many clients simultaneously accessing the database. This information can be visualized through either a 2D or 3D Java based client with live job data either being overlaid on to a 2 dimensional map of the world or rendered in 3 dimensions over a globe map using OpenGL.

Uncertainties in global radiation time series forecasting using machine learning: The multilayer perceptron case

International Nuclear Information System (INIS)

Voyant, Cyril; Notton, Gilles; Darras, Christophe; Fouilloy, Alexis; Motte, Fabrice

2017-01-01

As global solar radiation forecasting is a very important challenge, several methods are devoted to this goal with different levels of accuracy and confidence. In this study we propose to better understand how the uncertainty is propagated in the context of global radiation time series forecasting using machine learning. Indeed we propose to decompose the error considering four kinds of uncertainties: the error due to the measurement, the variability of time series, the machine learning uncertainty and the error related to the horizon. All these components of the error allow to determinate a global uncertainty generating prediction bands related to the prediction efficiency. We also have defined a reliability index which could be very interesting for the grid manager in order to estimate the validity of predictions. We have experimented this method on a multilayer perceptron which is a popular machine learning technique. We have shown that the global error and its components are essential to quantify in order to estimate the reliability of the model outputs. The described method has been successfully applied to four meteorological stations in Mediterranean area. - Highlights: • Solar irradiation predictions require confidence bands. • There are a lot of kinds of uncertainties to take into account in order to propose prediction bands. • the ranking of different kinds of uncertainties is essential to propose an operational tool for the grid managers.

Net load forecasting for high renewable energy penetration grids

International Nuclear Information System (INIS)

Kaur, Amanpreet; Nonnenmacher, Lukas; Coimbra, Carlos F.M.

2016-01-01

We discuss methods for net load forecasting and their significance for operation and management of power grids with high renewable energy penetration. Net load forecasting is an enabling technology for the integration of microgrid fleets with the macrogrid. Net load represents the load that is traded between the grids (microgrid and utility grid). It is important for resource allocation and electricity market participation at the point of common coupling between the interconnected grids. We compare two inherently different approaches: additive and integrated net load forecast models. The proposed methodologies are validated on a microgrid with 33% annual renewable energy (solar) penetration. A heuristics based solar forecasting technique is proposed, achieving skill of 24.20%. The integrated solar and load forecasting model outperforms the additive model by 10.69% and the uncertainty range for the additive model is larger than the integrated model by 2.2%. Thus, for grid applications an integrated forecast model is recommended. We find that the net load forecast errors and the solar forecasting errors are cointegrated with a common stochastic drift. This is useful for future planning and modeling because the solar energy time-series allows to infer important features of the net load time-series, such as expected variability and uncertainty. - Highlights: • Net load forecasting methods for grids with renewable energy generation are discussed. • Integrated solar and load forecasting outperforms the additive model by 10.69%. • Net load forecasting reduces the uncertainty between the interconnected grids.

Stochastic models for time series

CERN Document Server

Doukhan, Paul

2018-01-01

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

Graphical Data Analysis on the Circle: Wrap-Around Time Series Plots for (Interrupted) Time Series Designs.

Science.gov (United States)

Rodgers, Joseph Lee; Beasley, William Howard; Schuelke, Matthew

2014-01-01

Many data structures, particularly time series data, are naturally seasonal, cyclical, or otherwise circular. Past graphical methods for time series have focused on linear plots. In this article, we move graphical analysis onto the circle. We focus on 2 particular methods, one old and one new. Rose diagrams are circular histograms and can be produced in several different forms using the RRose software system. In addition, we propose, develop, illustrate, and provide software support for a new circular graphical method, called Wrap-Around Time Series Plots (WATS Plots), which is a graphical method useful to support time series analyses in general but in particular in relation to interrupted time series designs. We illustrate the use of WATS Plots with an interrupted time series design evaluating the effect of the Oklahoma City bombing on birthrates in Oklahoma County during the 10 years surrounding the bombing of the Murrah Building in Oklahoma City. We compare WATS Plots with linear time series representations and overlay them with smoothing and error bands. Each method is shown to have advantages in relation to the other; in our example, the WATS Plots more clearly show the existence and effect size of the fertility differential.

Monofractal Characteristics of Traffic in Wireless Sensor Networks for Smart Grid

Directory of Open Access Journals (Sweden)

Ming-Yue Zhai

2014-05-01

Full Text Available Wireless sensor networks (WSNs have extensive applications in the smart grid in recently years. The time series in WSNs in smart grid should be discussed by analysis method which is non-linear. The method of monofractal is used here. Self-similar Hurst parameter calculated by the algorithm of Rescaled Range Analysis (R/S and fractal dimension are obtained, which confirm that the time sequences in WSNs for smart grid belong to the fractal sets and have the characteristic of self- similarity.

Time Series with Long Memory

OpenAIRE

西埜, 晴久

2004-01-01

The paper investigates an application of long-memory processes to economic time series. We show properties of long-memory processes, which are motivated to model a long-memory phenomenon in economic time series. An FARIMA model is described as an example of long-memory model in statistical terms. The paper explains basic limit theorems and estimation methods for long-memory processes in order to apply long-memory models to economic time series.

Visibility Graph Based Time Series Analysis.

Science.gov (United States)

Stephen, Mutua; Gu, Changgui; Yang, Huijie

2015-01-01

Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it's microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq) and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks.

Visibility Graph Based Time Series Analysis.

Directory of Open Access Journals (Sweden)

Mutua Stephen

Full Text Available Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it's microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks.

Network structure of multivariate time series.

Science.gov (United States)

Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

2015-10-21

Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

What marketing scholars should know about time series analysis : time series applications in marketing

NARCIS (Netherlands)

Horváth, Csilla; Kornelis, Marcel; Leeflang, Peter S.H.

2002-01-01

In this review, we give a comprehensive summary of time series techniques in marketing, and discuss a variety of time series analysis (TSA) techniques and models. We classify them in the sets (i) univariate TSA, (ii) multivariate TSA, and (iii) multiple TSA. We provide relevant marketing

Gridding artifacts on ENVISAT/MERIS temporal series

NARCIS (Netherlands)

Gómez Chova, L.; Zurita-Milla, R.; Alonso, L.; Guanter, L.; Amoros-Lopez, J.; Camps-Valls, G.; Moreno, J.; Lacoste-Francis, H.

2010-01-01

Earth observation satellites are a valuable source of data that can be used to better understand the Earth system dynamics. However, analysis of satellite image time series requires an accurate spatial co-registration so that the multi-temporal pixel entities offer a true temporal view of the study

DanGrid. Report from working group 24. Roadmap for Smart Grid in Denmark with emphasis on the transmission companies' role; DanGrid. Delrapport. Arbejdsgruppe 24. Roadmap for Smart Grid i Danmark med saerlig vaegt pae netselskabernes rolle

Energy Technology Data Exchange (ETDEWEB)

Lomholt Finnemann, K. [DONG Energy, Virum (Denmark); Soerensen, Per [TRE-FOR, Kolding (Denmark); Larsen, Jim [EnergiMidt, Silkeborg (Denmark); Balasiu, A. [Siemens, Ballerup (Denmark); Holmberg Rasmussen, L. [Nordjysk Elhandel, Aalborg (Denmark); Moeller Joergensen, J. [Energinet.dk, Fredericia (Denmark); Norsk Jensen, A.; Nejsum, T.; Andersen, Kim [Dansk Energi, Frederiksberg (Denmark)

2012-09-15

The present report identifies a number of recommendations where specifically grid companies, but also authorities, customers and market players must initiate actions and activities to realize Smart Grid in Denmark by 2020. The report forms the basis for each network operator to implement Smart Grid, and is divided such that: 1 the management can create an overview of the steps to be taken in connection with the implementation, and 2. the technical department can develop a series of concrete actions to be implemented in the Smart Grid. The measures are divided into four groups: 1 - Establishment of business case and strategy: Efforts that support that each network operator prepares a business case and define a strategy for the creation of a Smart Grid. 2 - New technology in the grid: Measures relating to the importation of new technology in the grid, for example establishment of technology solutions for automating and monitoring the load on the network, and installing additional measurements at strategic points in the distribution network. 3 - Enabling customers' demand response: Initiatives that support that the grid company actively involves end users through price signals or by entering into agreements to regulate or move power consumption to a time of free capacity in the grid. 4 - Other activities: Measures which are horizontal and support that network companies develop a Smart Grid. New economic regulation of network companies is required to support the introduction of new technology in the grid, and at the same time crucial to enable customers' demand response. IT and data security must be established and customer engagement as active consumers of electricity should be strengthened. (LN)

Data mining in time series databases

CERN Document Server

Kandel, Abraham; Bunke, Horst

2004-01-01

Adding the time dimension to real-world databases produces Time SeriesDatabases (TSDB) and introduces new aspects and difficulties to datamining and knowledge discovery. This book covers the state-of-the-artmethodology for mining time series databases. The novel data miningmethods presented in the book include techniques for efficientsegmentation, indexing, and classification of noisy and dynamic timeseries. A graph-based method for anomaly detection in time series isdescribed and the book also studies the implications of a novel andpotentially useful representation of time series as strings. Theproblem of detecting changes in data mining models that are inducedfrom temporal databases is additionally discussed.

Models for dependent time series

CERN Document Server

Tunnicliffe Wilson, Granville; Haywood, John

2015-01-01

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

Visual time series analysis

DEFF Research Database (Denmark)

Fischer, Paul; Hilbert, Astrid

2012-01-01

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

A Review of Subsequence Time Series Clustering

Directory of Open Access Journals (Sweden)

Seyedjamal Zolhavarieh

2014-01-01

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

A review of subsequence time series clustering.

Science.gov (United States)

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

2014-01-01

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

A Review of Subsequence Time Series Clustering

Science.gov (United States)

Teh, Ying Wah

2014-01-01

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies. PMID:25140332

Analysis of Heavy-Tailed Time Series

DEFF Research Database (Denmark)

Xie, Xiaolei

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

Real-Time Market Concept Architecture for EcoGrid EU—A Prototype for European Smart Grids

DEFF Research Database (Denmark)

Ding, Yi; Pineda Morente, Salvador; Nyeng, Preben

2014-01-01

Industrialized countries are increasingly committed to move towards a low carbon generating mix by increasing the penetration of renewable generation. Additionally, the Development in communication technologies will allow small end-consumers and small-scale distributed energy resources (DER......) to participate in electricity markets. Current electricity markets need to be tailored to incorporate these changes regarding how electricity will be generated and consumed in the future. The EcoGrid EU is a large-scale EU-funded project, which establishes the first prototype of the future European intelligent...... grids. In this project, small-scale DERs and small end-consumers can actively participate in a new real-time electricity market by responding to 5-min real time electricity prices. In this way, the market operator will also obtain additional balancing power to cancel out the production variation...

Adaptive time-variant models for fuzzy-time-series forecasting.

Science.gov (United States)

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.

Time Series Analysis and Forecasting by Example

CERN Document Server

Bisgaard, Soren

2011-01-01

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

Time series with tailored nonlinearities

Science.gov (United States)

Räth, C.; Laut, I.

2015-10-01

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

Clustering of financial time series

Science.gov (United States)

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

2013-05-01

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

A Systematic Multi-Time Scale Solution for Regional Power Grid Operation

Science.gov (United States)

Zhu, W. J.; Liu, Z. G.; Cheng, T.; Hu, B. Q.; Liu, X. Z.; Zhou, Y. F.

2017-10-01

Many aspects need to be taken into consideration in a regional grid while making schedule plans. In this paper, a systematic multi-time scale solution for regional power grid operation considering large scale renewable energy integration and Ultra High Voltage (UHV) power transmission is proposed. In the time scale aspect, we discuss the problem from month, week, day-ahead, within-day to day-behind, and the system also contains multiple generator types including thermal units, hydro-plants, wind turbines and pumped storage stations. The 9 subsystems of the scheduling system are described, and their functions and relationships are elaborated. The proposed system has been constructed in a provincial power grid in Central China, and the operation results further verified the effectiveness of the system.

Data Mining Smart Energy Time Series

Directory of Open Access Journals (Sweden)

Janina POPEANGA

2015-07-01

Full Text Available With the advent of smart metering technology the amount of energy data will increase significantly and utilities industry will have to face another big challenge - to find relationships within time-series data and even more - to analyze such huge numbers of time series to find useful patterns and trends with fast or even real-time response. This study makes a small review of the literature in the field, trying to demonstrate how essential is the application of data mining techniques in the time series to make the best use of this large quantity of data, despite all the difficulties. Also, the most important Time Series Data Mining techniques are presented, highlighting their applicability in the energy domain.

Predicting chaotic time series

International Nuclear Information System (INIS)

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

1987-01-01

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

Measuring multiscaling in financial time-series

International Nuclear Information System (INIS)

Buonocore, R.J.; Aste, T.; Di Matteo, T.

2016-01-01

We discuss the origin of multiscaling in financial time-series and investigate how to best quantify it. Our methodology consists in separating the different sources of measured multifractality by analyzing the multi/uni-scaling behavior of synthetic time-series with known properties. We use the results from the synthetic time-series to interpret the measure of multifractality of real log-returns time-series. The main finding is that the aggregation horizon of the returns can introduce a strong bias effect on the measure of multifractality. This effect can become especially important when returns distributions have power law tails with exponents in the range (2, 5). We discuss the right aggregation horizon to mitigate this bias.

Time averaging, ageing and delay analysis of financial time series

Science.gov (United States)

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

2017-06-01

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

Applied time series analysis

CERN Document Server

Woodward, Wayne A; Elliott, Alan C

2011-01-01

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

Grid Technologies: scientific and industrial prospects

CERN Multimedia

2002-01-01

On Friday 27th, 17:00-21:00, CERN will for the first time be host to the popular 'First Tuesday Geneva' events for entrepreneurs, investors and all those interested in how new technologies will impact industry. Organised by the non-profit group Rezonance, these evening events typically attract over 300 persons, and combine a series of short presentations on a hot topic with an informal networking session. The topic for this 'First Tuesday@CERN' is Grid Technologies. Over the last year, the concept of a Grid of geographically distributed computers providing huge computing resources 'on tap' to companies and institutions has led to a great deal of interest and activity from major computer hardware and software companies. The session is hosted by the CERN openlab for DataGrid applications, a recently established industrial partnership on Grid technologies, and will profile both CERN's activities in this emerging field and those of some key industrial players. Speakers include: Hans Hoffmann: CERN, The LHC a...

Entropic Analysis of Electromyography Time Series

Science.gov (United States)

Kaufman, Miron; Sung, Paul

2005-03-01

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

Quantifying memory in complex physiological time-series.

Science.gov (United States)

Shirazi, Amir H; Raoufy, Mohammad R; Ebadi, Haleh; De Rui, Michele; Schiff, Sami; Mazloom, Roham; Hajizadeh, Sohrab; Gharibzadeh, Shahriar; Dehpour, Ahmad R; Amodio, Piero; Jafari, G Reza; Montagnese, Sara; Mani, Ali R

2013-01-01

In a time-series, memory is a statistical feature that lasts for a period of time and distinguishes the time-series from a random, or memory-less, process. In the present study, the concept of "memory length" was used to define the time period, or scale over which rare events within a physiological time-series do not appear randomly. The method is based on inverse statistical analysis and provides empiric evidence that rare fluctuations in cardio-respiratory time-series are 'forgotten' quickly in healthy subjects while the memory for such events is significantly prolonged in pathological conditions such as asthma (respiratory time-series) and liver cirrhosis (heart-beat time-series). The memory length was significantly higher in patients with uncontrolled asthma compared to healthy volunteers. Likewise, it was significantly higher in patients with decompensated cirrhosis compared to those with compensated cirrhosis and healthy volunteers. We also observed that the cardio-respiratory system has simple low order dynamics and short memory around its average, and high order dynamics around rare fluctuations.

Effective Feature Preprocessing for Time Series Forecasting

DEFF Research Database (Denmark)

Zhao, Junhua; Dong, Zhaoyang; Xu, Zhao

2006-01-01

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

Relating interesting quantitative time series patterns with text events and text features

Science.gov (United States)

Wanner, Franz; Schreck, Tobias; Jentner, Wolfgang; Sharalieva, Lyubka; Keim, Daniel A.

2013-12-01

application domains such as data analysis of smart grids, cyber physical systems or the security of critical infrastructure, where the data consists of a combination of quantitative and textual time series data.

Diagnosis of inconsistencies in multi-year gridded precipitation data over mountainous areas and related impacts on hydrologic simulations

Science.gov (United States)

Mizukami, N.; Smith, M. B.

2010-12-01

It is common for the error characteristics of long-term precipitation data to change over time due to various factors such as gauge relocation and changes in data processing methods. The temporal consistency of precipitation data error characteristics is as important as data accuracy itself for hydrologic model calibration and subsequent use of the calibrated model for streamflow prediction. In mountainous areas, the generation of precipitation grids relies on sparse gage networks, the makeup of which often varies over time. This causes a change in error characteristics of the long-term precipitation data record. We will discuss the diagnostic analysis of the consistency of gridded precipitation time series and illustrate the adverse effect of inconsistent precipitation data on a hydrologic model simulation. We used hourly 4 km gridded precipitation time series over a mountainous basin in the Sierra Nevada Mountains of California from October 1988 through September 2006. The basin is part of the broader study area that served as the focus of the second phase of the Distributed Model Intercomparison Project (DMIP-2), organized by the U.S. National Weather Service (NWS) of the National Oceanographic and Atmospheric Administration (NOAA). To check the consistency of the gridded precipitation time series, double mass analysis was performed using single pixel and basin mean areal precipitation (MAP) values derived from gridded DMIP-2 and Parameter-Elevation Regressions on Independent Slopes Model (PRISM) precipitation data. The analysis leads to the conclusion that over the entire study time period, a clear change in error characteristics in the DMIP-2 data occurred in the beginning of 2003. This matches the timing of one of the major gage network changes. The inconsistency of two MAP time series computed from the gridded precipitation fields over two elevation zones was corrected by adjusting hourly values based on the double mass analysis. We show that model

Grid and Data Analyzing and Security

Directory of Open Access Journals (Sweden)

Fatemeh SHOKRI

2012-12-01

Full Text Available This paper examines the importance of secure structures in the process of analyzing and distributing information with aid of Grid-based technologies. The advent of distributed network has provided many practical opportunities for detecting and recording the time of events, and made efforts to identify the events and solve problems of storing information such as being up-to-date and documented. In this regard, the data distribution systems in a network environment should be accurate. As a consequence, a series of continuous and updated data must be at hand. In this case, Grid is the best answer to use data and resource of organizations by common processing.

Trends in life science grid: from computing grid to knowledge grid

Directory of Open Access Journals (Sweden)

Konagaya Akihiko

2006-12-01

Full Text Available Abstract Background Grid computing has great potential to become a standard cyberinfrastructure for life sciences which often require high-performance computing and large data handling which exceeds the computing capacity of a single institution. Results This survey reviews the latest grid technologies from the viewpoints of computing grid, data grid and knowledge grid. Computing grid technologies have been matured enough to solve high-throughput real-world life scientific problems. Data grid technologies are strong candidates for realizing "resourceome" for bioinformatics. Knowledge grids should be designed not only from sharing explicit knowledge on computers but also from community formulation for sharing tacit knowledge among a community. Conclusion Extending the concept of grid from computing grid to knowledge grid, it is possible to make use of a grid as not only sharable computing resources, but also as time and place in which people work together, create knowledge, and share knowledge and experiences in a community.

Statistical criteria for characterizing irradiance time series.

Energy Technology Data Exchange (ETDEWEB)

Stein, Joshua S.; Ellis, Abraham; Hansen, Clifford W.

2010-10-01

We propose and examine several statistical criteria for characterizing time series of solar irradiance. Time series of irradiance are used in analyses that seek to quantify the performance of photovoltaic (PV) power systems over time. Time series of irradiance are either measured or are simulated using models. Simulations of irradiance are often calibrated to or generated from statistics for observed irradiance and simulations are validated by comparing the simulation output to the observed irradiance. Criteria used in this comparison should derive from the context of the analyses in which the simulated irradiance is to be used. We examine three statistics that characterize time series and their use as criteria for comparing time series. We demonstrate these statistics using observed irradiance data recorded in August 2007 in Las Vegas, Nevada, and in June 2009 in Albuquerque, New Mexico.

Analysis of turbine-grid interaction of grid-connected wind turbine using HHT

Science.gov (United States)

Chen, A.; Wu, W.; Miao, J.; Xie, D.

2018-05-01

This paper processes the output power of the grid-connected wind turbine with the denoising and extracting method based on Hilbert Huang transform (HHT) to discuss the turbine-grid interaction. At first, the detailed Empirical Mode Decomposition (EMD) and the Hilbert Transform (HT) are introduced. Then, on the premise of decomposing the output power of the grid-connected wind turbine into a series of Intrinsic Mode Functions (IMFs), energy ratio and power volatility are calculated to detect the unessential components. Meanwhile, combined with vibration function of turbine-grid interaction, data fitting of instantaneous amplitude and phase of each IMF is implemented to extract characteristic parameters of different interactions. Finally, utilizing measured data of actual parallel-operated wind turbines in China, this work accurately obtains the characteristic parameters of turbine-grid interaction of grid-connected wind turbine.

Homogenising time series: beliefs, dogmas and facts

Science.gov (United States)

Domonkos, P.

2011-06-01

In the recent decades various homogenisation methods have been developed, but the real effects of their application on time series are still not known sufficiently. The ongoing COST action HOME (COST ES0601) is devoted to reveal the real impacts of homogenisation methods more detailed and with higher confidence than earlier. As a part of the COST activity, a benchmark dataset was built whose characteristics approach well the characteristics of real networks of observed time series. This dataset offers much better opportunity than ever before to test the wide variety of homogenisation methods, and analyse the real effects of selected theoretical recommendations. Empirical results show that real observed time series usually include several inhomogeneities of different sizes. Small inhomogeneities often have similar statistical characteristics than natural changes caused by climatic variability, thus the pure application of the classic theory that change-points of observed time series can be found and corrected one-by-one is impossible. However, after homogenisation the linear trends, seasonal changes and long-term fluctuations of time series are usually much closer to the reality than in raw time series. Some problems around detecting multiple structures of inhomogeneities, as well as that of time series comparisons within homogenisation procedures are discussed briefly in the study.

Ocean time-series near Bermuda: Hydrostation S and the US JGOFS Bermuda Atlantic time-series study

Science.gov (United States)

Michaels, Anthony F.; Knap, Anthony H.

1992-01-01

Bermuda is the site of two ocean time-series programs. At Hydrostation S, the ongoing biweekly profiles of temperature, salinity and oxygen now span 37 years. This is one of the longest open-ocean time-series data sets and provides a view of decadal scale variability in ocean processes. In 1988, the U.S. JGOFS Bermuda Atlantic Time-series Study began a wide range of measurements at a frequency of 14-18 cruises each year to understand temporal variability in ocean biogeochemistry. On each cruise, the data range from chemical analyses of discrete water samples to data from electronic packages of hydrographic and optics sensors. In addition, a range of biological and geochemical rate measurements are conducted that integrate over time-periods of minutes to days. This sampling strategy yields a reasonable resolution of the major seasonal patterns and of decadal scale variability. The Sargasso Sea also has a variety of episodic production events on scales of days to weeks and these are only poorly resolved. In addition, there is a substantial amount of mesoscale variability in this region and some of the perceived temporal patterns are caused by the intersection of the biweekly sampling with the natural spatial variability. In the Bermuda time-series programs, we have added a series of additional cruises to begin to assess these other sources of variation and their impacts on the interpretation of the main time-series record. However, the adequate resolution of higher frequency temporal patterns will probably require the introduction of new sampling strategies and some emerging technologies such as biogeochemical moorings and autonomous underwater vehicles.

Advancing Smart Grid Interoperability and Implementing NIST's Interoperability Roadmap

Energy Technology Data Exchange (ETDEWEB)

Basso,T.; DeBlasio, R.

2010-04-01

The IEEE American National Standards project P2030TM addressing smart grid interoperability and the IEEE 1547 series of standards addressing distributed resources interconnection with the grid have been identified in priority action plans in the Report to NIST on the Smart Grid Interoperability Standards Roadmap. This paper presents the status of the IEEE P2030 development, the IEEE 1547 series of standards publications and drafts, and provides insight on systems integration and grid infrastructure. The P2030 and 1547 series of standards are sponsored by IEEE Standards Coordinating Committee 21.

Smart-Grid Backbone Network Real-Time Delay Reduction via Integer Programming.

Science.gov (United States)

Pagadrai, Sasikanth; Yilmaz, Muhittin; Valluri, Pratyush

2016-08-01

This research investigates an optimal delay-based virtual topology design using integer linear programming (ILP), which is applied to the current backbone networks such as smart-grid real-time communication systems. A network traffic matrix is applied and the corresponding virtual topology problem is solved using the ILP formulations that include a network delay-dependent objective function and lightpath routing, wavelength assignment, wavelength continuity, flow routing, and traffic loss constraints. The proposed optimization approach provides an efficient deterministic integration of intelligent sensing and decision making, and network learning features for superior smart grid operations by adaptively responding the time-varying network traffic data as well as operational constraints to maintain optimal virtual topologies. A representative optical backbone network has been utilized to demonstrate the proposed optimization framework whose simulation results indicate that superior smart-grid network performance can be achieved using commercial networks and integer programming.

GRIDCC: A Real-Time Grid Workflow System with QoS

Directory of Open Access Journals (Sweden)

A. Stephen McGough

2007-01-01

Full Text Available The over-arching aim of Grid computing is to move computational resources from individual institutions where they can only be used for in-house work, to a more open vision of vast online ubiquitous `virtual computational' resources which support individuals and collaborative projects. A major step towards realizing this vision is the provision of instrumentation – such as telescopes, accelerators or electrical power stations – as Grid resources, and the tools to manage these resources online. The GRIDCC project attempts to satisfy these requirements by providing the following four co-dependent components; a flexible wrapper for publishing instruments as Grid resources; workflow support for the orchestration of multiple Grid resources in a timely manner; the machinery to make reservation agreements on Grid resources; and the facility to satisfy quality of service (QoS requirements on elements within workflows. In this paper we detail the set of services developed as part of the GRIDCC project to provide the last three of these components. We provide a detailed architecture for these services along with experimental results from load testing experiments. These services are currently deployed as a test-bed at a number of institutions across Europe, and are poised to provide a 'virtual lab' to production level applications.

Multivariate Time Series Decomposition into Oscillation Components.

Science.gov (United States)

Matsuda, Takeru; Komaki, Fumiyasu

2017-08-01

Many time series are considered to be a superposition of several oscillation components. We have proposed a method for decomposing univariate time series into oscillation components and estimating their phases (Matsuda & Komaki, 2017 ). In this study, we extend that method to multivariate time series. We assume that several oscillators underlie the given multivariate time series and that each variable corresponds to a superposition of the projections of the oscillators. Thus, the oscillators superpose on each variable with amplitude and phase modulation. Based on this idea, we develop gaussian linear state-space models and use them to decompose the given multivariate time series. The model parameters are estimated from data using the empirical Bayes method, and the number of oscillators is determined using the Akaike information criterion. Therefore, the proposed method extracts underlying oscillators in a data-driven manner and enables investigation of phase dynamics in a given multivariate time series. Numerical results show the effectiveness of the proposed method. From monthly mean north-south sunspot number data, the proposed method reveals an interesting phase relationship.

Optimal Design of DC Fast-Charging Stations for EVs in Low Voltage Grids

DEFF Research Database (Denmark)

Gjelaj, Marjan; Træholt, Chresten; Hashemi Toghroljerdi, Seyedmostafa

2017-01-01

DC Fast Charging Station (DCFCS) is essential for widespread use of Electric Vehicle (EVs). It can recharge EVs in direct current in a short period of time. In recent years, the increasing penetration of EVs and their charging systems are going through a series of changes. This paper addresses...... on the power grid through the application of electrical storage systems within the DC fast charging stations. The proposed solution decreases the charging time and the impact on the low voltage (LV) grid significantly. The charger can be used as a multifunctional grid-utility such as congestion management...

Forecasting Enrollments with Fuzzy Time Series.

Science.gov (United States)

Song, Qiang; Chissom, Brad S.

The concept of fuzzy time series is introduced and used to forecast the enrollment of a university. Fuzzy time series, an aspect of fuzzy set theory, forecasts enrollment using a first-order time-invariant model. To evaluate the model, the conventional linear regression technique is applied and the predicted values obtained are compared to the…

Forecasting Cryptocurrencies Financial Time Series

DEFF Research Database (Denmark)

Catania, Leopoldo; Grassi, Stefano; Ravazzolo, Francesco

2018-01-01

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

Forecasting Cryptocurrencies Financial Time Series

OpenAIRE

Catania, Leopoldo; Grassi, Stefano; Ravazzolo, Francesco

2018-01-01

This paper studies the predictability of cryptocurrencies time series. We compare several alternative univariate and multivariate models in point and density forecasting of four of the most capitalized series: Bitcoin, Litecoin, Ripple and Ethereum. We apply a set of crypto–predictors and rely on Dynamic Model Averaging to combine a large set of univariate Dynamic Linear Models and several multivariate Vector Autoregressive models with different forms of time variation. We find statistical si...

