Bayesian Time Series Models

Bayesian Time Series Models

The first unified treatment of time series modelling techniques spanning machine learning, statistics, engineering and computer science.

Author: David Barber

Publisher: Cambridge University Press

ISBN: 9780521196765

Category: Computers

Page: 417

View: 955

The first unified treatment of time series modelling techniques spanning machine learning, statistics, engineering and computer science.
Categories: Computers

Time Series

Time Series

Along with core models and methods, this text offers sophisticated tools for analyzing challenging time series problems. It also demonstrates the growth of time series analysis into new application areas.

Author: Raquel Prado

Publisher: CRC Press

ISBN: 9781420093360

Category: Mathematics

Page: 368

View: 956

Focusing on Bayesian approaches and computations using simulation-based methods for inference, Time Series: Modeling, Computation, and Inference integrates mainstream approaches for time series modeling with significant recent developments in methodology and applications of time series analysis. It encompasses a graduate-level account of Bayesian time series modeling and analysis, a broad range of references to state-of-the-art approaches to univariate and multivariate time series analysis, and emerging topics at research frontiers. The book presents overviews of several classes of models and related methodology for inference, statistical computation for model fitting and assessment, and forecasting. The authors also explore the connections between time- and frequency-domain approaches and develop various models and analyses using Bayesian tools, such as Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods. They illustrate the models and methods with examples and case studies from a variety of fields, including signal processing, biomedicine, and finance. Data sets, R and MATLAB® code, and other material are available on the authors’ websites. Along with core models and methods, this text offers sophisticated tools for analyzing challenging time series problems. It also demonstrates the growth of time series analysis into new application areas.
Categories: Mathematics

Bayesian Analysis of Time Series

Bayesian Analysis of Time Series

Bayesian Analysis of Time Series discusses how to use models that explain the probabilistic characteristics of these time series and then utilizes the Bayesian approach to make inferences about their parameters.

Author: Lyle D. Broemeling

Publisher: CRC Press

ISBN: 9780429948923

Category: Mathematics

Page: 280

View: 421

In many branches of science relevant observations are taken sequentially over time. Bayesian Analysis of Time Series discusses how to use models that explain the probabilistic characteristics of these time series and then utilizes the Bayesian approach to make inferences about their parameters. This is done by taking the prior information and via Bayes theorem implementing Bayesian inferences of estimation, testing hypotheses, and prediction. The methods are demonstrated using both R and WinBUGS. The R package is primarily used to generate observations from a given time series model, while the WinBUGS packages allows one to perform a posterior analysis that provides a way to determine the characteristic of the posterior distribution of the unknown parameters. Features Presents a comprehensive introduction to the Bayesian analysis of time series. Gives many examples over a wide variety of fields including biology, agriculture, business, economics, sociology, and astronomy. Contains numerous exercises at the end of each chapter many of which use R and WinBUGS. Can be used in graduate courses in statistics and biostatistics, but is also appropriate for researchers, practitioners and consulting statisticians. About the author Lyle D. Broemeling, Ph.D., is Director of Broemeling and Associates Inc., and is a consulting biostatistician. He has been involved with academic health science centers for about 20 years and has taught and been a consultant at the University of Texas Medical Branch in Galveston, The University of Texas MD Anderson Cancer Center and the University of Texas School of Public Health. His main interest is in developing Bayesian methods for use in medical and biological problems and in authoring textbooks in statistics. His previous books for Chapman & Hall/CRC include Bayesian Biostatistics and Diagnostic Medicine, and Bayesian Methods for Agreement.
Categories: Mathematics

Bayesian Forecasting and Dynamic Models

Bayesian Forecasting and Dynamic Models

This text is concerned with Bayesian learning, inference and forecasting in dynamic environments.

Author: Mike West

Publisher: Springer Science & Business Media

ISBN: 9780387227771

Category: Mathematics

Page: 682

View: 899

This text is concerned with Bayesian learning, inference and forecasting in dynamic environments. We describe the structure and theory of classes of dynamic models and their uses in forecasting and time series analysis. The principles, models and methods of Bayesian forecasting and time - ries analysis have been developed extensively during the last thirty years. Thisdevelopmenthasinvolvedthoroughinvestigationofmathematicaland statistical aspects of forecasting models and related techniques. With this has come experience with applications in a variety of areas in commercial, industrial, scienti?c, and socio-economic ?elds. Much of the technical - velopment has been driven by the needs of forecasting practitioners and applied researchers. As a result, there now exists a relatively complete statistical and mathematical framework, presented and illustrated here. In writing and revising this book, our primary goals have been