Time series modeling, computation, and inference

CERN Document Server

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

Time Series Analysis Forecasting and Control

CERN Document Server

Box, George E P; Reinsel, Gregory C

2011-01-01

A modernized new edition of one of the most trusted books on time series analysis. Since publication of the first edition in 1970, Time Series Analysis has served as one of the most influential and prominent works on the subject. This new edition maintains its balanced presentation of the tools for modeling and analyzing time series and also introduces the latest developments that have occurred n the field over the past decade through applications from areas such as business, finance, and engineering. The Fourth Edition provides a clearly written exploration of the key methods for building, cl

Costationarity of Locally Stationary Time Series Using costat

OpenAIRE

Cardinali, Alessandro; Nason, Guy P.

2013-01-01

This article describes the R package costat. This package enables a user to (i) perform a test for time series stationarity; (ii) compute and plot time-localized autocovariances, and (iii) to determine and explore any costationary relationship between two locally stationary time series. Two locally stationary time series are said to be costationary if there exists two time-varying combination functions such that the linear combination of the two series with the functions produces another time...

Detecting nonlinear structure in time series

International Nuclear Information System (INIS)

Theiler, J.

1991-01-01

We describe an approach for evaluating the statistical significance of evidence for nonlinearity in a time series. The formal application of our method requires the careful statement of a null hypothesis which characterizes a candidate linear process, the generation of an ensemble of ''surrogate'' data sets which are similar to the original time series but consistent with the null hypothesis, and the computation of a discriminating statistic for the original and for each of the surrogate data sets. The idea is to test the original time series against the null hypothesis by checking whether the discriminating statistic computed for the original time series differs significantly from the statistics computed for each of the surrogate sets. While some data sets very cleanly exhibit low-dimensional chaos, there are many cases where the evidence is sketchy and difficult to evaluate. We hope to provide a framework within which such claims of nonlinearity can be evaluated. 5 refs., 4 figs

Introduction to time series and forecasting

CERN Document Server

Brockwell, Peter J

2016-01-01

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

Synchrotron Imaging Computations on the Grid without the Computing Element

International Nuclear Information System (INIS)

Curri, A; Pugliese, R; Borghes, R; Kourousias, G

2011-01-01

Besides the heavy use of the Grid in the Synchrotron Radiation Facility (SRF) Elettra, additional special requirements from the beamlines had to be satisfied through a novel solution that we present in this work. In the traditional Grid Computing paradigm the computations are performed on the Worker Nodes of the grid element known as the Computing Element. A Grid middleware extension that our team has been working on, is that of the Instrument Element. In general it is used to Grid-enable instrumentation; and it can be seen as a neighbouring concept to that of the traditional Control Systems. As a further extension we demonstrate the Instrument Element as the steering mechanism for a series of computations. In our deployment it interfaces a Control System that manages a series of computational demanding Scientific Imaging tasks in an online manner. The instrument control in Elettra is done through a suitable Distributed Control System, a common approach in the SRF community. The applications that we present are for a beamline working in medical imaging. The solution resulted to a substantial improvement of a Computed Tomography workflow. The near-real-time requirements could not have been easily satisfied from our Grid's middleware (gLite) due to the various latencies often occurred during the job submission and queuing phases. Moreover the required deployment of a set of TANGO devices could not have been done in a standard gLite WN. Besides the avoidance of certain core Grid components, the Grid Security infrastructure has been utilised in the final solution.

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.

Frontiers in Time Series and Financial Econometrics

OpenAIRE

Ling, S.; McAleer, M.J.; Tong, H.

2015-01-01

__Abstract__ Two of the fastest growing frontiers in econometrics and quantitative finance are time series and financial econometrics. Significant theoretical contributions to financial econometrics have been made by experts in statistics, econometrics, mathematics, and time series analysis. The purpose of this special issue of the journal on “Frontiers in Time Series and Financial Econometrics” is to highlight several areas of research by leading academics in which novel methods have contrib...

Resistance Fluctuations in GaAs Nanowire Grids

Directory of Open Access Journals (Sweden)

Ivan Marasović

2014-01-01

Full Text Available We present a numerical study on resistance fluctuations in a series of nanowire-based grids. Each grid is made of GaAs nanowires arranged in parallel with metallic contacts crossing all nanowires perpendicularly. Electrical properties of GaAs nanowires known from previous experimental research are used as input parameters in the simulation procedure. Due to the nonhomogeneous doping, the resistivity changes along nanowire. Allowing two possible nanowire orientations (“upwards” or “downwards”, the resulting grid is partially disordered in vertical direction which causes resistance fluctuations. The system is modeled using a two-dimensional random resistor network. Transfer-matrix computation algorithm is used to calculate the total network resistance. It is found that probability density function (PDF of resistance fluctuations for a series of nanowire grids changes from Gaussian behavior towards the Bramwell-Holdsworth-Pinton distribution when both nanowire orientations are equally represented in the grid.

Scale-dependent intrinsic entropies of complex time series.

Science.gov (United States)

Yeh, Jia-Rong; Peng, Chung-Kang; Huang, Norden E

2016-04-13

Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal's complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease. © 2016 The Author(s).

Elements of nonlinear time series analysis and forecasting

CERN Document Server

De Gooijer, Jan G

2017-01-01

This book provides an overview of the current state-of-the-art of nonlinear time series analysis, richly illustrated with examples, pseudocode algorithms and real-world applications. Avoiding a “theorem-proof” format, it shows concrete applications on a variety of empirical time series. The book can be used in graduate courses in nonlinear time series and at the same time also includes interesting material for more advanced readers. Though it is largely self-contained, readers require an understanding of basic linear time series concepts, Markov chains and Monte Carlo simulation methods. The book covers time-domain and frequency-domain methods for the analysis of both univariate and multivariate (vector) time series. It makes a clear distinction between parametric models on the one hand, and semi- and nonparametric models/methods on the other. This offers the reader the option of concentrating exclusively on one of these nonlinear time series analysis methods. To make the book as user friendly as possible...

An Energy-Based Similarity Measure for Time Series

Directory of Open Access Journals (Sweden)

Pierre Brunagel

2007-11-01

Full Text Available A new similarity measure, called SimilB, for time series analysis, based on the cross-ΨB-energy operator (2004, is introduced. ΨB is a nonlinear measure which quantifies the interaction between two time series. Compared to Euclidean distance (ED or the Pearson correlation coefficient (CC, SimilB includes the temporal information and relative changes of the time series using the first and second derivatives of the time series. SimilB is well suited for both nonstationary and stationary time series and particularly those presenting discontinuities. Some new properties of ΨB are presented. Particularly, we show that ΨB as similarity measure is robust to both scale and time shift. SimilB is illustrated with synthetic time series and an artificial dataset and compared to the CC and the ED measures.

Detecting chaos in irregularly sampled time series.

Science.gov (United States)

Kulp, C W

2013-09-01

Recently, Wiebe and Virgin [Chaos 22, 013136 (2012)] developed an algorithm which detects chaos by analyzing a time series' power spectrum which is computed using the Discrete Fourier Transform (DFT). Their algorithm, like other time series characterization algorithms, requires that the time series be regularly sampled. Real-world data, however, are often irregularly sampled, thus, making the detection of chaotic behavior difficult or impossible with those methods. In this paper, a characterization algorithm is presented, which effectively detects chaos in irregularly sampled time series. The work presented here is a modification of Wiebe and Virgin's algorithm and uses the Lomb-Scargle Periodogram (LSP) to compute a series' power spectrum instead of the DFT. The DFT is not appropriate for irregularly sampled time series. However, the LSP is capable of computing the frequency content of irregularly sampled data. Furthermore, a new method of analyzing the power spectrum is developed, which can be useful for differentiating between chaotic and non-chaotic behavior. The new characterization algorithm is successfully applied to irregularly sampled data generated by a model as well as data consisting of observations of variable stars.

Building Chaotic Model From Incomplete Time Series

Science.gov (United States)

Siek, Michael; Solomatine, Dimitri

2010-05-01

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

Multivariate Time Series Search

Data.gov (United States)

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

Analysing Stable Time Series

National Research Council Canada - National Science Library

Adler, Robert

1997-01-01

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

Analysis of trend in temperature and rainfall time series of an Indian arid region: comparative evaluation of salient techniques

Science.gov (United States)

Machiwal, Deepesh; Gupta, Ankit; Jha, Madan Kumar; Kamble, Trupti

2018-04-01

This study investigated trends in 35 years (1979-2013) temperature (maximum, Tmax and minimum, Tmin) and rainfall at annual and seasonal (pre-monsoon, monsoon, post-monsoon, and winter) scales for 31 grid points in a coastal arid region of India. Box-whisker plots of annual temperature and rainfall time series depict systematic spatial gradients. Trends were examined by applying eight tests, such as Kendall rank correlation (KRC), Spearman rank order correlation (SROC), Mann-Kendall (MK), four modified MK tests, and innovative trend analysis (ITA). Trend magnitudes were quantified by Sen's slope estimator, and a new method was adopted to assess the significance of linear trends in MK-test statistics. It was found that the significant serial correlation is prominent in the annual and post-monsoon Tmax and Tmin, and pre-monsoon Tmin. The KRC and MK tests yielded similar results in close resemblance with the SROC test. The performance of two modified MK tests considering variance-correction approaches was found superior to the KRC, MK, modified MK with pre-whitening, and ITA tests. The performance of original MK test is poor due to the presence of serial correlation, whereas the ITA method is over-sensitive in identifying trends. Significantly increasing trends are more prominent in Tmin than Tmax. Further, both the annual and monsoon rainfall time series have a significantly increasing trend of 9 mm year-1. The sequential significance of linear trend in MK test-statistics is very strong (R 2 ≥ 0.90) in the annual and pre-monsoon Tmin (90% grid points), and strong (R 2 ≥ 0.75) in monsoon Tmax (68% grid points), monsoon, post-monsoon, and winter Tmin (respectively 65, 55, and 48% grid points), as well as in the annual and monsoon rainfalls (respectively 68 and 61% grid points). Finally, this study recommends use of variance-corrected MK test for the precise identification of trends. It is emphasized that the rising Tmax may hamper crop growth due to enhanced

Neural Network Models for Time Series Forecasts

OpenAIRE

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 ...

Time Series Observations in the North Indian Ocean

Digital Repository Service at National Institute of Oceanography (India)

Shenoy, D.M.; Naik, H.; Kurian, S.; Naqvi, S.W.A.; Khare, N.

Ocean and the ongoing time series study (Candolim Time Series; CaTS) off Goa. In addition, this article also focuses on the new time series initiative in the Arabian Sea and the Bay of Bengal under Sustained Indian Ocean Biogeochemistry and Ecosystem...

Validation of a Robust Neural Real-Time Voltage Estimator for Active Distribution Grids on Field Data

DEFF Research Database (Denmark)

Pertl, Michael; Douglass, Philip James; Heussen, Kai

2018-01-01

network approach for voltage estimation in active distribution grids by means of measured data from two feeders of a real low voltage distribution grid. The approach enables a real-time voltage estimation at locations in the distribution grid, where otherwise only non-real-time measurements are available......The installation of measurements in distribution grids enables the development of data driven methods for the power system. However, these methods have to be validated in order to understand the limitations and capabilities for their use. This paper presents a systematic validation of a neural...

Geometric noise reduction for multivariate time series.

Science.gov (United States)

Mera, M Eugenia; Morán, Manuel

2006-03-01

We propose an algorithm for the reduction of observational noise in chaotic multivariate time series. The algorithm is based on a maximum likelihood criterion, and its goal is to reduce the mean distance of the points of the cleaned time series to the attractor. We give evidence of the convergence of the empirical measure associated with the cleaned time series to the underlying invariant measure, implying the possibility to predict the long run behavior of the true dynamics.

BRITS: Bidirectional Recurrent Imputation for Time Series

OpenAIRE

Cao, Wei; Wang, Dong; Li, Jian; Zhou, Hao; Li, Lei; Li, Yitan

2018-01-01

Time series are widely used as signals in many classification/regression tasks. It is ubiquitous that time series contains many missing values. Given multiple correlated time series data, how to fill in missing values and to predict their class labels? Existing imputation methods often impose strong assumptions of the underlying data generating process, such as linear dynamics in the state space. In this paper, we propose BRITS, a novel method based on recurrent neural networks for missing va...

Efficient Algorithms for Segmentation of Item-Set Time Series

Science.gov (United States)

Chundi, Parvathi; Rosenkrantz, Daniel J.

We propose a special type of time series, which we call an item-set time series, to facilitate the temporal analysis of software version histories, email logs, stock market data, etc. In an item-set time series, each observed data value is a set of discrete items. We formalize the concept of an item-set time series and present efficient algorithms for segmenting a given item-set time series. Segmentation of a time series partitions the time series into a sequence of segments where each segment is constructed by combining consecutive time points of the time series. Each segment is associated with an item set that is computed from the item sets of the time points in that segment, using a function which we call a measure function. We then define a concept called the segment difference, which measures the difference between the item set of a segment and the item sets of the time points in that segment. The segment difference values are required to construct an optimal segmentation of the time series. We describe novel and efficient algorithms to compute segment difference values for each of the measure functions described in the paper. We outline a dynamic programming based scheme to construct an optimal segmentation of the given item-set time series. We use the item-set time series segmentation techniques to analyze the temporal content of three different data sets—Enron email, stock market data, and a synthetic data set. The experimental results show that an optimal segmentation of item-set time series data captures much more temporal content than a segmentation constructed based on the number of time points in each segment, without examining the item set data at the time points, and can be used to analyze different types of temporal data.

Studies on time series applications in environmental sciences

CERN Document Server

Bărbulescu, Alina

2016-01-01

Time series analysis and modelling represent a large study field, implying the approach from the perspective of the time and frequency, with applications in different domains. Modelling hydro-meteorological time series is difficult due to the characteristics of these series, as long range dependence, spatial dependence, the correlation with other series. Continuous spatial data plays an important role in planning, risk assessment and decision making in environmental management. In this context, in this book we present various statistical tests and modelling techniques used for time series analysis, as well as applications to hydro-meteorological series from Dobrogea, a region situated in the south-eastern part of Romania, less studied till now. Part of the results are accompanied by their R code. .

Prediction and Geometry of Chaotic Time Series

National Research Council Canada - National Science Library

Leonardi, Mary

1997-01-01

This thesis examines the topic of chaotic time series. An overview of chaos, dynamical systems, and traditional approaches to time series analysis is provided, followed by an examination of state space reconstruction...

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

International Nuclear Information System (INIS)

Lefieux, V.

2007-10-01

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

Sensor-Generated Time Series Events: A Definition Language

Science.gov (United States)

Anguera, Aurea; Lara, Juan A.; Lizcano, David; Martínez, Maria Aurora; Pazos, Juan

2012-01-01

There are now a great many domains where information is recorded by sensors over a limited time period or on a permanent basis. This data flow leads to sequences of data known as time series. In many domains, like seismography or medicine, time series analysis focuses on particular regions of interest, known as events, whereas the remainder of the time series contains hardly any useful information. In these domains, there is a need for mechanisms to identify and locate such events. In this paper, we propose an events definition language that is general enough to be used to easily and naturally define events in time series recorded by sensors in any domain. The proposed language has been applied to the definition of time series events generated within the branch of medicine dealing with balance-related functions in human beings. A device, called posturograph, is used to study balance-related functions. The platform has four sensors that record the pressure intensity being exerted on the platform, generating four interrelated time series. As opposed to the existing ad hoc proposals, the results confirm that the proposed language is valid, that is generally applicable and accurate, for identifying the events contained in the time series.

Correlation and multifractality in climatological time series

International Nuclear Information System (INIS)

Pedron, I T

2010-01-01

Climate can be described by statistical analysis of mean values of atmospheric variables over a period. It is possible to detect correlations in climatological time series and to classify its behavior. In this work the Hurst exponent, which can characterize correlation and persistence in time series, is obtained by using the Detrended Fluctuation Analysis (DFA) method. Data series of temperature, precipitation, humidity, solar radiation, wind speed, maximum squall, atmospheric pressure and randomic series are studied. Furthermore, the multifractality of such series is analyzed applying the Multifractal Detrended Fluctuation Analysis (MF-DFA) method. The results indicate presence of correlation (persistent character) in all climatological series and multifractality as well. A larger set of data, and longer, could provide better results indicating the universality of the exponents.

Near-Real-Time DMSP SSM/I-SSMIS Daily Polar Gridded Brightness Temperatures

Data.gov (United States)

National Aeronautics and Space Administration — The Near-Real-Time DMSP SSM/I-SSMIS Daily Polar Gridded Brightness Temperature product provides near-real-time brightness temperatures for both the Northern and...

Time Series Forecasting with Missing Values

Directory of Open Access Journals (Sweden)

Shin-Fu Wu

2015-11-01

Full Text Available Time series prediction has become more popular in various kinds of applications such as weather prediction, control engineering, financial analysis, industrial monitoring, etc. To deal with real-world problems, we are often faced with missing values in the data due to sensor malfunctions or human errors. Traditionally, the missing values are simply omitted or replaced by means of imputation methods. However, omitting those missing values may cause temporal discontinuity. Imputation methods, on the other hand, may alter the original time series. In this study, we propose a novel forecasting method based on least squares support vector machine (LSSVM. We employ the input patterns with the temporal information which is defined as local time index (LTI. Time series data as well as local time indexes are fed to LSSVM for doing forecasting without imputation. We compare the forecasting performance of our method with other imputation methods. Experimental results show that the proposed method is promising and is worth further investigations.

Reconstruction of ensembles of coupled time-delay systems from time series.

Science.gov (United States)

Sysoev, I V; Prokhorov, M D; Ponomarenko, V I; Bezruchko, B P

2014-06-01

We propose a method to recover from time series the parameters of coupled time-delay systems and the architecture of couplings between them. The method is based on a reconstruction of model delay-differential equations and estimation of statistical significance of couplings. It can be applied to networks composed of nonidentical nodes with an arbitrary number of unidirectional and bidirectional couplings. We test our method on chaotic and periodic time series produced by model equations of ensembles of diffusively coupled time-delay systems in the presence of noise, and apply it to experimental time series obtained from electronic oscillators with delayed feedback coupled by resistors.

The analysis of time series: an introduction

National Research Council Canada - National Science Library

Chatfield, Christopher

1989-01-01

.... A variety of practical examples are given to support the theory. The book covers a wide range of time-series topics, including probability models for time series, Box-Jenkins forecasting, spectral analysis, linear systems and system identification...

Real Time Photovoltaic Array Simulator for Testing Grid-Connected PV Inverters

DEFF Research Database (Denmark)

Sera, Dezso; Valentini, Massimo; Raducu, Alin

2008-01-01

In this paper a real time flexible PV array simulator is presented. It is a system that can simulate different PV panel arrays in specific environmental conditions. To evaluate performance of the Maximum Power Point Tracking (MPPT) of grid-connected Photovoltaic (PV) inverters only measurements...... undertaken with an appropriate PV array simulator provide accurate and reproducible results. Thus the PV array simulator has been developed and implemented. MPPT efficiency tests on a commercial grid-connected PV inverter have been performed to validate the PV array simulator....

Transmission Line Series Compensation for Wind Energy Transmission

International Nuclear Information System (INIS)

Palanichamy, C; Wong, Y C

2015-01-01

Wind energy has demonstrated to be a clean, copious and absolutely renewable source of energy, and the large penetration of it into the power grid indicates that wind energy is considered an effective means of power generation, Transmission of wind energy from remote locations to load centers necessitates long transmission lines. Series compensation is a proven and economical transmission solution to address system power transfer strength, grid stability, and voltage profile issues of long transmission lines. In this paper, a programmable approach to determine the capacitive reactance of series capacitor and optimum location for its placement to achieve maximum power transfer gas been presented. The respective program with sample solutions has been provided for real-time applications. (paper)

Time series modeling in traffic safety research.

Science.gov (United States)

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.

Research and design of smart grid monitoring control via terminal based on iOS system

Science.gov (United States)

Fu, Wei; Gong, Li; Chen, Heli; Pan, Guangji

2017-06-01

Aiming at a series of problems existing in current smart grid monitoring Control Terminal, such as high costs, poor portability, simple monitoring system, poor software extensions, low system reliability when transmitting information, single man-machine interface, poor security, etc., smart grid remote monitoring system based on the iOS system has been designed. The system interacts with smart grid server so that it can acquire grid data through WiFi/3G/4G networks, and monitor each grid line running status, as well as power plant equipment operating conditions. When it occurs an exception in the power plant, incident information can be sent to the user iOS terminal equipment timely, which will provide troubleshooting information to help the grid staff to make the right decisions in a timely manner, to avoid further accidents. Field tests have shown the system realizes the integrated grid monitoring functions, low maintenance cost, friendly interface, high security and reliability, and it possesses certain applicable value.

Time series prediction: statistical and neural techniques

Science.gov (United States)

Zahirniak, Daniel R.; DeSimio, Martin P.

1996-03-01

In this paper we compare the performance of nonlinear neural network techniques to those of linear filtering techniques in the prediction of time series. Specifically, we compare the results of using the nonlinear systems, known as multilayer perceptron and radial basis function neural networks, with the results obtained using the conventional linear Wiener filter, Kalman filter and Widrow-Hoff adaptive filter in predicting future values of stationary and non- stationary time series. Our results indicate the performance of each type of system is heavily dependent upon the form of the time series being predicted and the size of the system used. In particular, the linear filters perform adequately for linear or near linear processes while the nonlinear systems perform better for nonlinear processes. Since the linear systems take much less time to be developed, they should be tried prior to using the nonlinear systems when the linearity properties of the time series process are unknown.

Effectiveness of Multivariate Time Series Classification Using Shapelets

Directory of Open Access Journals (Sweden)

A. P. Karpenko

2015-01-01

Full Text Available Typically, time series classifiers require signal pre-processing (filtering signals from noise and artifact removal, etc., enhancement of signal features (amplitude, frequency, spectrum, etc., classification of signal features in space using the classical techniques and classification algorithms of multivariate data. We consider a method of classifying time series, which does not require enhancement of the signal features. The method uses the shapelets of time series (time series shapelets i.e. small fragments of this series, which reflect properties of one of its classes most of all.Despite the significant number of publications on the theory and shapelet applications for classification of time series, the task to evaluate the effectiveness of this technique remains relevant. An objective of this publication is to study the effectiveness of a number of modifications of the original shapelet method as applied to the multivariate series classification that is a littlestudied problem. The paper presents the problem statement of multivariate time series classification using the shapelets and describes the shapelet–based basic method of binary classification, as well as various generalizations and proposed modification of the method. It also offers the software that implements a modified method and results of computational experiments confirming the effectiveness of the algorithmic and software solutions.The paper shows that the modified method and the software to use it allow us to reach the classification accuracy of about 85%, at best. The shapelet search time increases in proportion to input data dimension.

Neural Model with Particle Swarm Optimization Kalman Learning for Forecasting in Smart Grids

Directory of Open Access Journals (Sweden)

Alma Y. Alanis

2013-01-01

Full Text Available This paper discusses a novel training algorithm for a neural network architecture applied to time series prediction with smart grids applications. The proposed training algorithm is based on an extended Kalman filter (EKF improved using particle swarm optimization (PSO to compute the design parameters. The EKF-PSO-based algorithm is employed to update the synaptic weights of the neural network. The size of the regression vector is determined by means of the Cao methodology. The proposed structure captures more efficiently the complex nature of the wind speed, energy generation, and electrical load demand time series that are constantly monitorated in a smart grid benchmark. The proposed model is trained and tested using real data values in order to show the applicability of the proposed scheme.

Workshop on Future Generation Grids

CERN Document Server

Laforenza, Domenico; Reinefeld, Alexander

2006-01-01

The Internet and the Web continue to have a major impact on society. By allowing us to discover and access information on a global scale, they have created entirely new businesses and brought new meaning to the term surf. In addition, however, we want processing, and increasingly, we want collaborative processing within distributed teams. This need has led to the creation of the Grid - an infrastructure that enables us to share capabilities, and integrate services and resources within and across enterprises. "Future Generation Grids" is the second in the "CoreGRID" series. This edited volume brings together contributed articles by scientists and researchers in the Grid community in an attempt to draw a clearer picture of the future generation Grids. This book also identifies some of the most challenging problems on the way to achieving the invisible Grid ideas

Time-series-analysis techniques applied to nuclear-material accounting

International Nuclear Information System (INIS)

Pike, D.H.; Morrison, G.W.; Downing, D.J.

1982-05-01

This document is designed to introduce the reader to the applications of Time Series Analysis techniques to Nuclear Material Accountability data. Time series analysis techniques are designed to extract information from a collection of random variables ordered by time by seeking to identify any trends, patterns, or other structure in the series. Since nuclear material accountability data is a time series, one can extract more information using time series analysis techniques than by using other statistical techniques. Specifically, the objective of this document is to examine the applicability of time series analysis techniques to enhance loss detection of special nuclear materials. An introductory section examines the current industry approach which utilizes inventory differences. The error structure of inventory differences is presented. Time series analysis techniques discussed include the Shewhart Control Chart, the Cumulative Summation of Inventory Differences Statistics (CUSUM) and the Kalman Filter and Linear Smoother

Clinical and epidemiological rounds. Time series

Directory of Open Access Journals (Sweden)

León-Álvarez, Alba Luz

2016-07-01

Full Text Available Analysis of time series is a technique that implicates the study of individuals or groups observed in successive moments in time. This type of analysis allows the study of potential causal relationships between different variables that change over time and relate to each other. It is the most important technique to make inferences about the future, predicting, on the basis or what has happened in the past and it is applied in different disciplines of knowledge. Here we discuss different components of time series, the analysis technique and specific examples in health research.

Integer-valued time series

NARCIS (Netherlands)

van den Akker, R.

2007-01-01

This thesis adresses statistical problems in econometrics. The first part contributes statistical methodology for nonnegative integer-valued time series. The second part of this thesis discusses semiparametric estimation in copula models and develops semiparametric lower bounds for a large class of

Robust Forecasting of Non-Stationary Time Series

NARCIS (Netherlands)

Croux, C.; Fried, R.; Gijbels, I.; Mahieu, K.

2010-01-01

This paper proposes a robust forecasting method for non-stationary time series. The time series is modelled using non-parametric heteroscedastic regression, and fitted by a localized MM-estimator, combining high robustness and large efficiency. The proposed method is shown to produce reliable

Application of rule-based data mining techniques to real time ATLAS Grid job monitoring data

CERN Document Server

Ahrens, R; The ATLAS collaboration; Kalinin, S; Maettig, P; Sandhoff, M; dos Santos, T; Volkmer, F

2012-01-01

The Job Execution Monitor (JEM) is a job-centric grid job monitoring software developed at the University of Wuppertal and integrated into the pilot-based “PanDA” job brokerage system leveraging physics analysis and Monte Carlo event production for the ATLAS experiment on the Worldwide LHC Computing Grid (WLCG). With JEM, job progress and grid worker node health can be supervised in real time by users, site admins and shift personnel. Imminent error conditions can be detected early and countermeasures can be initiated by the Job’s owner immideatly. Grid site admins can access aggregated data of all monitored jobs to infer the site status and to detect job and Grid worker node misbehaviour. Shifters can use the same aggregated data to quickly react to site error conditions and broken production tasks. In this work, the application of novel data-centric rule based methods and data-mining techniques to the real time monitoring data is discussed. The usage of such automatic inference techniques on monitorin...

Characterizing time series via complexity-entropy curves

Science.gov (United States)

Ribeiro, Haroldo V.; Jauregui, Max; Zunino, Luciano; Lenzi, Ervin K.

2017-06-01

The search for patterns in time series is a very common task when dealing with complex systems. This is usually accomplished by employing a complexity measure such as entropies and fractal dimensions. However, such measures usually only capture a single aspect of the system dynamics. Here, we propose a family of complexity measures for time series based on a generalization of the complexity-entropy causality plane. By replacing the Shannon entropy by a monoparametric entropy (Tsallis q entropy) and after considering the proper generalization of the statistical complexity (q complexity), we build up a parametric curve (the q -complexity-entropy curve) that is used for characterizing and classifying time series. Based on simple exact results and numerical simulations of stochastic processes, we show that these curves can distinguish among different long-range, short-range, and oscillating correlated behaviors. Also, we verify that simulated chaotic and stochastic time series can be distinguished based on whether these curves are open or closed. We further test this technique in experimental scenarios related to chaotic laser intensity, stock price, sunspot, and geomagnetic dynamics, confirming its usefulness. Finally, we prove that these curves enhance the automatic classification of time series with long-range correlations and interbeat intervals of healthy subjects and patients with heart disease.

Complex network approach to fractional time series

Energy Technology Data Exchange (ETDEWEB)

Manshour, Pouya [Physics Department, Persian Gulf University, Bushehr 75169 (Iran, Islamic Republic of)

2015-10-15

In order to extract correlation information inherited in stochastic time series, the visibility graph algorithm has been recently proposed, by which a time series can be mapped onto a complex network. We demonstrate that the visibility algorithm is not an appropriate one to study the correlation aspects of a time series. We then employ the horizontal visibility algorithm, as a much simpler one, to map fractional processes onto complex networks. The degree distributions are shown to have parabolic exponential forms with Hurst dependent fitting parameter. Further, we take into account other topological properties such as maximum eigenvalue of the adjacency matrix and the degree assortativity, and show that such topological quantities can also be used to predict the Hurst exponent, with an exception for anti-persistent fractional Gaussian noises. To solve this problem, we take into account the Spearman correlation coefficient between nodes' degrees and their corresponding data values in the original time series.

Introduction to time series analysis and forecasting

CERN Document Server

Montgomery, Douglas C; Kulahci, Murat

2008-01-01

An accessible introduction to the most current thinking in and practicality of forecasting techniques in the context of time-oriented data. Analyzing time-oriented data and forecasting are among the most important problems that analysts face across many fields, ranging from finance and economics to production operations and the natural sciences. As a result, there is a widespread need for large groups of people in a variety of fields to understand the basic concepts of time series analysis and forecasting. Introduction to Time Series Analysis and Forecasting presents the time series analysis branch of applied statistics as the underlying methodology for developing practical forecasts, and it also bridges the gap between theory and practice by equipping readers with the tools needed to analyze time-oriented data and construct useful, short- to medium-term, statistically based forecasts.

Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance.

Science.gov (United States)

Liu, Yongli; Chen, Jingli; Wu, Shuai; Liu, Zhizhong; Chao, Hao

2018-01-01

Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy.

Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance

Science.gov (United States)

Chen, Jingli; Wu, Shuai; Liu, Zhizhong; Chao, Hao

2018-01-01

Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy. PMID:29795600

A Real-Time Open Access Platform Towards Proof of Concept for Smart Grid Applications

DEFF Research Database (Denmark)

Kemal, Mohammed Seifu; Petersen, Lennart; Iov, Florin

2018-01-01

: electrical grid layer, ICT & network emulation layer and control layer. DiSC-OPAL, a toolbox built for OPAL-RT real time grid simulation; comprising of models for wide variety of controllable flexible assets, stochastic power sources for wind and solar power plants, real consumption data’s and electrical...

The foundations of modern time series analysis

CERN Document Server

Mills, Terence C

2011-01-01

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

Time series clustering in large data sets

Directory of Open Access Journals (Sweden)

Jiří Fejfar

2011-01-01

Full Text Available The clustering of time series is a widely researched area. There are many methods for dealing with this task. We are actually using the Self-organizing map (SOM with the unsupervised learning algorithm for clustering of time series. After the first experiment (Fejfar, Weinlichová, Šťastný, 2009 it seems that the whole concept of the clustering algorithm is correct but that we have to perform time series clustering on much larger dataset to obtain more accurate results and to find the correlation between configured parameters and results more precisely. The second requirement arose in a need for a well-defined evaluation of results. It seems useful to use sound recordings as instances of time series again. There are many recordings to use in digital libraries, many interesting features and patterns can be found in this area. We are searching for recordings with the similar development of information density in this experiment. It can be used for musical form investigation, cover songs detection and many others applications.The objective of the presented paper is to compare clustering results made with different parameters of feature vectors and the SOM itself. We are describing time series in a simplistic way evaluating standard deviations for separated parts of recordings. The resulting feature vectors are clustered with the SOM in batch training mode with different topologies varying from few neurons to large maps.There are other algorithms discussed, usable for finding similarities between time series and finally conclusions for further research are presented. We also present an overview of the related actual literature and projects.

Harmonizing electricity markets with physics : real time performance monitoring using grid-3PTM

International Nuclear Information System (INIS)

Budhraja, V.S.

2003-01-01

The Electric Power Group, LLC provides management and strategic consulting services for the electric power industry, with special emphasis on industry restructuring, competitive electricity markets, grid operations and reliability, power technologies, venture investments and start-ups. The Consortium for Electric Reliability Technology Solutions involves national laboratories, universities, and industry partners in researching, developing, and commercializing electric reliability technology solutions to protect and enhance the reliability of the American electric power system under the emerging competitive electricity market structure. Physics differentiate electric markets from other markets: there is real-time balancing, no storage, interconnected network, and power flows governed by physics. Some issues affecting both grid reliability and market issues are difficult to separate, such as security and congestion management, voltage management, reserves, frequency volatility, and others. The author examined the following investment challenges facing the electricity market: grid solutions, market solutions, and technology solutions. The real time performance monitoring and prediction platform, grid-3P was described and applications discussed, such as ACE-frequency monitoring, performance monitoring for automatic generation control (AGC) and frequency response, voltage/VAR monitoring, stability monitoring using phasor technology, and market monitoring. figs

Transmission of linear regression patterns between time series: from relationship in time series to complex networks.

Science.gov (United States)

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

2014-07-01

The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

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 prediction and applications a machine intelligence approach

CERN Document Server

Konar, Amit

2017-01-01

This book presents machine learning and type-2 fuzzy sets for the prediction of time-series with a particular focus on business forecasting applications. It also proposes new uncertainty management techniques in an economic time-series using type-2 fuzzy sets for prediction of the time-series at a given time point from its preceding value in fluctuating business environments. It employs machine learning to determine repetitively occurring similar structural patterns in the time-series and uses stochastic automaton to predict the most probabilistic structure at a given partition of the time-series. Such predictions help in determining probabilistic moves in a stock index time-series Primarily written for graduate students and researchers in computer science, the book is equally useful for researchers/professionals in business intelligence and stock index prediction. A background of undergraduate level mathematics is presumed, although not mandatory, for most of the sections. Exercises with tips are provided at...

A Time Series Forecasting Method

Directory of Open Access Journals (Sweden)

Wang Zhao-Yu

2017-01-01

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

Stochastic nature of series of waiting times

Science.gov (United States)

Anvari, Mehrnaz; Aghamohammadi, Cina; Dashti-Naserabadi, H.; Salehi, E.; Behjat, E.; Qorbani, M.; Khazaei Nezhad, M.; Zirak, M.; Hadjihosseini, Ali; Peinke, Joachim; Tabar, M. Reza Rahimi

2013-06-01

Although fluctuations in the waiting time series have been studied for a long time, some important issues such as its long-range memory and its stochastic features in the presence of nonstationarity have so far remained unstudied. Here we find that the “waiting times” series for a given increment level have long-range correlations with Hurst exponents belonging to the interval 1/2time distribution. We find that the logarithmic difference of waiting times series has a short-range correlation, and then we study its stochastic nature using the Markovian method and determine the corresponding Kramers-Moyal coefficients. As an example, we analyze the velocity fluctuations in high Reynolds number turbulence and determine the level dependence of Markov time scales, as well as the drift and diffusion coefficients. We show that the waiting time distributions exhibit power law tails, and we were able to model the distribution with a continuous time random walk.

Efficient Approximate OLAP Querying Over Time Series

DEFF Research Database (Denmark)

Perera, Kasun Baruhupolage Don Kasun Sanjeewa; Hahmann, Martin; Lehner, Wolfgang

2016-01-01

The ongoing trend for data gathering not only produces larger volumes of data, but also increases the variety of recorded data types. Out of these, especially time series, e.g. various sensor readings, have attracted attention in the domains of business intelligence and decision making. As OLAP...... queries play a major role in these domains, it is desirable to also execute them on time series data. While this is not a problem on the conceptual level, it can become a bottleneck with regards to query run-time. In general, processing OLAP queries gets more computationally intensive as the volume...... of data grows. This is a particular problem when querying time series data, which generally contains multiple measures recorded at fine time granularities. Usually, this issue is addressed either by scaling up hardware or by employing workload based query optimization techniques. However, these solutions...

A Dynamic Fuzzy Cluster Algorithm for Time Series

Directory of Open Access Journals (Sweden)

Min Ji

2013-01-01

clustering time series by introducing the definition of key point and improving FCM algorithm. The proposed algorithm works by determining those time series whose class labels are vague and further partitions them into different clusters over time. The main advantage of this approach compared with other existing algorithms is that the property of some time series belonging to different clusters over time can be partially revealed. Results from simulation-based experiments on geographical data demonstrate the excellent performance and the desired results have been obtained. The proposed algorithm can be applied to solve other clustering problems in data mining.

A novel weight determination method for time series data aggregation

Science.gov (United States)

Xu, Paiheng; Zhang, Rong; Deng, Yong

2017-09-01

Aggregation in time series is of great importance in time series smoothing, predicting and other time series analysis process, which makes it crucial to address the weights in times series correctly and reasonably. In this paper, a novel method to obtain the weights in time series is proposed, in which we adopt induced ordered weighted aggregation (IOWA) operator and visibility graph averaging (VGA) operator and linearly combine the weights separately generated by the two operator. The IOWA operator is introduced to the weight determination of time series, through which the time decay factor is taken into consideration. The VGA operator is able to generate weights with respect to the degree distribution in the visibility graph constructed from the corresponding time series, which reflects the relative importance of vertices in time series. The proposed method is applied to two practical datasets to illustrate its merits. The aggregation of Construction Cost Index (CCI) demonstrates the ability of proposed method to smooth time series, while the aggregation of The Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) illustrate how proposed method maintain the variation tendency of original data.

Foundations of Sequence-to-Sequence Modeling for Time Series

OpenAIRE

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...

Climate Prediction Center (CPC) Global Precipitation Time Series

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The global precipitation time series provides time series charts showing observations of daily precipitation as well as accumulated precipitation compared to normal...

Climate Prediction Center (CPC) Global Temperature Time Series

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The global temperature time series provides time series charts using station based observations of daily temperature. These charts provide information about the...

Recurrent Neural Network Applications for Astronomical Time Series

Science.gov (United States)

Protopapas, Pavlos

2017-06-01

The benefits of good predictive models in astronomy lie in early event prediction systems and effective resource allocation. Current time series methods applicable to regular time series have not evolved to generalize for irregular time series. In this talk, I will describe two Recurrent Neural Network methods, Long Short-Term Memory (LSTM) and Echo State Networks (ESNs) for predicting irregular time series. Feature engineering along with a non-linear modeling proved to be an effective predictor. For noisy time series, the prediction is improved by training the network on error realizations using the error estimates from astronomical light curves. In addition to this, we propose a new neural network architecture to remove correlation from the residuals in order to improve prediction and compensate for the noisy data. Finally, I show how to set hyperparameters for a stable and performant solution correctly. In this work, we circumvent this obstacle by optimizing ESN hyperparameters using Bayesian optimization with Gaussian Process priors. This automates the tuning procedure, enabling users to employ the power of RNN without needing an in-depth understanding of the tuning procedure.

Real Time Grid Reliability Management 2005

Energy Technology Data Exchange (ETDEWEB)

Eto, Joe; Eto, Joe; Lesieutre, Bernard; Lewis, Nancy Jo; Parashar, Manu

2008-07-07

The increased need to manage California?s electricity grid in real time is a result of the ongoing transition from a system operated by vertically-integrated utilities serving native loads to one operated by an independent system operator supporting competitive energy markets. During this transition period, the traditional approach to reliability management -- construction of new transmission lines -- has not been pursued due to unresolved issues related to the financing and recovery of transmission project costs. In the absence of investments in new transmission infrastructure, the best strategy for managing reliability is to equip system operators with better real-time information about actual operating margins so that they can better understand and manage the risk of operating closer to the edge. A companion strategy is to address known deficiencies in offline modeling tools that are needed to ground the use of improved real-time tools. This project: (1) developed and conducted first-ever demonstrations of two prototype real-time software tools for voltage security assessment and phasor monitoring; and (2) prepared a scoping study on improving load and generator response models. Additional funding through two separate subsequent work authorizations has already been provided to build upon the work initiated in this project.

Auction Mechanism of Micro-Grid Project Transfer

Directory of Open Access Journals (Sweden)

Yong Long

2017-10-01

Full Text Available Micro-grid project transfer is the primary issue of micro-grid development. The efficiency and quality of the micro-grid project transfer directly affect the quality of micro-grid project construction and development, which is very important for the sustainable development of micro-grid. This paper constructs a multi-attribute auction model of micro-grid project transfer, which reflects the characteristics of micro-grid system and the interests of stakeholders, calculates the optimal bidding strategy and analyzes the influence of relevant factors on auction equilibrium by multi-stage dynamic game with complete information, and makes a numerical simulation analysis. Results indicate that the optimal strategy of auction mechanism is positively related to power quality, energy storage quality, and carbon emissions. Different from the previous lowest price winning mechanism, the auction mechanism formed in this paper emphasizes that the energy suppliers which provide the comprehensive optimization of power quality, energy storage quality, carbon emissions, and price will win the auction, when both the project owners and energy suppliers maximize their benefits under this auction mechanism. The auction mechanism is effective because it is in line with the principle of individual rationality and incentive compatibility. In addition, the number of energy suppliers participating in the auction and the cost of the previous auction are positively related to the auction equilibrium, both of which are adjusting the equilibrium results of the auction. At the same time, the utilization rate of renewable energy and the comprehensive utilization of energy also have a positive impact on the auction equilibrium. In the end, this paper puts forward a series of policy suggestions about micro-grid project auction. The research in this paper is of great significance to improve the auction quality of micro-grid projects and promote the sustainable development of micro-grid.

Transition Icons for Time-Series Visualization and Exploratory Analysis.

Science.gov (United States)

Nickerson, Paul V; Baharloo, Raheleh; Wanigatunga, Amal A; Manini, Todd M; Tighe, Patrick J; Rashidi, Parisa

2018-03-01

The modern healthcare landscape has seen the rapid emergence of techniques and devices that temporally monitor and record physiological signals. The prevalence of time-series data within the healthcare field necessitates the development of methods that can analyze the data in order to draw meaningful conclusions. Time-series behavior is notoriously difficult to intuitively understand due to its intrinsic high-dimensionality, which is compounded in the case of analyzing groups of time series collected from different patients. Our framework, which we call transition icons, renders common patterns in a visual format useful for understanding the shared behavior within groups of time series. Transition icons are adept at detecting and displaying subtle differences and similarities, e.g., between measurements taken from patients receiving different treatment strategies or stratified by demographics. We introduce various methods that collectively allow for exploratory analysis of groups of time series, while being free of distribution assumptions and including simple heuristics for parameter determination. Our technique extracts discrete transition patterns from symbolic aggregate approXimation representations, and compiles transition frequencies into a bag of patterns constructed for each group. These transition frequencies are normalized and aligned in icon form to intuitively display the underlying patterns. We demonstrate the transition icon technique for two time-series datasets-postoperative pain scores, and hip-worn accelerometer activity counts. We believe transition icons can be an important tool for researchers approaching time-series data, as they give rich and intuitive information about collective time-series behaviors.

Mixing times in quantum walks on two-dimensional grids

International Nuclear Information System (INIS)

Marquezino, F. L.; Portugal, R.; Abal, G.

2010-01-01

Mixing properties of discrete-time quantum walks on two-dimensional grids with toruslike boundary conditions are analyzed, focusing on their connection to the complexity of the corresponding abstract search algorithm. In particular, an exact expression for the stationary distribution of the coherent walk over odd-sided lattices is obtained after solving the eigenproblem for the evolution operator for this particular graph. The limiting distribution and mixing time of a quantum walk with a coin operator modified as in the abstract search algorithm are obtained numerically. On the basis of these results, the relation between the mixing time of the modified walk and the running time of the corresponding abstract search algorithm is discussed.

Multifractal analysis of visibility graph-based Ito-related connectivity time series.

Science.gov (United States)

Czechowski, Zbigniew; Lovallo, Michele; Telesca, Luciano

2016-02-01

In this study, we investigate multifractal properties of connectivity time series resulting from the visibility graph applied to normally distributed time series generated by the Ito equations with multiplicative power-law noise. We show that multifractality of the connectivity time series (i.e., the series of numbers of links outgoing any node) increases with the exponent of the power-law noise. The multifractality of the connectivity time series could be due to the width of connectivity degree distribution that can be related to the exit time of the associated Ito time series. Furthermore, the connectivity time series are characterized by persistence, although the original Ito time series are random; this is due to the procedure of visibility graph that, connecting the values of the time series, generates persistence but destroys most of the nonlinear correlations. Moreover, the visibility graph is sensitive for detecting wide "depressions" in input time series.

Mathematical foundations of time series analysis a concise introduction

CERN Document Server

Beran, Jan

2017-01-01

This book provides a concise introduction to the mathematical foundations of time series analysis, with an emphasis on mathematical clarity. The text is reduced to the essential logical core, mostly using the symbolic language of mathematics, thus enabling readers to very quickly grasp the essential reasoning behind time series analysis. It appeals to anybody wanting to understand time series in a precise, mathematical manner. It is suitable for graduate courses in time series analysis but is equally useful as a reference work for students and researchers alike.

Time series analysis in the social sciences the fundamentals

CERN Document Server

Shin, Youseop

2017-01-01

Times Series Analysis in the Social Sciences is a practical and highly readable introduction written exclusively for students and researchers whose mathematical background is limited to basic algebra. The book focuses on fundamental elements of time series analysis that social scientists need to understand so they can employ time series analysis for their research and practice. Through step-by-step explanations and using monthly violent crime rates as case studies, this book explains univariate time series from the preliminary visual analysis through the modeling of seasonality, trends, and re

Data imputation analysis for Cosmic Rays time series

Science.gov (United States)

Fernandes, R. C.; Lucio, P. S.; Fernandez, J. H.

2017-05-01

The occurrence of missing data concerning Galactic Cosmic Rays time series (GCR) is inevitable since loss of data is due to mechanical and human failure or technical problems and different periods of operation of GCR stations. The aim of this study was to perform multiple dataset imputation in order to depict the observational dataset. The study has used the monthly time series of GCR Climax (CLMX) and Roma (ROME) from 1960 to 2004 to simulate scenarios of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80% and 90% of missing data compared to observed ROME series, with 50 replicates. Then, the CLMX station as a proxy for allocation of these scenarios was used. Three different methods for monthly dataset imputation were selected: AMÉLIA II - runs the bootstrap Expectation Maximization algorithm, MICE - runs an algorithm via Multivariate Imputation by Chained Equations and MTSDI - an Expectation Maximization algorithm-based method for imputation of missing values in multivariate normal time series. The synthetic time series compared with the observed ROME series has also been evaluated using several skill measures as such as RMSE, NRMSE, Agreement Index, R, R2, F-test and t-test. The results showed that for CLMX and ROME, the R2 and R statistics were equal to 0.98 and 0.96, respectively. It was observed that increases in the number of gaps generate loss of quality of the time series. Data imputation was more efficient with MTSDI method, with negligible errors and best skill coefficients. The results suggest a limit of about 60% of missing data for imputation, for monthly averages, no more than this. It is noteworthy that CLMX, ROME and KIEL stations present no missing data in the target period. This methodology allowed reconstructing 43 time series.

Algorithm for Compressing Time-Series Data

Science.gov (United States)

Hawkins, S. Edward, III; Darlington, Edward Hugo

2012-01-01

An algorithm based on Chebyshev polynomials effects lossy compression of time-series data or other one-dimensional data streams (e.g., spectral data) that are arranged in blocks for sequential transmission. The algorithm was developed for use in transmitting data from spacecraft scientific instruments to Earth stations. In spite of its lossy nature, the algorithm preserves the information needed for scientific analysis. The algorithm is computationally simple, yet compresses data streams by factors much greater than two. The algorithm is not restricted to spacecraft or scientific uses: it is applicable to time-series data in general. The algorithm can also be applied to general multidimensional data that have been converted to time-series data, a typical example being image data acquired by raster scanning. However, unlike most prior image-data-compression algorithms, this algorithm neither depends on nor exploits the two-dimensional spatial correlations that are generally present in images. In order to understand the essence of this compression algorithm, it is necessary to understand that the net effect of this algorithm and the associated decompression algorithm is to approximate the original stream of data as a sequence of finite series of Chebyshev polynomials. For the purpose of this algorithm, a block of data or interval of time for which a Chebyshev polynomial series is fitted to the original data is denoted a fitting interval. Chebyshev approximation has two properties that make it particularly effective for compressing serial data streams with minimal loss of scientific information: The errors associated with a Chebyshev approximation are nearly uniformly distributed over the fitting interval (this is known in the art as the "equal error property"); and the maximum deviations of the fitted Chebyshev polynomial from the original data have the smallest possible values (this is known in the art as the "min-max property").

Modeling of Volatility with Non-linear Time Series Model

OpenAIRE

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.

Layered Ensemble Architecture for Time Series Forecasting.

Science.gov (United States)

Rahman, Md Mustafizur; Islam, Md Monirul; Murase, Kazuyuki; Yao, Xin

2016-01-01

Time series forecasting (TSF) has been widely used in many application areas such as science, engineering, and finance. The phenomena generating time series are usually unknown and information available for forecasting is only limited to the past values of the series. It is, therefore, necessary to use an appropriate number of past values, termed lag, for forecasting. This paper proposes a layered ensemble architecture (LEA) for TSF problems. Our LEA consists of two layers, each of which uses an ensemble of multilayer perceptron (MLP) networks. While the first ensemble layer tries to find an appropriate lag, the second ensemble layer employs the obtained lag for forecasting. Unlike most previous work on TSF, the proposed architecture considers both accuracy and diversity of the individual networks in constructing an ensemble. LEA trains different networks in the ensemble by using different training sets with an aim of maintaining diversity among the networks. However, it uses the appropriate lag and combines the best trained networks to construct the ensemble. This indicates LEAs emphasis on accuracy of the networks. The proposed architecture has been tested extensively on time series data of neural network (NN)3 and NN5 competitions. It has also been tested on several standard benchmark time series data. In terms of forecasting accuracy, our experimental results have revealed clearly that LEA is better than other ensemble and nonensemble methods.

Photodissociation of NaH using time-dependent Fourier grid method

Indian Academy of Sciences (India)

We have solved the time dependent Schrödinger equation by using the Chebyshev polynomial scheme and Fourier grid Hamiltonian method to calculate the dissociation cross section of NaH molecule by 1-photon absorption from the 1+ state to the 1 state. We have found that the results differ significantly from an ...

A window-based time series feature extraction method.

Science.gov (United States)

Katircioglu-Öztürk, Deniz; Güvenir, H Altay; Ravens, Ursula; Baykal, Nazife

2017-10-01

This study proposes a robust similarity score-based time series feature extraction method that is termed as Window-based Time series Feature ExtraCtion (WTC). Specifically, WTC generates domain-interpretable results and involves significantly low computational complexity thereby rendering itself useful for densely sampled and populated time series datasets. In this study, WTC is applied to a proprietary action potential (AP) time series dataset on human cardiomyocytes and three precordial leads from a publicly available electrocardiogram (ECG) dataset. This is followed by comparing WTC in terms of predictive accuracy and computational complexity with shapelet transform and fast shapelet transform (which constitutes an accelerated variant of the shapelet transform). The results indicate that WTC achieves a slightly higher classification performance with significantly lower execution time when compared to its shapelet-based alternatives. With respect to its interpretable features, WTC has a potential to enable medical experts to explore definitive common trends in novel datasets. Copyright © 2017 Elsevier Ltd. All rights reserved.

Prewhitening of hydroclimatic time series? Implications for inferred change and variability across time scales

Science.gov (United States)

Razavi, Saman; Vogel, Richard

2018-02-01

Prewhitening, the process of eliminating or reducing short-term stochastic persistence to enable detection of deterministic change, has been extensively applied to time series analysis of a range of geophysical variables. Despite the controversy around its utility, methodologies for prewhitening time series continue to be a critical feature of a variety of analyses including: trend detection of hydroclimatic variables and reconstruction of climate and/or hydrology through proxy records such as tree rings. With a focus on the latter, this paper presents a generalized approach to exploring the impact of a wide range of stochastic structures of short- and long-term persistence on the variability of hydroclimatic time series. Through this approach, we examine the impact of prewhitening on the inferred variability of time series across time scales. We document how a focus on prewhitened, residual time series can be misleading, as it can drastically distort (or remove) the structure of variability across time scales. Through examples with actual data, we show how such loss of information in prewhitened time series of tree rings (so-called "residual chronologies") can lead to the underestimation of extreme conditions in climate and hydrology, particularly droughts, reconstructed for centuries preceding the historical period.

Time series modeling and forecasting using memetic algorithms for regime-switching models.

Science.gov (United States)

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.

Passivity Enhancement of Grid-Tied Converters by Series LC-Filtered Active Damper

DEFF Research Database (Denmark)

Bai, Haofeng; Wang, Xiongfei; Loh, Poh Chiang

2017-01-01

in the literature. Its effect with parasitic capacitance present in the grid has also usually been ignored, even though it may bring new challenges to the active damper. To address these issues, passivity is applied to study the grid converter stability before the understanding gained is used for formulating...

DTW-APPROACH FOR UNCORRELATED MULTIVARIATE TIME SERIES IMPUTATION

OpenAIRE

Phan , Thi-Thu-Hong; Poisson Caillault , Emilie; Bigand , André; Lefebvre , Alain

2017-01-01

International audience; Missing data are inevitable in almost domains of applied sciences. Data analysis with missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). Some well-known methods for multivariate time series imputation require high correlations between series or their features. In this paper , we propose an approach based on the shape-behaviour relation in low/un-correlated multivariate time series under an assumption of...

Non-Gaussian power grid frequency fluctuations characterized by Lévy-stable laws and superstatistics

Science.gov (United States)

Schäfer, Benjamin; Beck, Christian; Aihara, Kazuyuki; Witthaut, Dirk; Timme, Marc

2018-02-01

Multiple types of fluctuations impact the collective dynamics of power grids and thus challenge their robust operation. Fluctuations result from processes as different as dynamically changing demands, energy trading and an increasing share of renewable power feed-in. Here we analyse principles underlying the dynamics and statistics of power grid frequency fluctuations. Considering frequency time series for a range of power grids, including grids in North America, Japan and Europe, we find a strong deviation from Gaussianity best described as Lévy-stable and q-Gaussian distributions. We present a coarse framework to analytically characterize the impact of arbitrary noise distributions, as well as a superstatistical approach that systematically interprets heavy tails and skewed distributions. We identify energy trading as a substantial contribution to today's frequency fluctuations and effective damping of the grid as a controlling factor enabling reduction of fluctuation risks, with enhanced effects for small power grids.

MICROARRAY IMAGE GRIDDING USING GRID LINE REFINEMENT TECHNIQUE

Directory of Open Access Journals (Sweden)

V.G. Biju

2015-05-01

Full Text Available An important stage in microarray image analysis is gridding. Microarray image gridding is done to locate sub arrays in a microarray image and find co-ordinates of spots within each sub array. For accurate identification of spots, most of the proposed gridding methods require human intervention. In this paper a fully automatic gridding method which enhances spot intensity in the preprocessing step as per a histogram based threshold method is used. The gridding step finds co-ordinates of spots from horizontal and vertical profile of the image. To correct errors due to the grid line placement, a grid line refinement technique is proposed. The algorithm is applied on different image databases and results are compared based on spot detection accuracy and time. An average spot detection accuracy of 95.06% depicts the proposed method’s flexibility and accuracy in finding the spot co-ordinates for different database images.

IBM announces global Grid computing solutions for banking, financial markets

CERN Multimedia

2003-01-01

"IBM has announced a series of Grid projects around the world as part of its Grid computing program. They include IBM new Grid-based product offerings with business intelligence software provider SAS and other partners that address the computer-intensive needs of the banking and financial markets industry (1 page)."

Variable Selection in Time Series Forecasting Using Random Forests

Directory of Open Access Journals (Sweden)

Hristos Tyralis

2017-10-01

Full Text Available Time series forecasting using machine learning algorithms has gained popularity recently. Random forest is a machine learning algorithm implemented in time series forecasting; however, most of its forecasting properties have remained unexplored. Here we focus on assessing the performance of random forests in one-step forecasting using two large datasets of short time series with the aim to suggest an optimal set of predictor variables. Furthermore, we compare its performance to benchmarking methods. The first dataset is composed by 16,000 simulated time series from a variety of Autoregressive Fractionally Integrated Moving Average (ARFIMA models. The second dataset consists of 135 mean annual temperature time series. The highest predictive performance of RF is observed when using a low number of recent lagged predictor variables. This outcome could be useful in relevant future applications, with the prospect to achieve higher predictive accuracy.

Trend time-series modeling and forecasting with neural networks.

Science.gov (United States)

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.

Segmentation of Nonstationary Time Series with Geometric Clustering

DEFF Research Database (Denmark)

Bocharov, Alexei; Thiesson, Bo

2013-01-01

We introduce a non-parametric method for segmentation in regimeswitching time-series models. The approach is based on spectral clustering of target-regressor tuples and derives a switching regression tree, where regime switches are modeled by oblique splits. Such models can be learned efficiently...... from data, where clustering is used to propose one single split candidate at each split level. We use the class of ART time series models to serve as illustration, but because of the non-parametric nature of our segmentation approach, it readily generalizes to a wide range of time-series models that go...

Non-parametric characterization of long-term rainfall time series

Science.gov (United States)

Tiwari, Harinarayan; Pandey, Brij Kishor

2018-03-01

The statistical study of rainfall time series is one of the approaches for efficient hydrological system design. Identifying, and characterizing long-term rainfall time series could aid in improving hydrological systems forecasting. In the present study, eventual statistics was applied for the long-term (1851-2006) rainfall time series under seven meteorological regions of India. Linear trend analysis was carried out using Mann-Kendall test for the observed rainfall series. The observed trend using the above-mentioned approach has been ascertained using the innovative trend analysis method. Innovative trend analysis has been found to be a strong tool to detect the general trend of rainfall time series. Sequential Mann-Kendall test has also been carried out to examine nonlinear trends of the series. The partial sum of cumulative deviation test is also found to be suitable to detect the nonlinear trend. Innovative trend analysis, sequential Mann-Kendall test and partial cumulative deviation test have potential to detect the general as well as nonlinear trend for the rainfall time series. Annual rainfall analysis suggests that the maximum changes in mean rainfall is 11.53% for West Peninsular India, whereas the maximum fall in mean rainfall is 7.8% for the North Mountainous Indian region. The innovative trend analysis method is also capable of finding the number of change point available in the time series. Additionally, we have performed von Neumann ratio test and cumulative deviation test to estimate the departure from homogeneity. Singular spectrum analysis has been applied in this study to evaluate the order of departure from homogeneity in the rainfall time series. Monsoon season (JS) of North Mountainous India and West Peninsular India zones has higher departure from homogeneity and singular spectrum analysis shows the results to be in coherence with the same.

Time Series Decomposition into Oscillation Components and Phase Estimation.

Science.gov (United States)

Matsuda, Takeru; Komaki, Fumiyasu

2017-02-01

Many time series are naturally considered as a superposition of several oscillation components. For example, electroencephalogram (EEG) time series include oscillation components such as alpha, beta, and gamma. We propose a method for decomposing time series into such oscillation components using state-space models. Based on the concept of random frequency modulation, gaussian linear state-space models for oscillation components are developed. In this model, the frequency of an oscillator fluctuates by noise. Time series decomposition is accomplished by this model like the Bayesian seasonal adjustment method. Since the model parameters are estimated from data by the empirical Bayes' method, the amplitudes and the frequencies of oscillation components are determined in a data-driven manner. Also, the appropriate number of oscillation components is determined with the Akaike information criterion (AIC). In this way, the proposed method provides a natural decomposition of the given time series into oscillation components. In neuroscience, the phase of neural time series plays an important role in neural information processing. The proposed method can be used to estimate the phase of each oscillation component and has several advantages over a conventional method based on the Hilbert transform. Thus, the proposed method enables an investigation of the phase dynamics of time series. Numerical results show that the proposed method succeeds in extracting intermittent oscillations like ripples and detecting the phase reset phenomena. We apply the proposed method to real data from various fields such as astronomy, ecology, tidology, and neuroscience.

Introduction to time series analysis and forecasting

CERN Document Server

Montgomery, Douglas C; Kulahci, Murat

2015-01-01

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

Multi-Scale Dissemination of Time Series Data

DEFF Research Database (Denmark)

Guo, Qingsong; Zhou, Yongluan; Su, Li

2013-01-01

In this paper, we consider the problem of continuous dissemination of time series data, such as sensor measurements, to a large number of subscribers. These subscribers fall into multiple subscription levels, where each subscription level is specified by the bandwidth constraint of a subscriber......, which is an abstract indicator for both the physical limits and the amount of data that the subscriber would like to handle. To handle this problem, we propose a system framework for multi-scale time series data dissemination that employs a typical tree-based dissemination network and existing time...

High Quality Data for Grid Integration Studies

Energy Technology Data Exchange (ETDEWEB)

Clifton, Andrew; Draxl, Caroline; Sengupta, Manajit; Hodge, Bri-Mathias

2017-01-22

As variable renewable power penetration levels increase in power systems worldwide, renewable integration studies are crucial to ensure continued economic and reliable operation of the power grid. The existing electric grid infrastructure in the US in particular poses significant limitations on wind power expansion. In this presentation we will shed light on requirements for grid integration studies as far as wind and solar energy are concerned. Because wind and solar plants are strongly impacted by weather, high-resolution and high-quality weather data are required to drive power system simulations. Future data sets will have to push limits of numerical weather prediction to yield these high-resolution data sets, and wind data will have to be time-synchronized with solar data. Current wind and solar integration data sets are presented. The Wind Integration National Dataset (WIND) Toolkit is the largest and most complete grid integration data set publicly available to date. A meteorological data set, wind power production time series, and simulated forecasts created using the Weather Research and Forecasting Model run on a 2-km grid over the continental United States at a 5-min resolution is now publicly available for more than 126,000 land-based and offshore wind power production sites. The National Solar Radiation Database (NSRDB) is a similar high temporal- and spatial resolution database of 18 years of solar resource data for North America and India. The need for high-resolution weather data pushes modeling towards finer scales and closer synchronization. We also present how we anticipate such datasets developing in the future, their benefits, and the challenges with using and disseminating such large amounts of data.

RADON CONCENTRATION TIME SERIES MODELING AND APPLICATION DISCUSSION.

Science.gov (United States)

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.

Similarity estimators for irregular and age uncertain time series

Science.gov (United States)

Rehfeld, K.; Kurths, J.

2013-09-01

Paleoclimate time series are often irregularly sampled and age uncertain, which is an important technical challenge to overcome for successful reconstruction of past climate variability and dynamics. Visual comparison and interpolation-based linear correlation approaches have been used to infer dependencies from such proxy time series. While the first is subjective, not measurable and not suitable for the comparison of many datasets at a time, the latter introduces interpolation bias, and both face difficulties if the underlying dependencies are nonlinear. In this paper we investigate similarity estimators that could be suitable for the quantitative investigation of dependencies in irregular and age uncertain time series. We compare the Gaussian-kernel based cross correlation (gXCF, Rehfeld et al., 2011) and mutual information (gMI, Rehfeld et al., 2013) against their interpolation-based counterparts and the new event synchronization function (ESF). We test the efficiency of the methods in estimating coupling strength and coupling lag numerically, using ensembles of synthetic stalagmites with short, autocorrelated, linear and nonlinearly coupled proxy time series, and in the application to real stalagmite time series. In the linear test case coupling strength increases are identified consistently for all estimators, while in the nonlinear test case the correlation-based approaches fail. The lag at which the time series are coupled is identified correctly as the maximum of the similarity functions in around 60-55% (in the linear case) to 53-42% (for the nonlinear processes) of the cases when the dating of the synthetic stalagmite is perfectly precise. If the age uncertainty increases beyond 5% of the time series length, however, the true coupling lag is not identified more often than the others for which the similarity function was estimated. Age uncertainty contributes up to half of the uncertainty in the similarity estimation process. Time series irregularity

Similarity estimators for irregular and age-uncertain time series

Science.gov (United States)

Rehfeld, K.; Kurths, J.

2014-01-01

Paleoclimate time series are often irregularly sampled and age uncertain, which is an important technical challenge to overcome for successful reconstruction of past climate variability and dynamics. Visual comparison and interpolation-based linear correlation approaches have been used to infer dependencies from such proxy time series. While the first is subjective, not measurable and not suitable for the comparison of many data sets at a time, the latter introduces interpolation bias, and both face difficulties if the underlying dependencies are nonlinear. In this paper we investigate similarity estimators that could be suitable for the quantitative investigation of dependencies in irregular and age-uncertain time series. We compare the Gaussian-kernel-based cross-correlation (gXCF, Rehfeld et al., 2011) and mutual information (gMI, Rehfeld et al., 2013) against their interpolation-based counterparts and the new event synchronization function (ESF). We test the efficiency of the methods in estimating coupling strength and coupling lag numerically, using ensembles of synthetic stalagmites with short, autocorrelated, linear and nonlinearly coupled proxy time series, and in the application to real stalagmite time series. In the linear test case, coupling strength increases are identified consistently for all estimators, while in the nonlinear test case the correlation-based approaches fail. The lag at which the time series are coupled is identified correctly as the maximum of the similarity functions in around 60-55% (in the linear case) to 53-42% (for the nonlinear processes) of the cases when the dating of the synthetic stalagmite is perfectly precise. If the age uncertainty increases beyond 5% of the time series length, however, the true coupling lag is not identified more often than the others for which the similarity function was estimated. Age uncertainty contributes up to half of the uncertainty in the similarity estimation process. Time series irregularity

Current Collecting Grids for ITO-Free Solar Cells

DEFF Research Database (Denmark)

Galagan, Yulia; Zimmermann, Birger; Coenen, Erica W. C.

2012-01-01

Indium-tin-oxide (ITO) free polymer solar cells prepared by ink jet printing a composite front electrode comprising silver grid lines and a semitransparent PEDOT:PSS conductor are demonstrated. The effect of grid line density is explored for a large series of devices and a careful modeling study...

Robust Forecasting of Non-Stationary Time Series

OpenAIRE

Croux, C.; Fried, R.; Gijbels, I.; Mahieu, K.

2010-01-01

This paper proposes a robust forecasting method for non-stationary time series. The time series is modelled using non-parametric heteroscedastic regression, and fitted by a localized MM-estimator, combining high robustness and large efficiency. The proposed method is shown to produce reliable forecasts in the presence of outliers, non-linearity, and heteroscedasticity. In the absence of outliers, the forecasts are only slightly less precise than those based on a localized Least Squares estima...

The Grid

CERN Document Server

Klotz, Wolf-Dieter

2005-01-01

Grid technology is widely emerging. Grid computing, most simply stated, is distributed computing taken to the next evolutionary level. The goal is to create the illusion of a simple, robust yet large and powerful self managing virtual computer out of a large collection of connected heterogeneous systems sharing various combinations of resources. This talk will give a short history how, out of lessons learned from the Internet, the vision of Grids was born. Then the extensible anatomy of a Grid architecture will be discussed. The talk will end by presenting a selection of major Grid projects in Europe and US and if time permits a short on-line demonstration.

Time Series Econometrics for the 21st Century

Science.gov (United States)

Hansen, Bruce E.

2017-01-01

The field of econometrics largely started with time series analysis because many early datasets were time-series macroeconomic data. As the field developed, more cross-sectional and longitudinal datasets were collected, which today dominate the majority of academic empirical research. In nonacademic (private sector, central bank, and governmental)…

Effectiveness of firefly algorithm based neural network in time series ...

African Journals Online (AJOL)

Effectiveness of firefly algorithm based neural network in time series forecasting. ... In the experiments, three well known time series were used to evaluate the performance. Results obtained were compared with ... Keywords: Time series, Artificial Neural Network, Firefly Algorithm, Particle Swarm Optimization, Overfitting ...

Time Series Analysis of Insar Data: Methods and Trends

Science.gov (United States)

Osmanoglu, Batuhan; Sunar, Filiz; Wdowinski, Shimon; Cano-Cabral, Enrique

2015-01-01

Time series analysis of InSAR data has emerged as an important tool for monitoring and measuring the displacement of the Earth's surface. Changes in the Earth's surface can result from a wide range of phenomena such as earthquakes, volcanoes, landslides, variations in ground water levels, and changes in wetland water levels. Time series analysis is applied to interferometric phase measurements, which wrap around when the observed motion is larger than one-half of the radar wavelength. Thus, the spatio-temporal ''unwrapping" of phase observations is necessary to obtain physically meaningful results. Several different algorithms have been developed for time series analysis of InSAR data to solve for this ambiguity. These algorithms may employ different models for time series analysis, but they all generate a first-order deformation rate, which can be compared to each other. However, there is no single algorithm that can provide optimal results in all cases. Since time series analyses of InSAR data are used in a variety of applications with different characteristics, each algorithm possesses inherently unique strengths and weaknesses. In this review article, following a brief overview of InSAR technology, we discuss several algorithms developed for time series analysis of InSAR data using an example set of results for measuring subsidence rates in Mexico City.

Interpretation of a compositional time series

Science.gov (United States)

Tolosana-Delgado, R.; van den Boogaart, K. G.

2012-04-01

Common methods for multivariate time series analysis use linear operations, from the definition of a time-lagged covariance/correlation to the prediction of new outcomes. However, when the time series response is a composition (a vector of positive components showing the relative importance of a set of parts in a total, like percentages and proportions), then linear operations are afflicted of several problems. For instance, it has been long recognised that (auto/cross-)correlations between raw percentages are spurious, more dependent on which other components are being considered than on any natural link between the components of interest. Also, a long-term forecast of a composition in models with a linear trend will ultimately predict negative components. In general terms, compositional data should not be treated in a raw scale, but after a log-ratio transformation (Aitchison, 1986: The statistical analysis of compositional data. Chapman and Hill). This is so because the information conveyed by a compositional data is relative, as stated in their definition. The principle of working in coordinates allows to apply any sort of multivariate analysis to a log-ratio transformed composition, as long as this transformation is invertible. This principle is of full application to time series analysis. We will discuss how results (both auto/cross-correlation functions and predictions) can be back-transformed, viewed and interpreted in a meaningful way. One view is to use the exhaustive set of all possible pairwise log-ratios, which allows to express the results into D(D - 1)/2 separate, interpretable sets of one-dimensional models showing the behaviour of each possible pairwise log-ratios. Another view is the interpretation of estimated coefficients or correlations back-transformed in terms of compositions. These two views are compatible and complementary. These issues are illustrated with time series of seasonal precipitation patterns at different rain gauges of the USA

Capturing Structure Implicitly from Time-Series having Limited Data

OpenAIRE

Emaasit, Daniel; Johnson, Matthew

2018-01-01

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

Self-affinity in the dengue fever time series

Science.gov (United States)

Azevedo, S. M.; Saba, H.; Miranda, J. G. V.; Filho, A. S. Nascimento; Moret, M. A.

2016-06-01

Dengue is a complex public health problem that is common in tropical and subtropical regions. This disease has risen substantially in the last three decades, and the physical symptoms depict the self-affine behavior of the occurrences of reported dengue cases in Bahia, Brazil. This study uses detrended fluctuation analysis (DFA) to verify the scale behavior in a time series of dengue cases and to evaluate the long-range correlations that are characterized by the power law α exponent for different cities in Bahia, Brazil. The scaling exponent (α) presents different long-range correlations, i.e. uncorrelated, anti-persistent, persistent and diffusive behaviors. The long-range correlations highlight the complex behavior of the time series of this disease. The findings show that there are two distinct types of scale behavior. In the first behavior, the time series presents a persistent α exponent for a one-month period. For large periods, the time series signal approaches subdiffusive behavior. The hypothesis of the long-range correlations in the time series of the occurrences of reported dengue cases was validated. The observed self-affinity is useful as a forecasting tool for future periods through extrapolation of the α exponent behavior. This complex system has a higher predictability in a relatively short time (approximately one month), and it suggests a new tool in epidemiological control strategies. However, predictions for large periods using DFA are hidden by the subdiffusive behavior.

SAGE - MULTIDIMENSIONAL SELF-ADAPTIVE GRID CODE

Science.gov (United States)

Davies, C. B.

1994-01-01

SAGE, Self Adaptive Grid codE, is a flexible tool for adapting and restructuring both 2D and 3D grids. Solution-adaptive grid methods are useful tools for efficient and accurate flow predictions. In supersonic and hypersonic flows, strong gradient regions such as shocks, contact discontinuities, shear layers, etc., require careful distribution of grid points to minimize grid error and produce accurate flow-field predictions. SAGE helps the user obtain more accurate solutions by intelligently redistributing (i.e. adapting) the original grid points based on an initial or interim flow-field solution. The user then computes a new solution using the adapted grid as input to the flow solver. The adaptive-grid methodology poses the problem in an algebraic, unidirectional manner for multi-dimensional adaptations. The procedure is analogous to applying tension and torsion spring forces proportional to the local flow gradient at every grid point and finding the equilibrium position of the resulting system of grid points. The multi-dimensional problem of grid adaption is split into a series of one-dimensional problems along the computational coordinate lines. The reduced one dimensional problem then requires a tridiagonal solver to find the location of grid points along a coordinate line. Multi-directional adaption is achieved by the sequential application of the method in each coordinate direction. The tension forces direct the redistribution of points to the strong gradient region. To maintain smoothness and a measure of orthogonality of grid lines, torsional forces are introduced that relate information between the family of lines adjacent to one another. The smoothness and orthogonality constraints are direction-dependent, since they relate only the coordinate lines that are being adapted to the neighboring lines that have already been adapted. Therefore the solutions are non-unique and depend on the order and direction of adaption. Non-uniqueness of the adapted grid is

The LTDP ALTS Project: Contributing to the Continued Understanding and Exploitation of the ATSR Time Series

Science.gov (United States)

Clarke, Hannah; Done, Fay; Casadio, Stefano; Mackin, Stephen; Dinelli, Bianca Maria; Castelli, Elisa

2016-08-01

The long time-series of observations made by the Along Track Scanning Radiometers (ATSR) missions represents a valuable resource for a wide range of research and EO applications.With the advent of ESA's Long-TermData Preservation (LTDP) programme, thought has turned to the preservation and improved understanding of such long time-series, to support their continued exploitation in both existing and new areas of research, bringing the possibility of improving the existing data set and to inform and contribute towards future missions. For this reason, the 'Long Term Stability of the ATSR Instrument Series: SWIR Calibration, Cloud Masking and SAA' project, commonly known as the ATSR Long Term Stability (or ALTS) project, is designed to explore the key characteristics of the data set and new and innovative ways of enhancing and exploiting it.Work has focussed on: A new approach to the assessment of Short Wave Infra-Red (SWIR) channel calibration.; Developmentof a new method for Total Column Water Vapour (TCWV) retrieval.; Study of the South Atlantic Anomaly (SAA).; Radiative Transfer (RT) modelling for ATSR.; Providing AATSR observations with their location in the original instrument grid.; Strategies for the retrieval and archiving of historical ATSR documentation.; Study of TCWV retrieval over land; Development of new methods for cloud masking This paper provides an overview of these activities and illustrates the importance of preserving and understanding 'old' data for continued use in the future.

On the plurality of times: disunified time and the A-series | Nefdt ...

African Journals Online (AJOL)

Then, I attempt to show that disunified time is a problem for a semantics based on the A-series since A-truthmakers are hard to come by in a universe of temporally disconnected time-series. Finally, I provide a novel argument showing that presentists should be particularly fearful of such a universe. South African Journal of ...

Time-series modeling of long-term weight self-monitoring data.

Science.gov (United States)

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.

Time series prediction of apple scab using meteorological ...

African Journals Online (AJOL)

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

Characterization of time series via Rényi complexity-entropy curves

Science.gov (United States)

Jauregui, M.; Zunino, L.; Lenzi, E. K.; Mendes, R. S.; Ribeiro, H. V.

2018-05-01

One of the most useful tools for distinguishing between chaotic and stochastic time series is the so-called complexity-entropy causality plane. This diagram involves two complexity measures: the Shannon entropy and the statistical complexity. Recently, this idea has been generalized by considering the Tsallis monoparametric generalization of the Shannon entropy, yielding complexity-entropy curves. These curves have proven to enhance the discrimination among different time series related to stochastic and chaotic processes of numerical and experimental nature. Here we further explore these complexity-entropy curves in the context of the Rényi entropy, which is another monoparametric generalization of the Shannon entropy. By combining the Rényi entropy with the proper generalization of the statistical complexity, we associate a parametric curve (the Rényi complexity-entropy curve) with a given time series. We explore this approach in a series of numerical and experimental applications, demonstrating the usefulness of this new technique for time series analysis. We show that the Rényi complexity-entropy curves enable the differentiation among time series of chaotic, stochastic, and periodic nature. In particular, time series of stochastic nature are associated with curves displaying positive curvature in a neighborhood of their initial points, whereas curves related to chaotic phenomena have a negative curvature; finally, periodic time series are represented by vertical straight lines.

Quantifying Selection with Pool-Seq Time Series Data.

Science.gov (United States)

Taus, Thomas; Futschik, Andreas; Schlötterer, Christian

2017-11-01

Allele frequency time series data constitute a powerful resource for unraveling mechanisms of adaptation, because the temporal dimension captures important information about evolutionary forces. In particular, Evolve and Resequence (E&R), the whole-genome sequencing of replicated experimentally evolving populations, is becoming increasingly popular. Based on computer simulations several studies proposed experimental parameters to optimize the identification of the selection targets. No such recommendations are available for the underlying parameters selection strength and dominance. Here, we introduce a highly accurate method to estimate selection parameters from replicated time series data, which is fast enough to be applied on a genome scale. Using this new method, we evaluate how experimental parameters can be optimized to obtain the most reliable estimates for selection parameters. We show that the effective population size (Ne) and the number of replicates have the largest impact. Because the number of time points and sequencing coverage had only a minor effect, we suggest that time series analysis is feasible without major increase in sequencing costs. We anticipate that time series analysis will become routine in E&R studies. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Transformation-cost time-series method for analyzing irregularly sampled data.

Science.gov (United States)

Ozken, Ibrahim; Eroglu, Deniz; Stemler, Thomas; Marwan, Norbert; Bagci, G Baris; Kurths, Jürgen

2015-06-01

Irregular sampling of data sets is one of the challenges often encountered in time-series analysis, since traditional methods cannot be applied and the frequently used interpolation approach can corrupt the data and bias the subsequence analysis. Here we present the TrAnsformation-Cost Time-Series (TACTS) method, which allows us to analyze irregularly sampled data sets without degenerating the quality of the data set. Instead of using interpolation we consider time-series segments and determine how close they are to each other by determining the cost needed to transform one segment into the following one. Using a limited set of operations-with associated costs-to transform the time series segments, we determine a new time series, that is our transformation-cost time series. This cost time series is regularly sampled and can be analyzed using standard methods. While our main interest is the analysis of paleoclimate data, we develop our method using numerical examples like the logistic map and the Rössler oscillator. The numerical data allows us to test the stability of our method against noise and for different irregular samplings. In addition we provide guidance on how to choose the associated costs based on the time series at hand. The usefulness of the TACTS method is demonstrated using speleothem data from the Secret Cave in Borneo that is a good proxy for paleoclimatic variability in the monsoon activity around the maritime continent.

Transformation-cost time-series method for analyzing irregularly sampled data

Science.gov (United States)

Ozken, Ibrahim; Eroglu, Deniz; Stemler, Thomas; Marwan, Norbert; Bagci, G. Baris; Kurths, Jürgen

2015-06-01

Irregular sampling of data sets is one of the challenges often encountered in time-series analysis, since traditional methods cannot be applied and the frequently used interpolation approach can corrupt the data and bias the subsequence analysis. Here we present the TrAnsformation-Cost Time-Series (TACTS) method, which allows us to analyze irregularly sampled data sets without degenerating the quality of the data set. Instead of using interpolation we consider time-series segments and determine how close they are to each other by determining the cost needed to transform one segment into the following one. Using a limited set of operations—with associated costs—to transform the time series segments, we determine a new time series, that is our transformation-cost time series. This cost time series is regularly sampled and can be analyzed using standard methods. While our main interest is the analysis of paleoclimate data, we develop our method using numerical examples like the logistic map and the Rössler oscillator. The numerical data allows us to test the stability of our method against noise and for different irregular samplings. In addition we provide guidance on how to choose the associated costs based on the time series at hand. The usefulness of the TACTS method is demonstrated using speleothem data from the Secret Cave in Borneo that is a good proxy for paleoclimatic variability in the monsoon activity around the maritime continent.

A multidisciplinary database for geophysical time series management

Science.gov (United States)

Montalto, P.; Aliotta, M.; Cassisi, C.; Prestifilippo, M.; Cannata, A.

2013-12-01

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.

Real time hardware implementation of power converters for grid integration of distributed generation and STATCOM systems

Science.gov (United States)

Jaithwa, Ishan

Deployment of smart grid technologies is accelerating. Smart grid enables bidirectional flows of energy and energy-related communications. The future electricity grid will look very different from today's power system. Large variable renewable energy sources will provide a greater portion of electricity, small DERs and energy storage systems will become more common, and utilities will operate many different kinds of energy efficiency. All of these changes will add complexity to the grid and require operators to be able to respond to fast dynamic changes to maintain system stability and security. This thesis investigates advanced control technology for grid integration of renewable energy sources and STATCOM systems by verifying them on real time hardware experiments using two different systems: d SPACE and OPAL RT. Three controls: conventional, direct vector control and the intelligent Neural network control were first simulated using Matlab to check the stability and safety of the system and were then implemented on real time hardware using the d SPACE and OPAL RT systems. The thesis then shows how dynamic-programming (DP) methods employed to train the neural networks are better than any other controllers where, an optimal control strategy is developed to ensure effective power delivery and to improve system stability. Through real time hardware implementation it is proved that the neural vector control approach produces the fastest response time, low overshoot, and, the best performance compared to the conventional standard vector control method and DCC vector control technique. Finally the entrepreneurial approach taken to drive the technologies from the lab to market via ORANGE ELECTRIC is discussed in brief.

Modeling financial time series with S-plus

CERN Document Server

Zivot, Eric

2003-01-01

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

Application of Time Series Analysis in Determination of Lag Time in Jahanbin Basin

Directory of Open Access Journals (Sweden)

Seied Yahya Mirzaee

2005-11-01

    One of the important issues that have significant role in study of hydrology of basin is determination of lag time. Lag time has significant role in hydrological studies. Quantity of rainfall related lag time depends on several factors, such as permeability, vegetation cover, catchments slope, rainfall intensity, storm duration and type of rain. Determination of lag time is important parameter in many projects such as dam design and also water resource studies. Lag time of basin could be calculated using various methods. One of these methods is time series analysis of spectral density. The analysis is based on fouries series. The time series is approximated with Sinuous and Cosines functions. In this method harmonically significant quantities with individual frequencies are presented. Spectral density under multiple time series could be used to obtain basin lag time for annual runoff and short-term rainfall fluctuation. A long lag time could be due to snowmelt as well as melting ice due to rainfalls in freezing days. In this research the lag time of Jahanbin basin has been determined using spectral density method. The catchments is subjected to both rainfall and snowfall. For short term rainfall fluctuation with a return period  2, 3, 4 months, the lag times were found 0.18, 0.5 and 0.083 month, respectively.

Modeling Time Series Data for Supervised Learning

Science.gov (United States)

Baydogan, Mustafa Gokce

2012-01-01

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

Empirical method to measure stochasticity and multifractality in nonlinear time series

Science.gov (United States)

Lin, Chih-Hao; Chang, Chia-Seng; Li, Sai-Ping

2013-12-01

An empirical algorithm is used here to study the stochastic and multifractal nature of nonlinear time series. A parameter can be defined to quantitatively measure the deviation of the time series from a Wiener process so that the stochasticity of different time series can be compared. The local volatility of the time series under study can be constructed using this algorithm, and the multifractal structure of the time series can be analyzed by using this local volatility. As an example, we employ this method to analyze financial time series from different stock markets. The result shows that while developed markets evolve very much like an Ito process, the emergent markets are far from efficient. Differences about the multifractal structures and leverage effects between developed and emergent markets are discussed. The algorithm used here can be applied in a similar fashion to study time series of other complex systems.

Clinical time series prediction: Toward a hierarchical dynamical system framework.

Science.gov (United States)

Liu, Zitao; Hauskrecht, Milos

2015-09-01

Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. We tested our framework by first learning the time series model from data for the patients in the training set, and then using it to predict future time series values for the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive performance. Copyright © 2014 Elsevier B.V. All rights reserved.

Clinical time series prediction: towards a hierarchical dynamical system framework

Science.gov (United States)

Liu, Zitao; Hauskrecht, Milos

2014-01-01

Objective Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Materials and methods Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. Results We tested our framework by first learning the time series model from data for the patient in the training set, and then applying the model in order to predict future time series values on the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. Conclusion A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive

Turbulencelike Behavior of Seismic Time Series

International Nuclear Information System (INIS)

Manshour, P.; Saberi, S.; Sahimi, Muhammad; Peinke, J.; Pacheco, Amalio F.; Rahimi Tabar, M. Reza

2009-01-01

We report on a stochastic analysis of Earth's vertical velocity time series by using methods originally developed for complex hierarchical systems and, in particular, for turbulent flows. Analysis of the fluctuations of the detrended increments of the series reveals a pronounced transition in their probability density function from Gaussian to non-Gaussian. The transition occurs 5-10 hours prior to a moderate or large earthquake, hence representing a new and reliable precursor for detecting such earthquakes

Characterizing time series: when Granger causality triggers complex networks

Science.gov (United States)

Ge, Tian; Cui, Yindong; Lin, Wei; Kurths, Jürgen; Liu, Chong

2012-08-01

In this paper, we propose a new approach to characterize time series with noise perturbations in both the time and frequency domains by combining Granger causality and complex networks. We construct directed and weighted complex networks from time series and use representative network measures to describe their physical and topological properties. Through analyzing the typical dynamical behaviors of some physical models and the MIT-BIHMassachusetts Institute of Technology-Beth Israel Hospital. human electrocardiogram data sets, we show that the proposed approach is able to capture and characterize various dynamics and has much potential for analyzing real-world time series of rather short length.

Characterizing time series: when Granger causality triggers complex networks

International Nuclear Information System (INIS)

Ge Tian; Cui Yindong; Lin Wei; Liu Chong; Kurths, Jürgen

2012-01-01

In this paper, we propose a new approach to characterize time series with noise perturbations in both the time and frequency domains by combining Granger causality and complex networks. We construct directed and weighted complex networks from time series and use representative network measures to describe their physical and topological properties. Through analyzing the typical dynamical behaviors of some physical models and the MIT-BIH human electrocardiogram data sets, we show that the proposed approach is able to capture and characterize various dynamics and has much potential for analyzing real-world time series of rather short length. (paper)

Challenge Online Time Series Clustering For Demand Response A Theory to Break the ‘Curse of Dimensionality'

Energy Technology Data Exchange (ETDEWEB)

Pal, Ranjan [Univ. of Southern California, Los Angeles, CA (United States); Chelmis, Charalampos [Univ. of Southern California, Los Angeles, CA (United States); Aman, Saima [Univ. of Southern California, Los Angeles, CA (United States); Frincu, Marc [Univ. of Southern California, Los Angeles, CA (United States); Prasanna, Viktor [Univ. of Southern California, Los Angeles, CA (United States)

2015-07-15

The advent of smart meters and advanced communication infrastructures catalyzes numerous smart grid applications such as dynamic demand response, and paves the way to solve challenging research problems in sustainable energy consumption. The space of solution possibilities are restricted primarily by the huge amount of generated data requiring considerable computational resources and efficient algorithms. To overcome this Big Data challenge, data clustering techniques have been proposed. Current approaches however do not scale in the face of the “increasing dimensionality” problem where a cluster point is represented by the entire customer consumption time series. To overcome this aspect we first rethink the way cluster points are created and designed, and then design an efficient online clustering technique for demand response (DR) in order to analyze high volume, high dimensional energy consumption time series data at scale, and on the fly. Our online algorithm is randomized in nature, and provides optimal performance guarantees in a computationally efficient manner. Unlike prior work we (i) study the consumption properties of the whole population simultaneously rather than developing individual models for each customer separately, claiming it to be a ‘killer’ approach that breaks the “curse of dimensionality” in online time series clustering, and (ii) provide tight performance guarantees in theory to validate our approach. Our insights are driven by the field of sociology, where collective behavior often emerges as the result of individual patterns and lifestyles.

Multivariate time series analysis with R and financial applications

CERN Document Server

Tsay, Ruey S

2013-01-01

Since the publication of his first book, Analysis of Financial Time Series, Ruey Tsay has become one of the most influential and prominent experts on the topic of time series. Different from the traditional and oftentimes complex approach to multivariate (MV) time series, this sequel book emphasizes structural specification, which results in simplified parsimonious VARMA modeling and, hence, eases comprehension. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-worl

Measurements of spatial population synchrony: influence of time series transformations.

Science.gov (United States)

Chevalier, Mathieu; Laffaille, Pascal; Ferdy, Jean-Baptiste; Grenouillet, Gaël

2015-09-01

Two mechanisms have been proposed to explain spatial population synchrony: dispersal among populations, and the spatial correlation of density-independent factors (the "Moran effect"). To identify which of these two mechanisms is driving spatial population synchrony, time series transformations (TSTs) of abundance data have been used to remove the signature of one mechanism, and highlight the effect of the other. However, several issues with TSTs remain, and to date no consensus has emerged about how population time series should be handled in synchrony studies. Here, by using 3131 time series involving 34 fish species found in French rivers, we computed several metrics commonly used in synchrony studies to determine whether a large-scale climatic factor (temperature) influenced fish population dynamics at the regional scale, and to test the effect of three commonly used TSTs (detrending, prewhitening and a combination of both) on these metrics. We also tested whether the influence of TSTs on time series and population synchrony levels was related to the features of the time series using both empirical and simulated time series. For several species, and regardless of the TST used, we evidenced a Moran effect on freshwater fish populations. However, these results were globally biased downward by TSTs which reduced our ability to detect significant signals. Depending on the species and the features of the time series, we found that TSTs could lead to contradictory results, regardless of the metric considered. Finally, we suggest guidelines on how population time series should be processed in synchrony studies.

Desert locust populations, rainfall and climate change: insights from phenomenological models using gridded monthly data

OpenAIRE

Tratalos, Jamie A.; Cheke, Robert A.; Healey, Richard G.; Stenseth, Nils Chr.

2010-01-01

Using autocorrelation analysis and autoregressive integrated moving average (ARIMA)modelling, we analysed a time series of the monthly number of 1° grid squares infested with desert locust Schistocerca gregaria swarms throughout the geographical range of the species from 1930–1987. Statistically significant first- and higher-order autocorrelations were found in the series. Although endogenous components captured much of the variance, adding rainfall data improved endogenous ARIMA models and r...

Stochastic time series analysis of hydrology data for water resources

Science.gov (United States)

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

2017-11-01

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

Neural network versus classical time series forecasting models

Science.gov (United States)

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.

Nonlinear time series analysis of the human electrocardiogram

International Nuclear Information System (INIS)

Perc, Matjaz

2005-01-01

We analyse the human electrocardiogram with simple nonlinear time series analysis methods that are appropriate for graduate as well as undergraduate courses. In particular, attention is devoted to the notions of determinism and stationarity in physiological data. We emphasize that methods of nonlinear time series analysis can be successfully applied only if the studied data set originates from a deterministic stationary system. After positively establishing the presence of determinism and stationarity in the studied electrocardiogram, we calculate the maximal Lyapunov exponent, thus providing interesting insights into the dynamics of the human heart. Moreover, to facilitate interest and enable the integration of nonlinear time series analysis methods into the curriculum at an early stage of the educational process, we also provide user-friendly programs for each implemented method

Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis.

Science.gov (United States)

Wang, Jin; Sun, Xiangping; Nahavandi, Saeid; Kouzani, Abbas; Wu, Yuchuan; She, Mary

2014-11-01

Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Hidden Markov Models for Time Series An Introduction Using R

CERN Document Server

Zucchini, Walter

2009-01-01

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

Constructing ordinal partition transition networks from multivariate time series.

Science.gov (United States)

Zhang, Jiayang; Zhou, Jie; Tang, Ming; Guo, Heng; Small, Michael; Zou, Yong

2017-08-10

A growing number of algorithms have been proposed to map a scalar time series into ordinal partition transition networks. However, most observable phenomena in the empirical sciences are of a multivariate nature. We construct ordinal partition transition networks for multivariate time series. This approach yields weighted directed networks representing the pattern transition properties of time series in velocity space, which hence provides dynamic insights of the underling system. Furthermore, we propose a measure of entropy to characterize ordinal partition transition dynamics, which is sensitive to capturing the possible local geometric changes of phase space trajectories. We demonstrate the applicability of pattern transition networks to capture phase coherence to non-coherence transitions, and to characterize paths to phase synchronizations. Therefore, we conclude that the ordinal partition transition network approach provides complementary insight to the traditional symbolic analysis of nonlinear multivariate time series.

Permutation entropy of finite-length white-noise time series.

Science.gov (United States)

Little, Douglas J; Kane, Deb M

2016-08-01

Permutation entropy (PE) is commonly used to discriminate complex structure from white noise in a time series. While the PE of white noise is well understood in the long time-series limit, analysis in the general case is currently lacking. Here the expectation value and variance of white-noise PE are derived as functions of the number of ordinal pattern trials, N, and the embedding dimension, D. It is demonstrated that the probability distribution of the white-noise PE converges to a χ^{2} distribution with D!-1 degrees of freedom as N becomes large. It is further demonstrated that the PE variance for an arbitrary time series can be estimated as the variance of a related metric, the Kullback-Leibler entropy (KLE), allowing the qualitative N≫D! condition to be recast as a quantitative estimate of the N required to achieve a desired PE calculation precision. Application of this theory to statistical inference is demonstrated in the case of an experimentally obtained noise series, where the probability of obtaining the observed PE value was calculated assuming a white-noise time series. Standard statistical inference can be used to draw conclusions whether the white-noise null hypothesis can be accepted or rejected. This methodology can be applied to other null hypotheses, such as discriminating whether two time series are generated from different complex system states.

Multiresolution analysis of Bursa Malaysia KLCI time series

Science.gov (United States)

Ismail, Mohd Tahir; Dghais, Amel Abdoullah Ahmed

2017-05-01

In general, a time series is simply a sequence of numbers collected at regular intervals over a period. Financial time series data processing is concerned with the theory and practice of processing asset price over time, such as currency, commodity data, and stock market data. The primary aim of this study is to understand the fundamental characteristics of selected financial time series by using the time as well as the frequency domain analysis. After that prediction can be executed for the desired system for in sample forecasting. In this study, multiresolution analysis which the assist of discrete wavelet transforms (DWT) and maximal overlap discrete wavelet transform (MODWT) will be used to pinpoint special characteristics of Bursa Malaysia KLCI (Kuala Lumpur Composite Index) daily closing prices and return values. In addition, further case study discussions include the modeling of Bursa Malaysia KLCI using linear ARIMA with wavelets to address how multiresolution approach improves fitting and forecasting results.

Modelling bursty time series

International Nuclear Information System (INIS)

Vajna, Szabolcs; Kertész, János; Tóth, Bálint

2013-01-01

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

Quasi-static time-series simulation using OpenDSS in IEEE distribution feeder model with high PV penetration and its impact on solar forecasting

Science.gov (United States)

Mohammed, Touseef Ahmed Faisal

Distribution System Simulator developed by Electric Power Research Institute, to simulate grid voltage profile with a large scale PV system under quasi-static time series considering variations of PV output in seconds, minutes, and the average daily load variations. A 13 bus IEEE distribution feeder model is utilized with distributed residential and commercial scale PV at different buses for simulation studies. Time series simulations are discussed for various modes of operation considering dynamic PV penetration at different time periods in a day. In addition, this thesis demonstrates simulations taking into account the presence of moving cloud for solar forecasting studies.

Timing calibration and spectral cleaning of LOFAR time series data

NARCIS (Netherlands)

Corstanje, A.; Buitink, S.; Enriquez, J. E.; Falcke, H.; Horandel, J. R.; Krause, M.; Nelles, A.; Rachen, J. P.; Schellart, P.; Scholten, O.; ter Veen, S.; Thoudam, S.; Trinh, T. N. G.

We describe a method for spectral cleaning and timing calibration of short time series data of the voltage in individual radio interferometer receivers. It makes use of phase differences in fast Fourier transform (FFT) spectra across antenna pairs. For strong, localized terrestrial sources these are

Time series momentum and contrarian effects in the Chinese stock market

Science.gov (United States)

Shi, Huai-Long; Zhou, Wei-Xing

2017-10-01

This paper concentrates on the time series momentum or contrarian effects in the Chinese stock market. We evaluate the performance of the time series momentum strategy applied to major stock indices in mainland China and explore the relation between the performance of time series momentum strategies and some firm-specific characteristics. Our findings indicate that there is a time series momentum effect in the short run and a contrarian effect in the long run in the Chinese stock market. The performances of the time series momentum and contrarian strategies are highly dependent on the look-back and holding periods and firm-specific characteristics.

Time-Series Analysis: A Cautionary Tale

Science.gov (United States)

Damadeo, Robert

2015-01-01

Time-series analysis has often been a useful tool in atmospheric science for deriving long-term trends in various atmospherically important parameters (e.g., temperature or the concentration of trace gas species). In particular, time-series analysis has been repeatedly applied to satellite datasets in order to derive the long-term trends in stratospheric ozone, which is a critical atmospheric constituent. However, many of the potential pitfalls relating to the non-uniform sampling of the datasets were often ignored and the results presented by the scientific community have been unknowingly biased. A newly developed and more robust application of this technique is applied to the Stratospheric Aerosol and Gas Experiment (SAGE) II version 7.0 ozone dataset and the previous biases and newly derived trends are presented.

Characterizing interdependencies of multiple time series theory and applications

CERN Document Server

Hosoya, Yuzo; Takimoto, Taro; Kinoshita, Ryo

2017-01-01

This book introduces academic researchers and professionals to the basic concepts and methods for characterizing interdependencies of multiple time series in the frequency domain. Detecting causal directions between a pair of time series and the extent of their effects, as well as testing the non existence of a feedback relation between them, have constituted major focal points in multiple time series analysis since Granger introduced the celebrated definition of causality in view of prediction improvement. Causality analysis has since been widely applied in many disciplines. Although most analyses are conducted from the perspective of the time domain, a frequency domain method introduced in this book sheds new light on another aspect that disentangles the interdependencies between multiple time series in terms of long-term or short-term effects, quantitatively characterizing them. The frequency domain method includes the Granger noncausality test as a special case. Chapters 2 and 3 of the book introduce an i...

A perturbative approach for enhancing the performance of time series forecasting.

Science.gov (United States)

de Mattos Neto, Paulo S G; Ferreira, Tiago A E; Lima, Aranildo R; Vasconcelos, Germano C; Cavalcanti, George D C

2017-04-01

This paper proposes a method to perform time series prediction based on perturbation theory. The approach is based on continuously adjusting an initial forecasting model to asymptotically approximate a desired time series model. First, a predictive model generates an initial forecasting for a time series. Second, a residual time series is calculated as the difference between the original time series and the initial forecasting. If that residual series is not white noise, then it can be used to improve the accuracy of the initial model and a new predictive model is adjusted using residual series. The whole process is repeated until convergence or the residual series becomes white noise. The output of the method is then given by summing up the outputs of all trained predictive models in a perturbative sense. To test the method, an experimental investigation was conducted on six real world time series. A comparison was made with six other methods experimented and ten other results found in the literature. Results show that not only the performance of the initial model is significantly improved but also the proposed method outperforms the other results previously published. Copyright © 2017 Elsevier Ltd. All rights reserved.

Drunk driving detection based on classification of multivariate time series.

Science.gov (United States)

Li, Zhenlong; Jin, Xue; Zhao, Xiaohua

2015-09-01

This paper addresses the problem of detecting drunk driving based on classification of multivariate time series. First, driving performance measures were collected from a test in a driving simulator located in the Traffic Research Center, Beijing University of Technology. Lateral position and steering angle were used to detect drunk driving. Second, multivariate time series analysis was performed to extract the features. A piecewise linear representation was used to represent multivariate time series. A bottom-up algorithm was then employed to separate multivariate time series. The slope and time interval of each segment were extracted as the features for classification. Third, a support vector machine classifier was used to classify driver's state into two classes (normal or drunk) according to the extracted features. The proposed approach achieved an accuracy of 80.0%. Drunk driving detection based on the analysis of multivariate time series is feasible and effective. The approach has implications for drunk driving detection. Copyright © 2015 Elsevier Ltd and National Safety Council. All rights reserved.

Evaluation of scaling invariance embedded in short time series.

Directory of Open Access Journals (Sweden)

Xue Pan

Full Text Available Scaling invariance of time series has been making great contributions in diverse research fields. But how to evaluate scaling exponent from a real-world series is still an open problem. Finite length of time series may induce unacceptable fluctuation and bias to statistical quantities and consequent invalidation of currently used standard methods. In this paper a new concept called correlation-dependent balanced estimation of diffusion entropy is developed to evaluate scale-invariance in very short time series with length ~10(2. Calculations with specified Hurst exponent values of 0.2,0.3,...,0.9 show that by using the standard central moving average de-trending procedure this method can evaluate the scaling exponents for short time series with ignorable bias (≤0.03 and sharp confidential interval (standard deviation ≤0.05. Considering the stride series from ten volunteers along an approximate oval path of a specified length, we observe that though the averages and deviations of scaling exponents are close, their evolutionary behaviors display rich patterns. It has potential use in analyzing physiological signals, detecting early warning signals, and so on. As an emphasis, the our core contribution is that by means of the proposed method one can estimate precisely shannon entropy from limited records.

Evaluation of scaling invariance embedded in short time series.

Science.gov (United States)

Pan, Xue; Hou, Lei; Stephen, Mutua; Yang, Huijie; Zhu, Chenping

2014-01-01

Scaling invariance of time series has been making great contributions in diverse research fields. But how to evaluate scaling exponent from a real-world series is still an open problem. Finite length of time series may induce unacceptable fluctuation and bias to statistical quantities and consequent invalidation of currently used standard methods. In this paper a new concept called correlation-dependent balanced estimation of diffusion entropy is developed to evaluate scale-invariance in very short time series with length ~10(2). Calculations with specified Hurst exponent values of 0.2,0.3,...,0.9 show that by using the standard central moving average de-trending procedure this method can evaluate the scaling exponents for short time series with ignorable bias (≤0.03) and sharp confidential interval (standard deviation ≤0.05). Considering the stride series from ten volunteers along an approximate oval path of a specified length, we observe that though the averages and deviations of scaling exponents are close, their evolutionary behaviors display rich patterns. It has potential use in analyzing physiological signals, detecting early warning signals, and so on. As an emphasis, the our core contribution is that by means of the proposed method one can estimate precisely shannon entropy from limited records.

Modeling Non-Gaussian Time Series with Nonparametric Bayesian Model.

Science.gov (United States)

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.

Geomechanical time series and its singularity spectrum analysis

Czech Academy of Sciences Publication Activity Database

Lyubushin, Alexei A.; Kaláb, Zdeněk; Lednická, Markéta

2012-01-01

Roč. 47, č. 1 (2012), s. 69-77 ISSN 1217-8977 R&D Projects: GA ČR GA105/09/0089 Institutional research plan: CEZ:AV0Z30860518 Keywords : geomechanical time series * singularity spectrum * time series segmentation * laser distance meter Subject RIV: DC - Siesmology, Volcanology, Earth Structure Impact factor: 0.347, year: 2012 http://www.akademiai.com/content/88v4027758382225/fulltext.pdf

Pseudo-random bit generator based on lag time series

Science.gov (United States)

García-Martínez, M.; Campos-Cantón, E.

2014-12-01

In this paper, we present a pseudo-random bit generator (PRBG) based on two lag time series of the logistic map using positive and negative values in the bifurcation parameter. In order to hidden the map used to build the pseudo-random series we have used a delay in the generation of time series. These new series when they are mapped xn against xn+1 present a cloud of points unrelated to the logistic map. Finally, the pseudo-random sequences have been tested with the suite of NIST giving satisfactory results for use in stream ciphers.

Non-linear forecasting in high-frequency financial time series

Science.gov (United States)

Strozzi, F.; Zaldívar, J. M.

2005-08-01

A new methodology based on state space reconstruction techniques has been developed for trading in financial markets. The methodology has been tested using 18 high-frequency foreign exchange time series. The results are in apparent contradiction with the efficient market hypothesis which states that no profitable information about future movements can be obtained by studying the past prices series. In our (off-line) analysis positive gain may be obtained in all those series. The trading methodology is quite general and may be adapted to other financial time series. Finally, the steps for its on-line application are discussed.

Grid Architecture 2

Energy Technology Data Exchange (ETDEWEB)

Taft, Jeffrey D. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

2016-01-01

The report describes work done on Grid Architecture under the auspices of the Department of Electricity Office of Electricity Delivery and Reliability in 2015. As described in the first Grid Architecture report, the primary purpose of this work is to provide stakeholder insight about grid issues so as to enable superior decision making on their part. Doing this requires the creation of various work products, including oft-times complex diagrams, analyses, and explanations. This report provides architectural insights into several important grid topics and also describes work done to advance the science of Grid Architecture as well.

Analysis of JET ELMy time series

International Nuclear Information System (INIS)

Zvejnieks, G.; Kuzovkov, V.N.

2005-01-01

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

The Statistical Analysis of Time Series

CERN Document Server

Anderson, T W

2011-01-01

The Wiley Classics Library consists of selected books that have become recognized classics in their respective fields. With these new unabridged and inexpensive editions, Wiley hopes to extend the life of these important works by making them available to future generations of mathematicians and scientists. Currently available in the Series: T. W. Anderson Statistical Analysis of Time Series T. S. Arthanari & Yadolah Dodge Mathematical Programming in Statistics Emil Artin Geometric Algebra Norman T. J. Bailey The Elements of Stochastic Processes with Applications to the Natural Sciences George

Analysis of time series and size of equivalent sample

International Nuclear Information System (INIS)

Bernal, Nestor; Molina, Alicia; Pabon, Daniel; Martinez, Jorge

2004-01-01

In a meteorological context, a first approach to the modeling of time series is to use models of autoregressive type. This allows one to take into account the meteorological persistence or temporal behavior, thereby identifying the memory of the analyzed process. This article seeks to pre-sent the concept of the size of an equivalent sample, which helps to identify in the data series sub periods with a similar structure. Moreover, in this article we examine the alternative of adjusting the variance of the series, keeping in mind its temporal structure, as well as an adjustment to the covariance of two time series. This article presents two examples, the first one corresponding to seven simulated series with autoregressive structure of first order, and the second corresponding to seven meteorological series of anomalies of the air temperature at the surface in two Colombian regions

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...

AEP Ohio gridSMART Demonstration Project Real-Time Pricing Demonstration Analysis

Energy Technology Data Exchange (ETDEWEB)

Widergren, Steven E.; Subbarao, Krishnappa; Fuller, Jason C.; Chassin, David P.; Somani, Abhishek; Marinovici, Maria C.; Hammerstrom, Janelle L.

2014-02-01

This report contributes initial findings from an analysis of significant aspects of the gridSMART® Real-Time Pricing (RTP) – Double Auction demonstration project. Over the course of four years, Pacific Northwest National Laboratory (PNNL) worked with American Electric Power (AEP), Ohio and Battelle Memorial Institute to design, build, and operate an innovative system to engage residential consumers and their end-use resources in a participatory approach to electric system operations, an incentive-based approach that has the promise of providing greater efficiency under normal operating conditions and greater flexibility to react under situations of system stress. The material contained in this report supplements the findings documented by AEP Ohio in the main body of the gridSMART report. It delves into three main areas: impacts on system operations, impacts on households, and observations about the sensitivity of load to price changes.

Forecasting method for global radiation time series without training phase: Comparison with other well-known prediction methodologies

International Nuclear Information System (INIS)

Voyant, Cyril; Motte, Fabrice; Fouilloy, Alexis; Notton, Gilles; Paoli, Christophe; Nivet, Marie-Laure

2017-01-01

Integration of unpredictable renewable energy sources into electrical networks intensifies the complexity of the grid management due to their intermittent and unforeseeable nature. Because of the strong increase of solar power generation the prediction of solar yields becomes more and more important. Electrical operators need an estimation of the future production. For nowcasting and short term forecasting, the usual technics based on machine learning need large historical data sets of good quality during the training phase of predictors. However data are not always available and induce an advanced maintenance of meteorological stations, making the method inapplicable for poor instrumented or isolated sites. In this work, we propose intuitive methodologies based on the Kalman filter use (also known as linear quadratic estimation), able to predict a global radiation time series without the need of historical data. The accuracy of these methods is compared to other classical data driven methods, for different horizons of prediction and time steps. The proposed approach shows interesting capabilities allowing to improve quasi-systematically the prediction. For one to 10 h horizons Kalman model performances are competitive in comparison to more sophisticated models such as ANN which require both consistent historical data sets and computational resources. - Highlights: • Solar radiation forecasting with time series formalism. • Trainless approach compared to machine learning methods. • Very simple method dedicated to solar irradiation forecasting with high accuracy.

Nonparametric factor analysis of time series

OpenAIRE

Rodríguez-Poo, Juan M.; Linton, Oliver Bruce

1998-01-01

We introduce a nonparametric smoothing procedure for nonparametric factor analaysis of multivariate time series. The asymptotic properties of the proposed procedures are derived. We present an application based on the residuals from the Fair macromodel.

Time Series Outlier Detection Based on Sliding Window Prediction

Directory of Open Access Journals (Sweden)

Yufeng Yu

2014-01-01

Full Text Available In order to detect outliers in hydrological time series data for improving data quality and decision-making quality related to design, operation, and management of water resources, this research develops a time series outlier detection method for hydrologic data that can be used to identify data that deviate from historical patterns. The method first built a forecasting model on the history data and then used it to predict future values. Anomalies are assumed to take place if the observed values fall outside a given prediction confidence interval (PCI, which can be calculated by the predicted value and confidence coefficient. The use of PCI as threshold is mainly on the fact that it considers the uncertainty in the data series parameters in the forecasting model to address the suitable threshold selection problem. The method performs fast, incremental evaluation of data as it becomes available, scales to large quantities of data, and requires no preclassification of anomalies. Experiments with different hydrologic real-world time series showed that the proposed methods are fast and correctly identify abnormal data and can be used for hydrologic time series analysis.

Metagenomics meets time series analysis: unraveling microbial community dynamics

NARCIS (Netherlands)

Faust, K.; Lahti, L.M.; Gonze, D.; Vos, de W.M.; Raes, J.

2015-01-01

The recent increase in the number of microbial time series studies offers new insights into the stability and dynamics of microbial communities, from the world's oceans to human microbiota. Dedicated time series analysis tools allow taking full advantage of these data. Such tools can reveal periodic

Time series forecasting based on deep extreme learning machine

NARCIS (Netherlands)

Guo, Xuqi; Pang, Y.; Yan, Gaowei; Qiao, Tiezhu; Yang, Guang-Hong; Yang, Dan

2017-01-01

Multi-layer Artificial Neural Networks (ANN) has caught widespread attention as a new method for time series forecasting due to the ability of approximating any nonlinear function. In this paper, a new local time series prediction model is established with the nearest neighbor domain theory, in

False-nearest-neighbors algorithm and noise-corrupted time series

International Nuclear Information System (INIS)

Rhodes, C.; Morari, M.

1997-01-01

The false-nearest-neighbors (FNN) algorithm was originally developed to determine the embedding dimension for autonomous time series. For noise-free computer-generated time series, the algorithm does a good job in predicting the embedding dimension. However, the problem of predicting the embedding dimension when the time-series data are corrupted by noise was not fully examined in the original studies of the FNN algorithm. Here it is shown that with large data sets, even small amounts of noise can lead to incorrect prediction of the embedding dimension. Surprisingly, as the length of the time series analyzed by FNN grows larger, the cause of incorrect prediction becomes more pronounced. An analysis of the effect of noise on the FNN algorithm and a solution for dealing with the effects of noise are given here. Some results on the theoretically correct choice of the FNN threshold are also presented. copyright 1997 The American Physical Society

CauseMap: fast inference of causality from complex time series.

Science.gov (United States)

Maher, M Cyrus; Hernandez, Ryan D

2015-01-01

Background. Establishing health-related causal relationships is a central pursuit in biomedical research. Yet, the interdependent non-linearity of biological systems renders causal dynamics laborious and at times impractical to disentangle. This pursuit is further impeded by the dearth of time series that are sufficiently long to observe and understand recurrent patterns of flux. However, as data generation costs plummet and technologies like wearable devices democratize data collection, we anticipate a coming surge in the availability of biomedically-relevant time series data. Given the life-saving potential of these burgeoning resources, it is critical to invest in the development of open source software tools that are capable of drawing meaningful insight from vast amounts of time series data. Results. Here we present CauseMap, the first open source implementation of convergent cross mapping (CCM), a method for establishing causality from long time series data (≳25 observations). Compared to existing time series methods, CCM has the advantage of being model-free and robust to unmeasured confounding that could otherwise induce spurious associations. CCM builds on Takens' Theorem, a well-established result from dynamical systems theory that requires only mild assumptions. This theorem allows us to reconstruct high dimensional system dynamics using a time series of only a single variable. These reconstructions can be thought of as shadows of the true causal system. If reconstructed shadows can predict points from opposing time series, we can infer that the corresponding variables are providing views of the same causal system, and so are causally related. Unlike traditional metrics, this test can establish the directionality of causation, even in the presence of feedback loops. Furthermore, since CCM can extract causal relationships from times series of, e.g., a single individual, it may be a valuable tool to personalized medicine. We implement CCM in Julia, a

CauseMap: fast inference of causality from complex time series

Directory of Open Access Journals (Sweden)

M. Cyrus Maher

2015-03-01

Full Text Available Background. Establishing health-related causal relationships is a central pursuit in biomedical research. Yet, the interdependent non-linearity of biological systems renders causal dynamics laborious and at times impractical to disentangle. This pursuit is further impeded by the dearth of time series that are sufficiently long to observe and understand recurrent patterns of flux. However, as data generation costs plummet and technologies like wearable devices democratize data collection, we anticipate a coming surge in the availability of biomedically-relevant time series data. Given the life-saving potential of these burgeoning resources, it is critical to invest in the development of open source software tools that are capable of drawing meaningful insight from vast amounts of time series data.Results. Here we present CauseMap, the first open source implementation of convergent cross mapping (CCM, a method for establishing causality from long time series data (≳25 observations. Compared to existing time series methods, CCM has the advantage of being model-free and robust to unmeasured confounding that could otherwise induce spurious associations. CCM builds on Takens’ Theorem, a well-established result from dynamical systems theory that requires only mild assumptions. This theorem allows us to reconstruct high dimensional system dynamics using a time series of only a single variable. These reconstructions can be thought of as shadows of the true causal system. If reconstructed shadows can predict points from opposing time series, we can infer that the corresponding variables are providing views of the same causal system, and so are causally related. Unlike traditional metrics, this test can establish the directionality of causation, even in the presence of feedback loops. Furthermore, since CCM can extract causal relationships from times series of, e.g., a single individual, it may be a valuable tool to personalized medicine. We implement

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.

Track Irregularity Time Series Analysis and Trend Forecasting

Directory of Open Access Journals (Sweden)

Jia Chaolong

2012-01-01

Full Text Available The combination of linear and nonlinear methods is widely used in the prediction of time series data. This paper analyzes track irregularity time series data by using gray incidence degree models and methods of data transformation, trying to find the connotative relationship between the time series data. In this paper, GM (1,1 is based on first-order, single variable linear differential equations; after an adaptive improvement and error correction, it is used to predict the long-term changing trend of track irregularity at a fixed measuring point; the stochastic linear AR, Kalman filtering model, and artificial neural network model are applied to predict the short-term changing trend of track irregularity at unit section. Both long-term and short-term changes prove that the model is effective and can achieve the expected accuracy.

PRESEE: an MDL/MML algorithm to time-series stream segmenting.

Science.gov (United States)

Xu, Kaikuo; Jiang, Yexi; Tang, Mingjie; Yuan, Changan; Tang, Changjie

2013-01-01

Time-series stream is one of the most common data types in data mining field. It is prevalent in fields such as stock market, ecology, and medical care. Segmentation is a key step to accelerate the processing speed of time-series stream mining. Previous algorithms for segmenting mainly focused on the issue of ameliorating precision instead of paying much attention to the efficiency. Moreover, the performance of these algorithms depends heavily on parameters, which are hard for the users to set. In this paper, we propose PRESEE (parameter-free, real-time, and scalable time-series stream segmenting algorithm), which greatly improves the efficiency of time-series stream segmenting. PRESEE is based on both MDL (minimum description length) and MML (minimum message length) methods, which could segment the data automatically. To evaluate the performance of PRESEE, we conduct several experiments on time-series streams of different types and compare it with the state-of-art algorithm. The empirical results show that PRESEE is very efficient for real-time stream datasets by improving segmenting speed nearly ten times. The novelty of this algorithm is further demonstrated by the application of PRESEE in segmenting real-time stream datasets from ChinaFLUX sensor networks data stream.

Time-varying surrogate data to assess nonlinearity in nonstationary time series: application to heart rate variability.

Science.gov (United States)

Faes, Luca; Zhao, He; Chon, Ki H; Nollo, Giandomenico

2009-03-01

We propose a method to extend to time-varying (TV) systems the procedure for generating typical surrogate time series, in order to test the presence of nonlinear dynamics in potentially nonstationary signals. The method is based on fitting a TV autoregressive (AR) model to the original series and then regressing the model coefficients with random replacements of the model residuals to generate TV AR surrogate series. The proposed surrogate series were used in combination with a TV sample entropy (SE) discriminating statistic to assess nonlinearity in both simulated and experimental time series, in comparison with traditional time-invariant (TIV) surrogates combined with the TIV SE discriminating statistic. Analysis of simulated time series showed that using TIV surrogates, linear nonstationary time series may be erroneously regarded as nonlinear and weak TV nonlinearities may remain unrevealed, while the use of TV AR surrogates markedly increases the probability of a correct interpretation. Application to short (500 beats) heart rate variability (HRV) time series recorded at rest (R), after head-up tilt (T), and during paced breathing (PB) showed: 1) modifications of the SE statistic that were well interpretable with the known cardiovascular physiology; 2) significant contribution of nonlinear dynamics to HRV in all conditions, with significant increase during PB at 0.2 Hz respiration rate; and 3) a disagreement between TV AR surrogates and TIV surrogates in about a quarter of the series, suggesting that nonstationarity may affect HRV recordings and bias the outcome of the traditional surrogate-based nonlinearity test.

Local normalization: Uncovering correlations in non-stationary financial time series

Science.gov (United States)

Schäfer, Rudi; Guhr, Thomas

2010-09-01

The measurement of correlations between financial time series is of vital importance for risk management. In this paper we address an estimation error that stems from the non-stationarity of the time series. We put forward a method to rid the time series of local trends and variable volatility, while preserving cross-correlations. We test this method in a Monte Carlo simulation, and apply it to empirical data for the S&P 500 stocks.

Foundational Report Series. Advanced Distribution management Systems for Grid Modernization (Importance of DMS for Distribution Grid Modernization)

Energy Technology Data Exchange (ETDEWEB)

Wang, Jianhui [Argonne National Lab. (ANL), Argonne, IL (United States)

2015-09-01

Grid modernization is transforming the operation and management of electric distribution systems from manual, paper-driven business processes to electronic, computer-assisted decisionmaking. At the center of this business transformation is the distribution management system (DMS), which provides a foundation from which optimal levels of performance can be achieved in an increasingly complex business and operating environment. Electric distribution utilities are facing many new challenges that are dramatically increasing the complexity of operating and managing the electric distribution system: growing customer expectations for service reliability and power quality, pressure to achieve better efficiency and utilization of existing distribution system assets, and reduction of greenhouse gas emissions by accommodating high penetration levels of distributed generating resources powered by renewable energy sources (wind, solar, etc.). Recent “storm of the century” events in the northeastern United States and the lengthy power outages and customer hardships that followed have greatly elevated the need to make power delivery systems more resilient to major storm events and to provide a more effective electric utility response during such regional power grid emergencies. Despite these newly emerging challenges for electric distribution system operators, only a small percentage of electric utilities have actually implemented a DMS. This paper discusses reasons why a DMS is needed and why the DMS may emerge as a mission-critical system that will soon be considered essential as electric utilities roll out their grid modernization strategies.

Fuzzy time-series based on Fibonacci sequence for stock price forecasting

Science.gov (United States)

Chen, Tai-Liang; Cheng, Ching-Hsue; Jong Teoh, Hia

2007-07-01

Time-series models have been utilized to make reasonably accurate predictions in the areas of stock price movements, academic enrollments, weather, etc. For promoting the forecasting performance of fuzzy time-series models, this paper proposes a new model, which incorporates the concept of the Fibonacci sequence, the framework of Song and Chissom's model and the weighted method of Yu's model. This paper employs a 5-year period TSMC (Taiwan Semiconductor Manufacturing Company) stock price data and a 13-year period of TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) stock index data as experimental datasets. By comparing our forecasting performances with Chen's (Forecasting enrollments based on fuzzy time-series. Fuzzy Sets Syst. 81 (1996) 311-319), Yu's (Weighted fuzzy time-series models for TAIEX forecasting. Physica A 349 (2004) 609-624) and Huarng's (The application of neural networks to forecast fuzzy time series. Physica A 336 (2006) 481-491) models, we conclude that the proposed model surpasses in accuracy these conventional fuzzy time-series models.

Energy analysis of thermal energy storages with grid configurations

International Nuclear Information System (INIS)

Rezaie, Behnaz; Reddy, Bale V.; Rosen, Marc A.

2014-01-01

Highlights: • Grid configurations of TESs are developed and assessed. • Characteristics of various configurations of TESs are developed as functions of properties. • Functions for the discharge temperature and the discharge energy of the TES are developed. - Abstract: In some thermal networks like district energy systems, there can exist conditions, depending on space availability, economics, project requirements, insulation, storing media type and other issues, for which it may be advantageous to utilize several thermal energy storages (TESs) instead of one. Here, various configurations for multiple TESs are proposed and investigated. Significant parameters for a TES, or a set of TESs, include discharging temperature and recovered energy. First, one TES is modeled to determine the final temperature, energy recovery, and energy efficiency. Next, characteristics for various grid configurations of multiple TESs are developed as functions of TES characteristics (e.g., charging and discharging temperatures and energy quantities). Series, parallel and comprehensive grid TES configurations are considered. In the parallel configuration, the TESs behave independently. This suggests that the TES can consist of different storage media types and sizes, and that there is no restriction on initial temperature of the TES. In the series configuration, the situation is different because the TESs are connected directly or indirectly through a heat exchanger. If there is no heat exchanger between the TESs, the TES storage media should be the same, because the outlet of one TES in the series is the inlet to the next. The initial temperature of the second TES must be smaller than the discharge temperature of the first. There is no restriction on the TES size for series configurations. The general grid configuration is observed to exhibit characteristics of both series and parallel configurations

Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting

Directory of Open Access Journals (Sweden)

Shailendra Singh

2018-02-01

Full Text Available Responsible, efficient and environmentally aware energy consumption behavior is becoming a necessity for the reliable modern electricity grid. In this paper, we present an intelligent data mining model to analyze, forecast and visualize energy time series to uncover various temporal energy consumption patterns. These patterns define the appliance usage in terms of association with time such as hour of the day, period of the day, weekday, week, month and season of the year as well as appliance-appliance associations in a household, which are key factors to infer and analyze the impact of consumers’ energy consumption behavior and energy forecasting trend. This is challenging since it is not trivial to determine the multiple relationships among different appliances usage from concurrent streams of data. Also, it is difficult to derive accurate relationships between interval-based events where multiple appliance usages persist for some duration. To overcome these challenges, we propose unsupervised data clustering and frequent pattern mining analysis on energy time series, and Bayesian network prediction for energy usage forecasting. We perform extensive experiments using real-world context-rich smart meter datasets. The accuracy results of identifying appliance usage patterns using the proposed model outperformed Support Vector Machine (SVM and Multi-Layer Perceptron (MLP at each stage while attaining a combined accuracy of 81.82%, 85.90%, 89.58% for 25%, 50% and 75% of the training data size respectively. Moreover, we achieved energy consumption forecast accuracies of 81.89% for short-term (hourly and 75.88%, 79.23%, 74.74%, and 72.81% for the long-term; i.e., day, week, month, and season respectively.

Survey of off-grid dwellings : January and February 2003

Energy Technology Data Exchange (ETDEWEB)

NONE

2005-07-01

This report presents statistics from the results of a survey on off-grid dwellings in the Yukon. Two hundred and fifty-eight surveys were completed, and all questions asked in the survey were represented in this document. Dwellings were defined as any buildings in which people slept or ate, including cabins, cottages, lodges and camps, year-round or otherwise. Information was provided on whether or not the dwellings were main residences or recreational properties and the lengths of time in which the dwellings were used. Comments were presented on the difficulties of getting mortgages or other financing for off-grid dwellings. Detailed and specific questions were asked, requiring information about distances from electric poles and intentions to connect to the grid; whether or not the property was a subsistence and/or commercial residence; energy sources currently used; heating systems; light sources; water pumping; power generators; telephones; wind and solar power generators; and, costs involved in purchase and maintenance of energy systems. A series of questions revolved around the use, cost and maintenance of generators. Maintenance and installation costs of photovoltaic systems and wind power generators were presented. Micro-hydro system generators were discussed. Future involvement in energy programs was suggested with specific reference to off-grid energy programs. The survey concluded with comments and questions about off-grid energy in general. tabs.

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...

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

Science.gov (United States)

Ni, He; Yin, Hujun

2008-12-01

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

Analysis gets on the starting Grid

CERN Multimedia

Roger Jones

It is vital for ATLAS to have a functioning distributed analysis system to analyse its data. There are three major Grid deployments in ATLAS (Enabling Grids for E-sciencE, EGEE; the US Open Science Grid, OSG; and the Nordic DataGrid Facility, NGDF), and our data and jobs need to work across all of them, as well as on local machines and batch systems. Users must also be able to locate the data they want and register new small datasets so they can be used later. ATLAS has a suite of products to meet these needs, and a series of Distributed Analysis tutorials are training an increasing number of brave early adopters to use the system. Real users are vital to make sure that the tools are fit for their purpose and to refine our computing model. One such tutorial happened on the 1st and 2nd February at the National eScience Centre in Edinburgh, UK, sponsored by the GridPP Collaboration. The first day introduced an international set of tutees to the basic tools for Grid-based distributed analysis. The architecture...

Fractales y series de datos geofísicos

Directory of Open Access Journals (Sweden)

Montes Vides Luis Alfredo

1993-10-01

Full Text Available

grid-align: none;">There is a new Geometry which provides a potentially tool for the characterization of geophysical data: The Fractal Geometry. Generally, Geophysical data consist of records in time or data series, for example yearly records of temperature, and they show a random behavior or variation on both a short and a long-term time scale. The trace of a record is a curve with a fractal dimension D, and it is characterized by an exponent H. In this paper, the Hurt's rescaled range analysis method is used to determine the fractal dimension of a geophysical data serie D and H, his self-affinity measure.

grid-align: none;">La geometría de fractales ha surgido como una herramienta potencialmente útil para la caracterización de datos en Geofísica. Comúnmente, los datos geofísicos conforman series de tiempo, que exhiben un comportamiento aleatorio o variación a corto y a largo plazo. Un ejemplo típico son los registros anuales de temperatura. La traza de un registro es una curva con una dimensión fractal D, caracterizada por un exponente H.

grid-align: none;">En el presente trabajo se utiliza el método de análisis de rango en cambios de escala, creado por H. E. Hurst, para determinar la dimensión fractal de una serie de datos geofísicos, y su medida de auto-afinidad.

Bus.py: A GridLAB-D Communication Interface for Smart Distribution Grid Simulations

Energy Technology Data Exchange (ETDEWEB)

Hansen, Timothy M.; Palmintier, Bryan; Suryanarayanan, Siddharth; Maciejewski, Anthony A.; Siegel, Howard Jay

2015-07-03

As more Smart Grid technologies (e.g., distributed photovoltaic, spatially distributed electric vehicle charging) are integrated into distribution grids, static distribution simulations are no longer sufficient for performing modeling and analysis. GridLAB-D is an agent-based distribution system simulation environment that allows fine-grained end-user models, including geospatial and network topology detail. A problem exists in that, without outside intervention, once the GridLAB-D simulation begins execution, it will run to completion without allowing the real-time interaction of Smart Grid controls, such as home energy management systems and aggregator control. We address this lack of runtime interaction by designing a flexible communication interface, Bus.py (pronounced bus-dot-pie), that uses Python to pass messages between one or more GridLAB-D instances and a Smart Grid simulator. This work describes the design and implementation of Bus.py, discusses its usefulness in terms of some Smart Grid scenarios, and provides an example of an aggregator-based residential demand response system interacting with GridLAB-D through Bus.py. The small scale example demonstrates the validity of the interface and shows that an aggregator using said interface is able to control residential loads in GridLAB-D during runtime to cause a reduction in the peak load on the distribution system in (a) peak reduction and (b) time-of-use pricing cases.

Progress in Grid Generation: From Chimera to DRAGON Grids

Science.gov (United States)

Liou, Meng-Sing; Kao, Kai-Hsiung

1994-01-01

Hybrid grids, composed of structured and unstructured grids, combines the best features of both. The chimera method is a major stepstone toward a hybrid grid from which the present approach is evolved. The chimera grid composes a set of overlapped structured grids which are independently generated and body-fitted, yielding a high quality grid readily accessible for efficient solution schemes. The chimera method has been shown to be efficient to generate a grid about complex geometries and has been demonstrated to deliver accurate aerodynamic prediction of complex flows. While its geometrical flexibility is attractive, interpolation of data in the overlapped regions - which in today's practice in 3D is done in a nonconservative fashion, is not. In the present paper we propose a hybrid grid scheme that maximizes the advantages of the chimera scheme and adapts the strengths of the unstructured grid while at the same time keeps its weaknesses minimal. Like the chimera method, we first divide up the physical domain by a set of structured body-fitted grids which are separately generated and overlaid throughout a complex configuration. To eliminate any pure data manipulation which does not necessarily follow governing equations, we use non-structured grids only to directly replace the region of the arbitrarily overlapped grids. This new adaptation to the chimera thinking is coined the DRAGON grid. The nonstructured grid region sandwiched between the structured grids is limited in size, resulting in only a small increase in memory and computational effort. The DRAGON method has three important advantages: (1) preserving strengths of the chimera grid; (2) eliminating difficulties sometimes encountered in the chimera scheme, such as the orphan points and bad quality of interpolation stencils; and (3) making grid communication in a fully conservative and consistent manner insofar as the governing equations are concerned. To demonstrate its use, the governing equations are

The Prediction of Teacher Turnover Employing Time Series Analysis.

Science.gov (United States)

Costa, Crist H.

The purpose of this study was to combine knowledge of teacher demographic data with time-series forecasting methods to predict teacher turnover. Moving averages and exponential smoothing were used to forecast discrete time series. The study used data collected from the 22 largest school districts in Iowa, designated as FACT schools. Predictions…

Stacked Heterogeneous Neural Networks for Time Series Forecasting

Directory of Open Access Journals (Sweden)

Florin Leon

2010-01-01

Full Text Available A hybrid model for time series forecasting is proposed. It is a stacked neural network, containing one normal multilayer perceptron with bipolar sigmoid activation functions, and the other with an exponential activation function in the output layer. As shown by the case studies, the proposed stacked hybrid neural model performs well on a variety of benchmark time series. The combination of weights of the two stack components that leads to optimal performance is also studied.

Chaotic time series prediction: From one to another

International Nuclear Information System (INIS)

Zhao Pengfei; Xing Lei; Yu Jun

2009-01-01

In this Letter, a new local linear prediction model is proposed to predict a chaotic time series of a component x(t) by using the chaotic time series of another component y(t) in the same system with x(t). Our approach is based on the phase space reconstruction coming from the Takens embedding theorem. To illustrate our results, we present an example of Lorenz system and compare with the performance of the original local linear prediction model.

Grammar-based feature generation for time-series prediction

CERN Document Server

De Silva, Anthony Mihirana

2015-01-01

This book proposes a novel approach for time-series prediction using machine learning techniques with automatic feature generation. Application of machine learning techniques to predict time-series continues to attract considerable attention due to the difficulty of the prediction problems compounded by the non-linear and non-stationary nature of the real world time-series. The performance of machine learning techniques, among other things, depends on suitable engineering of features. This book proposes a systematic way for generating suitable features using context-free grammar. A number of feature selection criteria are investigated and a hybrid feature generation and selection algorithm using grammatical evolution is proposed. The book contains graphical illustrations to explain the feature generation process. The proposed approaches are demonstrated by predicting the closing price of major stock market indices, peak electricity load and net hourly foreign exchange client trade volume. The proposed method ...

Forecasting autoregressive time series under changing persistence

DEFF Research Database (Denmark)

Kruse, Robinson

Changing persistence in time series models means that a structural change from nonstationarity to stationarity or vice versa occurs over time. Such a change has important implications for forecasting, as negligence may lead to inaccurate model predictions. This paper derives generally applicable...

Recurrent Neural Networks for Multivariate Time Series with Missing Values.

Science.gov (United States)

Che, Zhengping; Purushotham, Sanjay; Cho, Kyunghyun; Sontag, David; Liu, Yan

2018-04-17

Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values. In time series prediction and other related tasks, it has been noted that missing values and their missing patterns are often correlated with the target labels, a.k.a., informative missingness. There is very limited work on exploiting the missing patterns for effective imputation and improving prediction performance. In this paper, we develop novel deep learning models, namely GRU-D, as one of the early attempts. GRU-D is based on Gated Recurrent Unit (GRU), a state-of-the-art recurrent neural network. It takes two representations of missing patterns, i.e., masking and time interval, and effectively incorporates them into a deep model architecture so that it not only captures the long-term temporal dependencies in time series, but also utilizes the missing patterns to achieve better prediction results. Experiments of time series classification tasks on real-world clinical datasets (MIMIC-III, PhysioNet) and synthetic datasets demonstrate that our models achieve state-of-the-art performance and provide useful insights for better understanding and utilization of missing values in time series analysis.

Conditional time series forecasting with convolutional neural networks

NARCIS (Netherlands)

A. Borovykh (Anastasia); S.M. Bohte (Sander); C.W. Oosterlee (Cornelis)

2017-01-01

textabstractForecasting financial time series using past observations has been a significant topic of interest. While temporal relationships in the data exist, they are difficult to analyze and predict accurately due to the non-linear trends and noise present in the series. We propose to learn these

Time Series Analysis of Wheat Futures Reward in China

Institute of Scientific and Technical Information of China (English)

无

2005-01-01

Different from the fact that the main researches are focused on single futures contract and lack of the comparison of different periods, this paper described the statistical characteristics of wheat futures reward time series of Zhengzhou Commodity Exchange in recent three years. Besides the basic statistic analysis, the paper used the GARCH and EGARCH model to describe the time series which had the ARCH effect and analyzed the persistence of volatility shocks and the leverage effect. The results showed that compared with that of normal one,wheat futures reward series were abnormality, leptokurtic and thick tail distribution. The study also found that two-part of the reward series had no autocorrelation. Among the six correlative series, three ones presented the ARCH effect. By using of the Auto-regressive Distributed Lag Model, GARCH model and EGARCH model, the paper demonstrates the persistence of volatility shocks and the leverage effect on the wheat futures reward time series. The results reveal that on the one hand, the statistical characteristics of the wheat futures reward are similar to the aboard mature futures market as a whole. But on the other hand, the results reflect some shortages such as the immatureness and the over-control by the government in the Chinese future market.

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.

Time series analysis of temporal networks

Science.gov (United States)

Sikdar, Sandipan; Ganguly, Niloy; Mukherjee, Animesh

2016-01-01

A common but an important feature of all real-world networks is that they are temporal in nature, i.e., the network structure changes over time. Due to this dynamic nature, it becomes difficult to propose suitable growth models that can explain the various important characteristic properties of these networks. In fact, in many application oriented studies only knowing these properties is sufficient. For instance, if one wishes to launch a targeted attack on a network, this can be done even without the knowledge of the full network structure; rather an estimate of some of the properties is sufficient enough to launch the attack. We, in this paper show that even if the network structure at a future time point is not available one can still manage to estimate its properties. We propose a novel method to map a temporal network to a set of time series instances, analyze them and using a standard forecast model of time series, try to predict the properties of a temporal network at a later time instance. To our aim, we consider eight properties such as number of active nodes, average degree, clustering coefficient etc. and apply our prediction framework on them. We mainly focus on the temporal network of human face-to-face contacts and observe that it represents a stochastic process with memory that can be modeled as Auto-Regressive-Integrated-Moving-Average (ARIMA). We use cross validation techniques to find the percentage accuracy of our predictions. An important observation is that the frequency domain properties of the time series obtained from spectrogram analysis could be used to refine the prediction framework by identifying beforehand the cases where the error in prediction is likely to be high. This leads to an improvement of 7.96% (for error level ≤20%) in prediction accuracy on an average across all datasets. As an application we show how such prediction scheme can be used to launch targeted attacks on temporal networks. Contribution to the Topical Issue

The Hierarchical Spectral Merger Algorithm: A New Time Series Clustering Procedure

KAUST Repository

Euá n, Carolina; Ombao, Hernando; Ortega, Joaquí n

2018-01-01

We present a new method for time series clustering which we call the Hierarchical Spectral Merger (HSM) method. This procedure is based on the spectral theory of time series and identifies series that share similar oscillations or waveforms

Notes on economic time series analysis system theoretic perspectives

CERN Document Server

Aoki, Masanao

1983-01-01

In seminars and graduate level courses I have had several opportunities to discuss modeling and analysis of time series with economists and economic graduate students during the past several years. These experiences made me aware of a gap between what economic graduate students are taught about vector-valued time series and what is available in recent system literature. Wishing to fill or narrow the gap that I suspect is more widely spread than my personal experiences indicate, I have written these notes to augment and reor­ ganize materials I have given in these courses and seminars. I have endeavored to present, in as much a self-contained way as practicable, a body of results and techniques in system theory that I judge to be relevant and useful to economists interested in using time series in their research. I have essentially acted as an intermediary and interpreter of system theoretic results and perspectives in time series by filtering out non-essential details, and presenting coherent accounts of wha...

Power management and frequency regulation for microgrid and smart grid: A real-time demand response approach

Science.gov (United States)

Pourmousavi Kani, Seyyed Ali

Future power systems (known as smart grid) will experience a high penetration level of variable distributed energy resources to bring abundant, affordable, clean, efficient, and reliable electric power to all consumers. However, it might suffer from the uncertain and variable nature of these generations in terms of reliability and especially providing required balancing reserves. In the current power system structure, balancing reserves (provided by spinning and non-spinning power generation units) usually are provided by conventional fossil-fueled power plants. However, such power plants are not the favorite option for the smart grid because of their low efficiency, high amount of emissions, and expensive capital investments on transmission and distribution facilities, to name a few. Providing regulation services in the presence of variable distributed energy resources would be even more difficult for islanded microgrids. The impact and effectiveness of demand response are still not clear at the distribution and transmission levels. In other words, there is no solid research reported in the literature on the evaluation of the impact of DR on power system dynamic performance. In order to address these issues, a real-time demand response approach along with real-time power management (specifically for microgrids) is proposed in this research. The real-time demand response solution is utilized at the transmission (through load-frequency control model) and distribution level (both in the islanded and grid-tied modes) to provide effective and fast regulation services for the stable operation of the power system. Then, multiple real-time power management algorithms for grid-tied and islanded microgrids are proposed to economically and effectively operate microgrids. Extensive dynamic modeling of generation, storage, and load as well as different controller design are considered and developed throughout this research to provide appropriate models and simulation

Dynamical analysis and visualization of tornadoes time series.

Directory of Open Access Journals (Sweden)

António M Lopes

Full Text Available In this paper we analyze the behavior of tornado time-series in the U.S. from the perspective of dynamical systems. A tornado is a violently rotating column of air extending from a cumulonimbus cloud down to the ground. Such phenomena reveal features that are well described by power law functions and unveil characteristics found in systems with long range memory effects. Tornado time series are viewed as the output of a complex system and are interpreted as a manifestation of its dynamics. Tornadoes are modeled as sequences of Dirac impulses with amplitude proportional to the events size. First, a collection of time series involving 64 years is analyzed in the frequency domain by means of the Fourier transform. The amplitude spectra are approximated by power law functions and their parameters are read as an underlying signature of the system dynamics. Second, it is adopted the concept of circular time and the collective behavior of tornadoes analyzed. Clustering techniques are then adopted to identify and visualize the emerging patterns.

Dynamical analysis and visualization of tornadoes time series.

Science.gov (United States)

Lopes, António M; Tenreiro Machado, J A

2015-01-01

In this paper we analyze the behavior of tornado time-series in the U.S. from the perspective of dynamical systems. A tornado is a violently rotating column of air extending from a cumulonimbus cloud down to the ground. Such phenomena reveal features that are well described by power law functions and unveil characteristics found in systems with long range memory effects. Tornado time series are viewed as the output of a complex system and are interpreted as a manifestation of its dynamics. Tornadoes are modeled as sequences of Dirac impulses with amplitude proportional to the events size. First, a collection of time series involving 64 years is analyzed in the frequency domain by means of the Fourier transform. The amplitude spectra are approximated by power law functions and their parameters are read as an underlying signature of the system dynamics. Second, it is adopted the concept of circular time and the collective behavior of tornadoes analyzed. Clustering techniques are then adopted to identify and visualize the emerging patterns.

"Observation Obscurer" - Time Series Viewer, Editor and Processor

Science.gov (United States)

Andronov, I. L.

The program is described, which contains a set of subroutines suitable for East viewing and interactive filtering and processing of regularly and irregularly spaced time series. Being a 32-bit DOS application, it may be used as a default fast viewer/editor of time series in any compute shell ("commander") or in Windows. It allows to view the data in the "time" or "phase" mode, to remove ("obscure") or filter outstanding bad points; to make scale transformations and smoothing using few methods (e.g. mean with phase binning, determination of the statistically opti- mal number of phase bins; "running parabola" (Andronov, 1997, As. Ap. Suppl, 125, 207) fit and to make time series analysis using some methods, e.g. correlation, autocorrelation and histogram analysis: determination of extrema etc. Some features have been developed specially for variable star observers, e.g. the barycentric correction, the creation and fast analysis of "OC" diagrams etc. The manual for "hot keys" is presented. The computer code was compiled with a 32-bit Free Pascal (www.freepascal.org).

Modelling road accidents: An approach using structural time series

Science.gov (United States)

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.

Multiscale Poincaré plots for visualizing the structure of heartbeat time series.

Science.gov (United States)

Henriques, Teresa S; Mariani, Sara; Burykin, Anton; Rodrigues, Filipa; Silva, Tiago F; Goldberger, Ary L

2016-02-09

Poincaré delay maps are widely used in the analysis of cardiac interbeat interval (RR) dynamics. To facilitate visualization of the structure of these time series, we introduce multiscale Poincaré (MSP) plots. Starting with the original RR time series, the method employs a coarse-graining procedure to create a family of time series, each of which represents the system's dynamics in a different time scale. Next, the Poincaré plots are constructed for the original and the coarse-grained time series. Finally, as an optional adjunct, color can be added to each point to represent its normalized frequency. We illustrate the MSP method on simulated Gaussian white and 1/f noise time series. The MSP plots of 1/f noise time series reveal relative conservation of the phase space area over multiple time scales, while those of white noise show a marked reduction in area. We also show how MSP plots can be used to illustrate the loss of complexity when heartbeat time series from healthy subjects are compared with those from patients with chronic (congestive) heart failure syndrome or with atrial fibrillation. This generalized multiscale approach to Poincaré plots may be useful in visualizing other types of time series.

Time series patterns and language support in DBMS

Science.gov (United States)

Telnarova, Zdenka

2017-07-01

This contribution is focused on pattern type Time Series as a rich in semantics representation of data. Some example of implementation of this pattern type in traditional Data Base Management Systems is briefly presented. There are many approaches how to manipulate with patterns and query patterns. Crucial issue can be seen in systematic approach to pattern management and specific pattern query language which takes into consideration semantics of patterns. Query language SQL-TS for manipulating with patterns is shown on Time Series data.

Two-fractal overlap time series: Earthquakes and market crashes

Indian Academy of Sciences (India)

velocity over the other and time series of stock prices. An anticipation method for some of the crashes have been proposed here, based on these observations. Keywords. Cantor set; time series; earthquake; market crash. PACS Nos 05.00; 02.50.-r; 64.60; 89.65.Gh; 95.75.Wx. 1. Introduction. Capturing dynamical patterns of ...

Nonlinear time series analysis with R

CERN Document Server

Huffaker, Ray; Rosa, Rodolfo

2017-01-01

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

InSAR Deformation Time Series Processed On-Demand in the Cloud

Science.gov (United States)

Horn, W. B.; Weeden, R.; Dimarchi, H.; Arko, S. A.; Hogenson, K.

2017-12-01

During this past year, ASF has developed a cloud-based on-demand processing system known as HyP3 (http://hyp3.asf.alaska.edu/), the Hybrid Pluggable Processing Pipeline, for Synthetic Aperture Radar (SAR) data. The system makes it easy for a user who doesn't have the time or inclination to install and use complex SAR processing software to leverage SAR data in their research or operations. One such processing algorithm is generation of a deformation time series product, which is a series of images representing ground displacements over time, which can be computed using a time series of interferometric SAR (InSAR) products. The set of software tools necessary to generate this useful product are difficult to install, configure, and use. Moreover, for a long time series with many images, the processing of just the interferograms can take days. Principally built by three undergraduate students at the ASF DAAC, the deformation time series processing relies the new Amazon Batch service, which enables processing of jobs with complex interconnected dependencies in a straightforward and efficient manner. In the case of generating a deformation time series product from a stack of single-look complex SAR images, the system uses Batch to serialize the up-front processing, interferogram generation, optional tropospheric correction, and deformation time series generation. The most time consuming portion is the interferogram generation, because even for a fairly small stack of images many interferograms need to be processed. By using AWS Batch, the interferograms are all generated in parallel; the entire process completes in hours rather than days. Additionally, the individual interferograms are saved in Amazon's cloud storage, so that when new data is acquired in the stack, an updated time series product can be generated with minimal addiitonal processing. This presentation will focus on the development techniques and enabling technologies that were used in developing the time

The International Symposium on Grids and Clouds and the Open Grid Forum

Science.gov (United States)

The International Symposium on Grids and Clouds 20111 was held at Academia Sinica in Taipei, Taiwan on 19th to 25th March 2011. A series of workshops and tutorials preceded the symposium. The aim of ISGC is to promote the use of grid and cloud computing in the Asia Pacific region. Over the 9 years that ISGC has been running, the programme has evolved to become more user community focused with subjects reaching out to a larger population. Research communities are making widespread use of distributed computing facilities. Linking together data centers, production grids, desktop systems or public clouds, many researchers are able to do more research and produce results more quickly. They could do much more if the computing infrastructures they use worked together more effectively. Changes in the way we approach distributed computing, and new services from commercial providers, mean that boundaries are starting to blur. This opens the way for hybrid solutions that make it easier for researchers to get their job done. Consequently the theme for ISGC2011 was the opportunities that better integrated computing infrastructures can bring, and the steps needed to achieve the vision of a seamless global research infrastructure. 2011 is a year of firsts for ISGC. First the title - while the acronym remains the same, its meaning has changed to reflect the evolution of computing: The International Symposium on Grids and Clouds. Secondly the programming - ISGC 2011 has always included topical workshops and tutorials. But 2011 is the first year that ISGC has been held in conjunction with the Open Grid Forum2 which held its 31st meeting with a series of working group sessions. The ISGC plenary session included keynote speakers from OGF that highlighted the relevance of standards for the research community. ISGC with its focus on applications and operational aspects complemented well with OGF's focus on standards development. ISGC brought to OGF real-life use cases and needs to be

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.

25 years of time series forecasting

NARCIS (Netherlands)

de Gooijer, J.G.; Hyndman, R.J.

2006-01-01

We review the past 25 years of research into time series forecasting. In this silver jubilee issue, we naturally highlight results published in journals managed by the International Institute of Forecasters (Journal of Forecasting 1982-1985 and International Journal of Forecasting 1985-2005). During

Markov Trends in Macroeconomic Time Series

NARCIS (Netherlands)

R. Paap (Richard)

1997-01-01

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

Modeling seasonality in bimonthly time series

NARCIS (Netherlands)

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

On clustering fMRI time series

DEFF Research Database (Denmark)

Goutte, Cyril; Toft, Peter Aundal; Rostrup, E.

1999-01-01

Analysis of fMRI time series is often performed by extracting one or more parameters for the individual voxels. Methods based, e.g., on various statistical tests are then used to yield parameters corresponding to probability of activation or activation strength. However, these methods do...

FALSE DETERMINATIONS OF CHAOS IN SHORT NOISY TIME SERIES. (R828745)

Science.gov (United States)

A method (NEMG) proposed in 1992 for diagnosing chaos in noisy time series with 50 or fewer observations entails fitting the time series with an empirical function which predicts an observation in the series from previous observations, and then estimating the rate of divergenc...

Multiscale multifractal multiproperty analysis of financial time series based on Rényi entropy

Science.gov (United States)

Yujun, Yang; Jianping, Li; Yimei, Yang

This paper introduces a multiscale multifractal multiproperty analysis based on Rényi entropy (3MPAR) method to analyze short-range and long-range characteristics of financial time series, and then applies this method to the five time series of five properties in four stock indices. Combining the two analysis techniques of Rényi entropy and multifractal detrended fluctuation analysis (MFDFA), the 3MPAR method focuses on the curves of Rényi entropy and generalized Hurst exponent of five properties of four stock time series, which allows us to study more universal and subtle fluctuation characteristics of financial time series. By analyzing the curves of the Rényi entropy and the profiles of the logarithm distribution of MFDFA of five properties of four stock indices, the 3MPAR method shows some fluctuation characteristics of the financial time series and the stock markets. Then, it also shows a richer information of the financial time series by comparing the profile of five properties of four stock indices. In this paper, we not only focus on the multifractality of time series but also the fluctuation characteristics of the financial time series and subtle differences in the time series of different properties. We find that financial time series is far more complex than reported in some research works using one property of time series.

A Literature Survey of Early Time Series Classification and Deep Learning

OpenAIRE

Santos, Tiago; Kern, Roman

2017-01-01

This paper provides an overview of current literature on time series classification approaches, in particular of early time series classification. A very common and effective time series classification approach is the 1-Nearest Neighbor classier, with different distance measures such as the Euclidean or dynamic time warping distances. This paper starts by reviewing these baseline methods. More recently, with the gain in popularity in the application of deep neural networks to the eld of...

Signal Processing for Time-Series Functions on a Graph

Science.gov (United States)

2018-02-01

Figures Fig. 1 Time -series function on a fixed graph.............................................2 iv Approved for public release; distribution is...φi〉`2(V)φi (39) 6= f̄ (40) Instead, we simply recover the average of f over time . 13 Approved for public release; distribution is unlimited. This...ARL-TR-8276• FEB 2018 US Army Research Laboratory Signal Processing for Time -Series Functions on a Graph by Humberto Muñoz-Barona, Jean Vettel, and

Non-linear time series extreme events and integer value problems

CERN Document Server

Turkman, Kamil Feridun; Zea Bermudez, Patrícia

2014-01-01

This book offers a useful combination of probabilistic and statistical tools for analyzing nonlinear time series. Key features of the book include a study of the extremal behavior of nonlinear time series and a comprehensive list of nonlinear models that address different aspects of nonlinearity. Several inferential methods, including quasi likelihood methods, sequential Markov Chain Monte Carlo Methods and particle filters, are also included so as to provide an overall view of the available tools for parameter estimation for nonlinear models. A chapter on integer time series models based on several thinning operations, which brings together all recent advances made in this area, is also included. Readers should have attended a prior course on linear time series, and a good grasp of simulation-based inferential methods is recommended. This book offers a valuable resource for second-year graduate students and researchers in statistics and other scientific areas who need a basic understanding of nonlinear time ...

Learning of time series through neuron-to-neuron instruction

Energy Technology Data Exchange (ETDEWEB)

Miyazaki, Y [Department of Physics, Kyoto University, Kyoto 606-8502, (Japan); Kinzel, W [Institut fuer Theoretische Physik, Universitaet Wurzburg, 97074 Wurzburg (Germany); Shinomoto, S [Department of Physics, Kyoto University, Kyoto (Japan)

2003-02-07

A model neuron with delayline feedback connections can learn a time series generated by another model neuron. It has been known that some student neurons that have completed such learning under the instruction of a teacher's quasi-periodic sequence mimic the teacher's time series over a long interval, even after instruction has ceased. We found that in addition to such faithful students, there are unfaithful students whose time series eventually diverge exponentially from that of the teacher. In order to understand the circumstances that allow for such a variety of students, the orbit dimension was estimated numerically. The quasi-periodic orbits in question were found to be confined in spaces with dimensions significantly smaller than that of the full phase space.

Learning of time series through neuron-to-neuron instruction

International Nuclear Information System (INIS)

Miyazaki, Y; Kinzel, W; Shinomoto, S

2003-01-01

A model neuron with delayline feedback connections can learn a time series generated by another model neuron. It has been known that some student neurons that have completed such learning under the instruction of a teacher's quasi-periodic sequence mimic the teacher's time series over a long interval, even after instruction has ceased. We found that in addition to such faithful students, there are unfaithful students whose time series eventually diverge exponentially from that of the teacher. In order to understand the circumstances that allow for such a variety of students, the orbit dimension was estimated numerically. The quasi-periodic orbits in question were found to be confined in spaces with dimensions significantly smaller than that of the full phase space

Quirky patterns in time-series of estimates of recruitment could be artefacts

DEFF Research Database (Denmark)

Dickey-Collas, M.; Hinzen, N.T.; Nash, R.D.M.

2015-01-01

of recruitment time-series in databases is therefore not consistent across or within species and stocks. Caution is therefore required as perhaps the characteristics of the time-series of stock dynamics may be determined by the model used to generate them, rather than underlying ecological phenomena......The accessibility of databases of global or regional stock assessment outputs is leading to an increase in meta-analysis of the dynamics of fish stocks. In most of these analyses, each of the time-series is generally assumed to be directly comparable. However, the approach to stock assessment...... employed, and the associated modelling assumptions, can have an important influence on the characteristics of each time-series. We explore this idea by investigating recruitment time-series with three different recruitment parameterizations: a stock–recruitment model, a random-walk time-series model...

The Hierarchical Spectral Merger Algorithm: A New Time Series Clustering Procedure

KAUST Repository

Euán, Carolina

2018-04-12

We present a new method for time series clustering which we call the Hierarchical Spectral Merger (HSM) method. This procedure is based on the spectral theory of time series and identifies series that share similar oscillations or waveforms. The extent of similarity between a pair of time series is measured using the total variation distance between their estimated spectral densities. At each step of the algorithm, every time two clusters merge, a new spectral density is estimated using the whole information present in both clusters, which is representative of all the series in the new cluster. The method is implemented in an R package HSMClust. We present two applications of the HSM method, one to data coming from wave-height measurements in oceanography and the other to electroencefalogram (EEG) data.

Estimation of time-delayed mutual information and bias for irregularly and sparsely sampled time-series

International Nuclear Information System (INIS)

Albers, D.J.; Hripcsak, George

2012-01-01

Highlights: ► Time-delayed mutual information for irregularly sampled time-series. ► Estimation bias for the time-delayed mutual information calculation. ► Fast, simple, PDF estimator independent, time-delayed mutual information bias estimate. ► Quantification of data-set-size limits of the time-delayed mutual calculation. - Abstract: A method to estimate the time-dependent correlation via an empirical bias estimate of the time-delayed mutual information for a time-series is proposed. In particular, the bias of the time-delayed mutual information is shown to often be equivalent to the mutual information between two distributions of points from the same system separated by infinite time. Thus intuitively, estimation of the bias is reduced to estimation of the mutual information between distributions of data points separated by large time intervals. The proposed bias estimation techniques are shown to work for Lorenz equations data and glucose time series data of three patients from the Columbia University Medical Center database.

Inferring interdependencies from short time series

Indian Academy of Sciences (India)

Abstract. Complex networks provide an invaluable framework for the study of interlinked dynamical systems. In many cases, such networks are constructed from observed time series by first estimating the ...... does not quantify causal relations (unlike IOTA, or .... Africa_map_regions.svg, which is under public domain.

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

Science.gov (United States)

Giorgio, M.; Greco, R.

2009-04-01

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

Gridded rainfall estimation for distributed modeling in western mountainous areas

Science.gov (United States)

Moreda, F.; Cong, S.; Schaake, J.; Smith, M.

2006-05-01

Estimation of precipitation in mountainous areas continues to be problematic. It is well known that radar-based methods are limited due to beam blockage. In these areas, in order to run a distributed model that accounts for spatially variable precipitation, we have generated hourly gridded rainfall estimates from gauge observations. These estimates will be used as basic data sets to support the second phase of the NWS-sponsored Distributed Hydrologic Model Intercomparison Project (DMIP 2). One of the major foci of DMIP 2 is to better understand the modeling and data issues in western mountainous areas in order to provide better water resources products and services to the Nation. We derive precipitation estimates using three data sources for the period of 1987-2002: 1) hourly cooperative observer (coop) gauges, 2) daily total coop gauges and 3) SNOw pack TELemetry (SNOTEL) daily gauges. The daily values are disaggregated using the hourly gauge values and then interpolated to approximately 4km grids using an inverse-distance method. Following this, the estimates are adjusted to match monthly mean values from the Parameter-elevation Regressions on Independent Slopes Model (PRISM). Several analyses are performed to evaluate the gridded estimates for DMIP 2 experiments. These gridded inputs are used to generate mean areal precipitation (MAPX) time series for comparison to the traditional mean areal precipitation (MAP) time series derived by the NWS' California-Nevada River Forecast Center for model calibration. We use two of the DMIP 2 basins in California and Nevada: the North Fork of the American River (catchment area 885 sq. km) and the East Fork of the Carson River (catchment area 922 sq. km) as test areas. The basins are sub-divided into elevation zones. The North Fork American basin is divided into two zones above and below an elevation threshold. Likewise, the Carson River basin is subdivided in to four zones. For each zone, the analyses include: a) overall

Grids for Kids gives next-generation IT an early start

CERN Multimedia

2008-01-01

Last week, the third in a growing series of Grids for Kids days was held at CERN, the European Organisation for Nuclear Research, involving children aged ten to twelve in games, tours and interactive presentations that introduced grid computing as a tool for researchers in everything from high energy physics to climate studies and genomics.

Using forbidden ordinal patterns to detect determinism in irregularly sampled time series.

Science.gov (United States)

Kulp, C W; Chobot, J M; Niskala, B J; Needhammer, C J

2016-02-01

It is known that when symbolizing a time series into ordinal patterns using the Bandt-Pompe (BP) methodology, there will be ordinal patterns called forbidden patterns that do not occur in a deterministic series. The existence of forbidden patterns can be used to identify deterministic dynamics. In this paper, the ability to use forbidden patterns to detect determinism in irregularly sampled time series is tested on data generated from a continuous model system. The study is done in three parts. First, the effects of sampling time on the number of forbidden patterns are studied on regularly sampled time series. The next two parts focus on two types of irregular-sampling, missing data and timing jitter. It is shown that forbidden patterns can be used to detect determinism in irregularly sampled time series for low degrees of sampling irregularity (as defined in the paper). In addition, comments are made about the appropriateness of using the BP methodology to symbolize irregularly sampled time series.

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

Complexity testing techniques for time series data: A comprehensive literature review

International Nuclear Information System (INIS)

Tang, Ling; Lv, Huiling; Yang, Fengmei; Yu, Lean

2015-01-01

Highlights: • A literature review of complexity testing techniques for time series data is provided. • Complexity measurements can generally fall into fractality, methods derived from nonlinear dynamics and entropy. • Different types investigate time series data from different perspectives. • Measures, applications and future studies for each type are presented. - Abstract: Complexity may be one of the most important measurements for analysing time series data; it covers or is at least closely related to different data characteristics within nonlinear system theory. This paper provides a comprehensive literature review examining the complexity testing techniques for time series data. According to different features, the complexity measurements for time series data can be divided into three primary groups, i.e., fractality (mono- or multi-fractality) for self-similarity (or system memorability or long-term persistence), methods derived from nonlinear dynamics (via attractor invariants or diagram descriptions) for attractor properties in phase-space, and entropy (structural or dynamical entropy) for the disorder state of a nonlinear system. These estimations analyse time series dynamics from different perspectives but are closely related to or even dependent on each other at the same time. In particular, a weaker self-similarity, a more complex structure of attractor, and a higher-level disorder state of a system consistently indicate that the observed time series data are at a higher level of complexity. Accordingly, this paper presents a historical tour of the important measures and works for each group, as well as ground-breaking and recent applications and future research directions.

Complex dynamic in ecological time series

Science.gov (United States)

Peter Turchin; Andrew D. Taylor

1992-01-01

Although the possibility of complex dynamical behaviors-limit cycles, quasiperiodic oscillations, and aperiodic chaos-has been recognized theoretically, most ecologists are skeptical of their importance in nature. In this paper we develop a methodology for reconstructing endogenous (or deterministic) dynamics from ecological time series. Our method consists of fitting...

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...

SensL B-Series and C-Series silicon photomultipliers for time-of-flight positron emission tomography

Energy Technology Data Exchange (ETDEWEB)

O' Neill, K., E-mail: koneill@sensl.com; Jackson, C., E-mail: cjackson@sensl.com

2015-07-01

Silicon photomultipliers from SensL are designed for high performance, uniformity and low cost. They demonstrate peak photon detection efficiency of 41% at 420 nm, which is matched to the output spectrum of cerium doped lutetium orthosilicate. Coincidence resolving time of less than 220 ps is demonstrated. New process improvements have lead to the development of C-Series SiPM which reduces the dark noise by over an order of magnitude. In this paper we will show characterization test results which include photon detection efficiency, dark count rate, crosstalk probability, afterpulse probability and coincidence resolving time comparing B-Series to the newest pre-production C-Series. Additionally we will discuss the effect of silicon photomultiplier microcell size on coincidence resolving time allowing the optimal microcell size choice to be made for time of flight positron emission tomography systems.

Kriging Methodology and Its Development in Forecasting Econometric Time Series

Directory of Open Access Journals (Sweden)

Andrej Gajdoš

2017-03-01

Full Text Available One of the approaches for forecasting future values of a time series or unknown spatial data is kriging. The main objective of the paper is to introduce a general scheme of kriging in forecasting econometric time series using a family of linear regression time series models (shortly named as FDSLRM which apply regression not only to a trend but also to a random component of the observed time series. Simultaneously performing a Monte Carlo simulation study with a real electricity consumption dataset in the R computational langure and environment, we investigate the well-known problem of “negative” estimates of variance components when kriging predictions fail. Our following theoretical analysis, including also the modern apparatus of advanced multivariate statistics, gives us the formulation and proof of a general theorem about the explicit form of moments (up to sixth order for a Gaussian time series observation. This result provides a basis for further theoretical and computational research in the kriging methodology development.

Use of Time-Series, ARIMA Designs to Assess Program Efficacy.

Science.gov (United States)

Braden, Jeffery P.; And Others

1990-01-01

Illustrates use of time-series designs for determining efficacy of interventions with fictitious data describing drug-abuse prevention program. Discusses problems and procedures associated with time-series data analysis using Auto Regressive Integrated Moving Averages (ARIMA) models. Example illustrates application of ARIMA analysis for…

A composite passive damping method of the LLCL-filter based grid-tied inverter

DEFF Research Database (Denmark)

Wu, Weimin; Huang, Min; Sun, Yunjie

2012-01-01

This paper investigates the maximum and the minimum gain of the proportional resonant based grid current controller for a grid-tied inverter with a passive damped high-order power filter. It is found that the choice of the controller gain is limited to the local maximum amplitude determined by Q......-factor around the characteristic frequency of the filter and grid impedance. To obtain the Q-factor of a high-order system, an equivalent circuit analysis method is proposed and illustrated through several classical passive damped LCL- and LLCL-filters. It is shown that both the RC parallel damper...... that is in parallel with the capacitor of the LCL-filter or with the Lf-Cf resonant circuit of the LLCL-filter, and the RL series damper in series with the grid-side inductor have their own application limits. Thus, a composite passive damped LLCL-filter for the grid-tied inverter is proposed, which can effectively...

Utilities’ Technology Management of Smart Grid Innovation and Implementation

DEFF Research Database (Denmark)

Tambo, Torben

2012-01-01

To create sustainability in society’s energy system, power producers and consumers need smart grid technology to balance among producing and consuming units. Smart grid makes up a host of specifications, unproven products and concepts as well as a strong and dedicated innovation process for utility...... companies. Few proven concepts exist, and with the prolongation, smart grid represents an excellent case of technology management. The current study uses a series of loosely related technological studies of smart grid technologies in SME electricity companies to highlight and characterise critical issues....... Major findings are that the companies have limited capabilities in management of technology despite both strong push and pull, and they suffer from vaguely expressed objectives....

An algorithm of Saxena-Easo on fuzzy time series forecasting

Science.gov (United States)

Ramadhani, L. C.; Anggraeni, D.; Kamsyakawuni, A.; Hadi, A. F.

2018-04-01

This paper presents a forecast model of Saxena-Easo fuzzy time series prediction to study the prediction of Indonesia inflation rate in 1970-2016. We use MATLAB software to compute this method. The algorithm of Saxena-Easo fuzzy time series doesn’t need stationarity like conventional forecasting method, capable of dealing with the value of time series which are linguistic and has the advantage of reducing the calculation, time and simplifying the calculation process. Generally it’s focus on percentage change as the universe discourse, interval partition and defuzzification. The result indicate that between the actual data and the forecast data are close enough with Root Mean Square Error (RMSE) = 1.5289.

Power grid reliability and security

Energy Technology Data Exchange (ETDEWEB)

Bose, Anjan [Washington State Univ., Pullman, WA (United States); Venkatasubramanian, Vaithianathan [Washington State Univ., Pullman, WA (United States); Hauser, Carl [Washington State Univ., Pullman, WA (United States); Bakken, David [Washington State Univ., Pullman, WA (United States); Anderson, David [Washington State Univ., Pullman, WA (United States); Zhao, Chuanlin [Washington State Univ., Pullman, WA (United States); Liu, Dong [Washington State Univ., Pullman, WA (United States); Yang, Tao [Washington State Univ., Pullman, WA (United States); Meng, Ming [Washington State Univ., Pullman, WA (United States); Zhang, Lin [Washington State Univ., Pullman, WA (United States); Ning, Jiawei [Washington State Univ., Pullman, WA (United States); Tashman, Zaid [Washington State Univ., Pullman, WA (United States)

2015-01-31

This project has led to the development of a real-time simulation platform for electric power grids called Grid Simulator or GridSim for simulating the dynamic and information network interactions of large- scale power systems. The platform consists of physical models of power system components including synchronous generators, loads and control, which are simulated using a modified commercial power simulator namely Transient Stability Analysis Tool (TSAT) [1] together with data cleanup components, as well as an emulated substation level and wide-area power analysis components. The platform also includes realistic representations of communication network middleware that can emulate the real-time information flow back and forth between substations and control centers in wide-area power systems. The platform has been validated on a realistic 6000-bus model of the western American power system. The simulator GridSim developed in this project is the first of its kind in its ability to simulate real-time response of large-scale power grids, and serves as a cost effective real-time stability and control simulation platform for power industry.

Analysis of Timing Requirements for Data Aggregation and Control in Smart Grids

DEFF Research Database (Denmark)

Kemal, Mohammed Seifu; Olsen, Rasmus Løvenstein

2014-01-01

Modern communication mechanisms are at the heart of a smart grid system to ensure that the required information is transmitted within various components of the grid. Throughout this paper, we have studied how communication performance delays and smart grid controller delays effects the overall co...

Evolutionary Algorithms for the Detection of Structural Breaks in Time Series

DEFF Research Database (Denmark)

Doerr, Benjamin; Fischer, Paul; Hilbert, Astrid

2013-01-01

Detecting structural breaks is an essential task for the statistical analysis of time series, for example, for fitting parametric models to it. In short, structural breaks are points in time at which the behavior of the time series changes. Typically, no solid background knowledge of the time...

On modeling panels of time series

NARCIS (Netherlands)

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

Unsupervised Symbolization of Signal Time Series for Extraction of the Embedded Information

Directory of Open Access Journals (Sweden)

Yue Li

2017-03-01

Full Text Available This paper formulates an unsupervised algorithm for symbolization of signal time series to capture the embedded dynamic behavior. The key idea is to convert time series of the digital signal into a string of (spatially discrete symbols from which the embedded dynamic information can be extracted in an unsupervised manner (i.e., no requirement for labeling of time series. The main challenges here are: (1 definition of the symbol assignment for the time series; (2 identification of the partitioning segment locations in the signal space of time series; and (3 construction of probabilistic finite-state automata (PFSA from the symbol strings that contain temporal patterns. The reported work addresses these challenges by maximizing the mutual information measures between symbol strings and PFSA states. The proposed symbolization method has been validated by numerical simulation as well as by experimentation in a laboratory environment. Performance of the proposed algorithm has been compared to that of two commonly used algorithms of time series partitioning.

Classification of time-series images using deep convolutional neural networks

Science.gov (United States)

Hatami, Nima; Gavet, Yann; Debayle, Johan

2018-04-01

Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw data. While the majority of Time-Series Classification (TSC) literature is focused on 1D signals, this paper uses Recurrence Plots (RP) to transform time-series into 2D texture images and then take advantage of the deep CNN classifier. Image representation of time-series introduces different feature types that are not available for 1D signals, and therefore TSC can be treated as texture image recognition task. CNN model also allows learning different levels of representations together with a classifier, jointly and automatically. Therefore, using RP and CNN in a unified framework is expected to boost the recognition rate of TSC. Experimental results on the UCR time-series classification archive demonstrate competitive accuracy of the proposed approach, compared not only to the existing deep architectures, but also to the state-of-the art TSC algorithms.

Long Range Dependence Prognostics for Bearing Vibration Intensity Chaotic Time Series

Directory of Open Access Journals (Sweden)

Qing Li

2016-01-01

Full Text Available According to the chaotic features and typical fractional order characteristics of the bearing vibration intensity time series, a forecasting approach based on long range dependence (LRD is proposed. In order to reveal the internal chaotic properties, vibration intensity time series are reconstructed based on chaos theory in phase-space, the delay time is computed with C-C method and the optimal embedding dimension and saturated correlation dimension are calculated via the Grassberger–Procaccia (G-P method, respectively, so that the chaotic characteristics of vibration intensity time series can be jointly determined by the largest Lyapunov exponent and phase plane trajectory of vibration intensity time series, meanwhile, the largest Lyapunov exponent is calculated by the Wolf method and phase plane trajectory is illustrated using Duffing-Holmes Oscillator (DHO. The Hurst exponent and long range dependence prediction method are proposed to verify the typical fractional order features and improve the prediction accuracy of bearing vibration intensity time series, respectively. Experience shows that the vibration intensity time series have chaotic properties and the LRD prediction method is better than the other prediction methods (largest Lyapunov, auto regressive moving average (ARMA and BP neural network (BPNN model in prediction accuracy and prediction performance, which provides a new approach for running tendency predictions for rotating machinery and provide some guidance value to the engineering practice.

AIRS-Observed Interrelationships of Anomaly Time-Series of Moist Process-Related Parameters and Inferred Feedback Values on Various Spatial Scales

Science.gov (United States)

Molnar, Gyula I.; Susskind, Joel; Iredell, Lena

2011-01-01

In the beginning, a good measure of a GMCs performance was their ability to simulate the observed mean seasonal cycle. That is, a reasonable simulation of the means (i.e., small biases) and standard deviations of TODAY?S climate would suffice. Here, we argue that coupled GCM (CG CM for short) simulations of FUTURE climates should be evaluated in much more detail, both spatially and temporally. Arguably, it is not the bias, but rather the reliability of the model-generated anomaly time-series, even down to the [C]GCM grid-scale, which really matter. This statement is underlined by the social need to address potential REGIONAL climate variability, and climate drifts/changes in a manner suitable for policy decisions.

Critical values for unit root tests in seasonal time series

NARCIS (Netherlands)

Ph.H.B.F. Franses (Philip Hans); B. Hobijn (Bart)

1997-01-01

textabstractIn this paper, we present tables with critical values for a variety of tests for seasonal and non-seasonal unit roots in seasonal time series. We consider (extensions of) the Hylleberg et al. and Osborn et al. test procedures. These extensions concern time series with increasing seasonal

Classification of time series patterns from complex dynamic systems

Energy Technology Data Exchange (ETDEWEB)

Schryver, J.C.; Rao, N.

1998-07-01

An increasing availability of high-performance computing and data storage media at decreasing cost is making possible the proliferation of large-scale numerical databases and data warehouses. Numeric warehousing enterprises on the order of hundreds of gigabytes to terabytes are a reality in many fields such as finance, retail sales, process systems monitoring, biomedical monitoring, surveillance and transportation. Large-scale databases are becoming more accessible to larger user communities through the internet, web-based applications and database connectivity. Consequently, most researchers now have access to a variety of massive datasets. This trend will probably only continue to grow over the next several years. Unfortunately, the availability of integrated tools to explore, analyze and understand the data warehoused in these archives is lagging far behind the ability to gain access to the same data. In particular, locating and identifying patterns of interest in numerical time series data is an increasingly important problem for which there are few available techniques. Temporal pattern recognition poses many interesting problems in classification, segmentation, prediction, diagnosis and anomaly detection. This research focuses on the problem of classification or characterization of numerical time series data. Highway vehicles and their drivers are examples of complex dynamic systems (CDS) which are being used by transportation agencies for field testing to generate large-scale time series datasets. Tools for effective analysis of numerical time series in databases generated by highway vehicle systems are not yet available, or have not been adapted to the target problem domain. However, analysis tools from similar domains may be adapted to the problem of classification of numerical time series data.

Sensitivity analysis of machine-learning models of hydrologic time series

Science.gov (United States)

O'Reilly, A. M.

2017-12-01

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

The LHCb Grid Simulation

CERN Multimedia

Baranov, Alexander

2016-01-01

The LHCb Grid access if based on the LHCbDirac system. It provides access to data and computational resources to researchers with different geographical locations. The Grid has a hierarchical topology with multiple sites distributed over the world. The sites differ from each other by their number of CPUs, amount of disk storage and connection bandwidth. These parameters are essential for the Grid work. Moreover, job scheduling and data distribution strategy have a great impact on the grid performance. However, it is hard to choose an appropriate algorithm and strategies as they need a lot of time to be tested on the real grid. In this study, we describe the LHCb Grid simulator. The simulator reproduces the LHCb Grid structure with its sites and their number of CPUs, amount of disk storage and bandwidth connection. We demonstrate how well the simulator reproduces the grid work, show its advantages and limitations. We show how well the simulator reproduces job scheduling and network anomalies, consider methods ...

Fractal analysis and nonlinear forecasting of indoor 222Rn time series

International Nuclear Information System (INIS)

Pausch, G.; Bossew, P.; Hofmann, W.; Steger, F.

1998-01-01

Fractal analyses of indoor 222 Rn time series were performed using different chaos theory based measurements such as time delay method, Hurst's rescaled range analysis, capacity (fractal) dimension, and Lyapunov exponent. For all time series we calculated only positive Lyapunov exponents which is a hint to chaos, while the Hurst exponents were well below 0.5, indicating antipersistent behaviour (past trends tend to reverse in the future). These time series were also analyzed with a nonlinear prediction method which allowed an estimation of the embedding dimensions with some restrictions, limiting the prediction to about three relative time steps. (orig.)

Testing for intracycle determinism in pseudoperiodic time series.

Science.gov (United States)

Coelho, Mara C S; Mendes, Eduardo M A M; Aguirre, Luis A

2008-06-01

A determinism test is proposed based on the well-known method of the surrogate data. Assuming predictability to be a signature of determinism, the proposed method checks for intracycle (e.g., short-term) determinism in the pseudoperiodic time series for which standard methods of surrogate analysis do not apply. The approach presented is composed of two steps. First, the data are preprocessed to reduce the effects of seasonal and trend components. Second, standard tests of surrogate analysis can then be used. The determinism test is applied to simulated and experimental pseudoperiodic time series and the results show the applicability of the proposed test.

Time series analysis and its applications with R examples

CERN Document Server

Shumway, Robert H

2017-01-01

The fourth edition of this popular graduate textbook, like its predecessors, presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Numerous examples using nontrivial data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and monitoring a nuclear test ban treaty. The book is designed as a textbook for graduate level students in the physical, biological, and social sciences and as a graduate level text in statistics. Some parts may also serve as an undergraduate introductory course. Theory and methodology are separated to allow presentations on different levels. In addition to coverage of classical methods of time series regression, ARIMA models, spectral analysis and state-space models, the text includes modern developments including categorical time series analysis, multivariate spectral methods, long memory series, nonli...

A KST framework for correlation network construction from time series signals

Science.gov (United States)

Qi, Jin-Peng; Gu, Quan; Zhu, Ying; Zhang, Ping

2018-04-01

A KST (Kolmogorov-Smirnov test and T statistic) method is used for construction of a correlation network based on the fluctuation of each time series within the multivariate time signals. In this method, each time series is divided equally into multiple segments, and the maximal data fluctuation in each segment is calculated by a KST change detection procedure. Connections between each time series are derived from the data fluctuation matrix, and are used for construction of the fluctuation correlation network (FCN). The method was tested with synthetic simulations and the result was compared with those from using KS or T only for detection of data fluctuation. The novelty of this study is that the correlation analyses was based on the data fluctuation in each segment of each time series rather than on the original time signals, which would be more meaningful for many real world applications and for analysis of large-scale time signals where prior knowledge is uncertain.

Multivariate stochastic analysis for Monthly hydrological time series at Cuyahoga River Basin

Science.gov (United States)

zhang, L.

2011-12-01

Copula has become a very powerful statistic and stochastic methodology in case of the multivariate analysis in Environmental and Water resources Engineering. In recent years, the popular one-parameter Archimedean copulas, e.g. Gumbel-Houggard copula, Cook-Johnson copula, Frank copula, the meta-elliptical copula, e.g. Gaussian Copula, Student-T copula, etc. have been applied in multivariate hydrological analyses, e.g. multivariate rainfall (rainfall intensity, duration and depth), flood (peak discharge, duration and volume), and drought analyses (drought length, mean and minimum SPI values, and drought mean areal extent). Copula has also been applied in the flood frequency analysis at the confluences of river systems by taking into account the dependence among upstream gauge stations rather than by using the hydrological routing technique. In most of the studies above, the annual time series have been considered as stationary signal which the time series have been assumed as independent identically distributed (i.i.d.) random variables. But in reality, hydrological time series, especially the daily and monthly hydrological time series, cannot be considered as i.i.d. random variables due to the periodicity existed in the data structure. Also, the stationary assumption is also under question due to the Climate Change and Land Use and Land Cover (LULC) change in the fast years. To this end, it is necessary to revaluate the classic approach for the study of hydrological time series by relaxing the stationary assumption by the use of nonstationary approach. Also as to the study of the dependence structure for the hydrological time series, the assumption of same type of univariate distribution also needs to be relaxed by adopting the copula theory. In this paper, the univariate monthly hydrological time series will be studied through the nonstationary time series analysis approach. The dependence structure of the multivariate monthly hydrological time series will be

Forecasting daily meteorological time series using ARIMA and regression models

Science.gov (United States)

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.

Analysis of complex time series using refined composite multiscale entropy

International Nuclear Information System (INIS)

Wu, Shuen-De; Wu, Chiu-Wen; Lin, Shiou-Gwo; Lee, Kung-Yen; Peng, Chung-Kang

2014-01-01

Multiscale entropy (MSE) is an effective algorithm for measuring the complexity of a time series that has been applied in many fields successfully. However, MSE may yield an inaccurate estimation of entropy or induce undefined entropy because the coarse-graining procedure reduces the length of a time series considerably at large scales. Composite multiscale entropy (CMSE) was recently proposed to improve the accuracy of MSE, but it does not resolve undefined entropy. Here we propose a refined composite multiscale entropy (RCMSE) to improve CMSE. For short time series analyses, we demonstrate that RCMSE increases the accuracy of entropy estimation and reduces the probability of inducing undefined entropy.

Compounding approach for univariate time series with nonstationary variances

Science.gov (United States)

Schäfer, Rudi; Barkhofen, Sonja; Guhr, Thomas; Stöckmann, Hans-Jürgen; Kuhl, Ulrich

2015-12-01

A defining feature of nonstationary systems is the time dependence of their statistical parameters. Measured time series may exhibit Gaussian statistics on short time horizons, due to the central limit theorem. The sample statistics for long time horizons, however, averages over the time-dependent variances. To model the long-term statistical behavior, we compound the local distribution with the distribution of its parameters. Here, we consider two concrete, but diverse, examples of such nonstationary systems: the turbulent air flow of a fan and a time series of foreign exchange rates. Our main focus is to empirically determine the appropriate parameter distribution for the compounding approach. To this end, we extract the relevant time scales by decomposing the time signals into windows and determine the distribution function of the thus obtained local variances.

Production grid systems and their programming

CERN Document Server

Kacsuk, P; Stefan, P

2004-01-01

Summary form only given. There are a large variety of grid test-beds that can be used for experimental purposes by a small community. However, the number of production grid systems that can be used as a service for a large community is very limited. The current tutorial provides introduction to three of these very few production grid systems. They represent different models and policies of using grid resources and hence understanding and comparing them is an extremely useful exercise to everyone interested in grid technology. The Hungarian ClusterGrid infrastructure connects clusters during the nights and weekends. These clusters are used during the day for educational purposes at the Hungarian universities and polytechnics. Therefore, a unique feature of this grid is the switching mechanism by which the day time and night time working modes are loaded to the computers. In order to manage the system as a production, one, the system is homogeneous, all the machines should install the same grid software package...

Tools for Generating Useful Time-series Data from PhenoCam Images

Science.gov (United States)

Milliman, T. E.; Friedl, M. A.; Frolking, S.; Hufkens, K.; Klosterman, S.; Richardson, A. D.; Toomey, M. P.

2012-12-01

The PhenoCam project (http://phenocam.unh.edu/) is tasked with acquiring, processing, and archiving digital repeat photography to be used for scientific studies of vegetation phenological processes. Over the past 5 years the PhenoCam project has collected over 2 million time series images for a total over 700 GB of image data. Several papers have been published describing derived "vegetation indices" (such as green-chromatic-coordinate or gcc) which can be compared to standard measures such as NDVI or EVI. Imagery from our archive is available for download but converting series of images for a particular camera into useful scientific data, while simple in principle, is complicated by a variety of factors. Cameras are often exposed to harsh weather conditions (high wind, rain, ice, snow pile up), which result in images where the field of view (FOV) is partially obscured or completely blocked for periods of time. The FOV can also change for other reasons (mount failures, tower maintenance, etc.) Some of the relatively inexpensive cameras that are being used can also temporarily lose color balance or exposure controls resulting in loss of imagery. All these factors negatively influence the automated analysis of the image time series making this a non-trivial task. Here we discuss the challenges of processing PhenoCam image time-series for vegetation monitoring and the associated data management tasks. We describe our current processing framework and a simple standardized output format for the resulting time-series data. The time-series data in this format will be generated for specific "regions of interest" (ROI's) for each of the cameras in the PhenoCam network. This standardized output (which will be updated daily) can be considered 'the pulse' of a particular camera and will provide a default phenological dynamic for said camera. The time-series data can also be viewed as a higher level product which can be used to generate "vegetation indices", like gcc, for

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

Grid interoperability: joining grid information systems

International Nuclear Information System (INIS)

Flechl, M; Field, L

2008-01-01

A grid is defined as being 'coordinated resource sharing and problem solving in dynamic, multi-institutional virtual organizations'. Over recent years a number of grid projects, many of which have a strong regional presence, have emerged to help coordinate institutions and enable grids. Today, we face a situation where a number of grid projects exist, most of which are using slightly different middleware. Grid interoperation is trying to bridge these differences and enable Virtual Organizations to access resources at the institutions independent of their grid project affiliation. Grid interoperation is usually a bilateral activity between two grid infrastructures. Recently within the Open Grid Forum, the Grid Interoperability Now (GIN) Community Group is trying to build upon these bilateral activities. The GIN group is a focal point where all the infrastructures can come together to share ideas and experiences on grid interoperation. It is hoped that each bilateral activity will bring us one step closer to the overall goal of a uniform grid landscape. A fundamental aspect of a grid is the information system, which is used to find available grid services. As different grids use different information systems, interoperation between these systems is crucial for grid interoperability. This paper describes the work carried out to overcome these differences between a number of grid projects and the experiences gained. It focuses on the different techniques used and highlights the important areas for future standardization

Research on wind power grid-connected operation and dispatching strategies of Liaoning power grid

Science.gov (United States)

Han, Qiu; Qu, Zhi; Zhou, Zhi; He, Xiaoyang; Li, Tie; Jin, Xiaoming; Li, Jinze; Ling, Zhaowei

2018-02-01

As a kind of clean energy, wind power has gained rapid development in recent years. Liaoning Province has abundant wind resources and the total installed capacity of wind power is in the forefront. With the large-scale wind power grid-connected operation, the contradiction between wind power utilization and peak load regulation of power grid has been more prominent. To this point, starting with the power structure and power grid installation situation of Liaoning power grid, the distribution and the space-time output characteristics of wind farm, the prediction accuracy, the curtailment and the off-grid situation of wind power are analyzed. Based on the deep analysis of the seasonal characteristics of power network load, the composition and distribution of main load are presented. Aiming at the problem between the acceptance of wind power and power grid adjustment, the scheduling strategies are given, including unit maintenance scheduling, spinning reserve, energy storage equipment settings by the analysis of the operation characteristics and the response time of thermal power units and hydroelectric units, which can meet the demand of wind power acceptance and provide a solution to improve the level of power grid dispatching.

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

Science.gov (United States)

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

2016-01-01

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.

FTSPlot: fast time series visualization for large datasets.

Directory of Open Access Journals (Sweden)

Michael Riss

Full Text Available The analysis of electrophysiological recordings often involves visual inspection of time series data to locate specific experiment epochs, mask artifacts, and verify the results of signal processing steps, such as filtering or spike detection. Long-term experiments with continuous data acquisition generate large amounts of data. Rapid browsing through these massive datasets poses a challenge to conventional data plotting software because the plotting time increases proportionately to the increase in the volume of data. This paper presents FTSPlot, which is a visualization concept for large-scale time series datasets using techniques from the field of high performance computer graphics, such as hierarchic level of detail and out-of-core data handling. In a preprocessing step, time series data, event, and interval annotations are converted into an optimized data format, which then permits fast, interactive visualization. The preprocessing step has a computational complexity of O(n x log(N; the visualization itself can be done with a complexity of O(1 and is therefore independent of the amount of data. A demonstration prototype has been implemented and benchmarks show that the technology is capable of displaying large amounts of time series data, event, and interval annotations lag-free with < 20 ms ms. The current 64-bit implementation theoretically supports datasets with up to 2(64 bytes, on the x86_64 architecture currently up to 2(48 bytes are supported, and benchmarks have been conducted with 2(40 bytes/1 TiB or 1.3 x 10(11 double precision samples. The presented software is freely available and can be included as a Qt GUI component in future software projects, providing a standard visualization method for long-term electrophysiological experiments.

Normalization methods in time series of platelet function assays

Science.gov (United States)

Van Poucke, Sven; Zhang, Zhongheng; Roest, Mark; Vukicevic, Milan; Beran, Maud; Lauwereins, Bart; Zheng, Ming-Hua; Henskens, Yvonne; Lancé, Marcus; Marcus, Abraham

2016-01-01

Abstract Platelet function can be quantitatively assessed by specific assays such as light-transmission aggregometry, multiple-electrode aggregometry measuring the response to adenosine diphosphate (ADP), arachidonic acid, collagen, and thrombin-receptor activating peptide and viscoelastic tests such as rotational thromboelastometry (ROTEM). The task of extracting meaningful statistical and clinical information from high-dimensional data spaces in temporal multivariate clinical data represented in multivariate time series is complex. Building insightful visualizations for multivariate time series demands adequate usage of normalization techniques. In this article, various methods for data normalization (z-transformation, range transformation, proportion transformation, and interquartile range) are presented and visualized discussing the most suited approach for platelet function data series. Normalization was calculated per assay (test) for all time points and per time point for all tests. Interquartile range, range transformation, and z-transformation demonstrated the correlation as calculated by the Spearman correlation test, when normalized per assay (test) for all time points. When normalizing per time point for all tests, no correlation could be abstracted from the charts as was the case when using all data as 1 dataset for normalization. PMID:27428217

Improving Grid Resilience through Informed Decision-making (IGRID)

Energy Technology Data Exchange (ETDEWEB)

Burnham, Laurie [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Power Systems Research; Stamber, Kevin L. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Systems Research, Analysis and Applications; Jeffers, Robert Fredric [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Resilience and Regulatory Effects; Stevens-Adams, Susan Marie [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Human Factors; Verzi, Stephen Joseph [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Systems Research, Analysis and Applications; Galiardi, Meghan [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Systems Research, Analysis and Applications; Haass, Michael Joseph [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Cognitive Systems; Cauthen, Katherine Regina [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Systems Research, Analysis and Applications

2016-09-01

The transformation of the distribution grid from a centralized to decentralized architecture, with bi-directional power and data flows, is made possible by a surge in network intelligence and grid automation. While changes are largely beneficial, the interface between grid operator and automated technologies is not well understood, nor are the benefits and risks of automation. Quantifying and understanding the latter is an important facet of grid resilience that needs to be fully investigated. The work described in this document represents the first empirical study aimed at identifying and mitigating the vulnerabilities posed by automation for a grid that for the foreseeable future will remain a human-in-the-loop critical infrastructure. Our scenario-based methodology enabled us to conduct a series of experimental studies to identify causal relationships between grid-operator performance and automated technologies and to collect measurements of human performance as a function of automation. Our findings, though preliminary, suggest there are predictive patterns in the interplay between human operators and automation, patterns that can inform the rollout of distribution automation and the hiring and training of operators, and contribute in multiple and significant ways to the field of grid resilience.

An LLCL Power Filter for Single-Phase Grid-Tied Inverter

DEFF Research Database (Denmark)

Wu, Weimin; He, Yuanbin; Blaabjerg, Frede

2012-01-01

This paper presents a new topology of higher order power filter for grid-tied voltage-source inverters, named the LLCL filter, which inserts a small inductor in the branch loop of the capacitor in the traditional LCL filter to compose a series resonant circuit at the switching frequency. Particul......This paper presents a new topology of higher order power filter for grid-tied voltage-source inverters, named the LLCL filter, which inserts a small inductor in the branch loop of the capacitor in the traditional LCL filter to compose a series resonant circuit at the switching frequency...... to the inverter system control. The parameter design criteria of the proposed LLCL filter is also introduced. The comparative analysis and discussions regarding the traditional LCL filter and the proposed LLCL filter have been presented and evaluated through experiment on a 1.8-kW-single-phase grid-tied inverter...

Development and application of a modified dynamic time warping algorithm (DTW-S) to analyses of primate brain expression time series.

Science.gov (United States)

Yuan, Yuan; Chen, Yi-Ping Phoebe; Ni, Shengyu; Xu, Augix Guohua; Tang, Lin; Vingron, Martin; Somel, Mehmet; Khaitovich, Philipp

2011-08-18

Comparing biological time series data across different conditions, or different specimens, is a common but still challenging task. Algorithms aligning two time series represent a valuable tool for such comparisons. While many powerful computation tools for time series alignment have been developed, they do not provide significance estimates for time shift measurements. Here, we present an extended version of the original DTW algorithm that allows us to determine the significance of time shift estimates in time series alignments, the DTW-Significance (DTW-S) algorithm. The DTW-S combines important properties of the original algorithm and other published time series alignment tools: DTW-S calculates the optimal alignment for each time point of each gene, it uses interpolated time points for time shift estimation, and it does not require alignment of the time-series end points. As a new feature, we implement a simulation procedure based on parameters estimated from real time series data, on a series-by-series basis, allowing us to determine the false positive rate (FPR) and the significance of the estimated time shift values. We assess the performance of our method using simulation data and real expression time series from two published primate brain expression datasets. Our results show that this method can provide accurate and robust time shift estimates for each time point on a gene-by-gene basis. Using these estimates, we are able to uncover novel features of the biological processes underlying human brain development and maturation. The DTW-S provides a convenient tool for calculating accurate and robust time shift estimates at each time point for each gene, based on time series data. The estimates can be used to uncover novel biological features of the system being studied. The DTW-S is freely available as an R package TimeShift at http://www.picb.ac.cn/Comparative/data.html.

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.

Science.gov (United States)

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

hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature Extraction.

Science.gov (United States)

Fulcher, Ben D; Jones, Nick S

2017-11-22

Phenotype measurements frequently take the form of time series, but we currently lack a systematic method for relating these complex data streams to scientifically meaningful outcomes, such as relating the movement dynamics of organisms to their genotype or measurements of brain dynamics of a patient to their disease diagnosis. Previous work addressed this problem by comparing implementations of thousands of diverse scientific time-series analysis methods in an approach termed highly comparative time-series analysis. Here, we introduce hctsa, a software tool for applying this methodological approach to data. hctsa includes an architecture for computing over 7,700 time-series features and a suite of analysis and visualization algorithms to automatically select useful and interpretable time-series features for a given application. Using exemplar applications to high-throughput phenotyping experiments, we show how hctsa allows researchers to leverage decades of time-series research to quantify and understand informative structure in time-series data. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models

Science.gov (United States)

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…

Applied time series analysis and innovative computing

CERN Document Server

Ao, Sio-Iong

2010-01-01

This text is a systematic, state-of-the-art introduction to the use of innovative computing paradigms as an investigative tool for applications in time series analysis. It includes frontier case studies based on recent research.

On Stabilizing the Variance of Dynamic Functional Brain Connectivity Time Series.

Science.gov (United States)

Thompson, William Hedley; Fransson, Peter

2016-12-01

Assessment of dynamic functional brain connectivity based on functional magnetic resonance imaging (fMRI) data is an increasingly popular strategy to investigate temporal dynamics of the brain's large-scale network architecture. Current practice when deriving connectivity estimates over time is to use the Fisher transformation, which aims to stabilize the variance of correlation values that fluctuate around varying true correlation values. It is, however, unclear how well the stabilization of signal variance performed by the Fisher transformation works for each connectivity time series, when the true correlation is assumed to be fluctuating. This is of importance because many subsequent analyses either assume or perform better when the time series have stable variance or adheres to an approximate Gaussian distribution. In this article, using simulations and analysis of resting-state fMRI data, we analyze the effect of applying different variance stabilization strategies on connectivity time series. We focus our investigation on the Fisher transformation, the Box-Cox (BC) transformation and an approach that combines both transformations. Our results show that, if the intention of stabilizing the variance is to use metrics on the time series, where stable variance or a Gaussian distribution is desired (e.g., clustering), the Fisher transformation is not optimal and may even skew connectivity time series away from being Gaussian. Furthermore, we show that the suboptimal performance of the Fisher transformation can be substantially improved by including an additional BC transformation after the dynamic functional connectivity time series has been Fisher transformed.

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

Energy Technology Data Exchange (ETDEWEB)

Lefieux, V

2007-10-15

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

Characteristics of the transmission of autoregressive sub-patterns in financial time series

Science.gov (United States)

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong

2014-09-01

There are many types of autoregressive patterns in financial time series, and they form a transmission process. Here, we define autoregressive patterns quantitatively through an econometrical regression model. We present a computational algorithm that sets the autoregressive patterns as nodes and transmissions between patterns as edges, and then converts the transmission process of autoregressive patterns in a time series into a network. We utilised daily Shanghai (securities) composite index time series to study the transmission characteristics of autoregressive patterns. We found statistically significant evidence that the financial market is not random and that there are similar characteristics between parts and whole time series. A few types of autoregressive sub-patterns and transmission patterns drive the oscillations of the financial market. A clustering effect on fluctuations appears in the transmission process, and certain non-major autoregressive sub-patterns have high media capabilities in the financial time series. Different stock indexes exhibit similar characteristics in the transmission of fluctuation information. This work not only proposes a distinctive perspective for analysing financial time series but also provides important information for investors.

A Review of Some Aspects of Robust Inference for Time Series.

Science.gov (United States)

1984-09-01

REVIEW OF SOME ASPECTSOF ROBUST INFERNCE FOR TIME SERIES by Ad . Dougla Main TE "iAL REPOW No. 63 Septermber 1984 Department of Statistics University of ...clear. One cannot hope to have a good method for dealing with outliers in time series by using only an instantaneous nonlinear transformation of the data...AI.49 716 A REVIEWd OF SOME ASPECTS OF ROBUST INFERENCE FOR TIME 1/1 SERIES(U) WASHINGTON UNIV SEATTLE DEPT OF STATISTICS R D MARTIN SEP 84 TR-53

Refined composite multiscale weighted-permutation entropy of financial time series

Science.gov (United States)

Zhang, Yongping; Shang, Pengjian

2018-04-01

For quantifying the complexity of nonlinear systems, multiscale weighted-permutation entropy (MWPE) has recently been proposed. MWPE has incorporated amplitude information and been applied to account for the multiple inherent dynamics of time series. However, MWPE may be unreliable, because its estimated values show large fluctuation for slight variation of the data locations, and a significant distinction only for the different length of time series. Therefore, we propose the refined composite multiscale weighted-permutation entropy (RCMWPE). By comparing the RCMWPE results with other methods' results on both synthetic data and financial time series, RCMWPE method shows not only the advantages inherited from MWPE but also lower sensitivity to the data locations, more stable and much less dependent on the length of time series. Moreover, we present and discuss the results of RCMWPE method on the daily price return series from Asian and European stock markets. There are significant differences between Asian markets and European markets, and the entropy values of Hang Seng Index (HSI) are close to but higher than those of European markets. The reliability of the proposed RCMWPE method has been supported by simulations on generated and real data. It could be applied to a variety of fields to quantify the complexity of the systems over multiple scales more accurately.

Parametric, nonparametric and parametric modelling of a chaotic circuit time series

Science.gov (United States)

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.

Synthetic river flow time series generator for dispatch and spot price forecast

International Nuclear Information System (INIS)

Flores, R.A.

2007-01-01

Decision-making in electricity markets is complicated by uncertainties in demand growth, power supplies and fuel prices. In Peru, where the electrical power system is highly dependent on water resources at dams and river flows, hydrological uncertainties play a primary role in planning, price and dispatch forecast. This paper proposed a signal processing method for generating new synthetic river flow time series as a support for planning and spot market price forecasting. River flow time series are natural phenomena representing a continuous-time domain process. As an alternative synthetic representation of the original river flow time series, this proposed signal processing method preserves correlations, basic statistics and seasonality. It takes into account deterministic, periodic and non periodic components such as those due to the El Nino Southern Oscillation phenomenon. The new synthetic time series has many correlations with the original river flow time series, rendering it suitable for possible replacement of the classical method of sorting historical river flow time series. As a dispatch and planning approach to spot pricing, the proposed method offers higher accuracy modeling by decomposing the signal into deterministic, periodic, non periodic and stochastic sub signals. 4 refs., 4 tabs., 13 figs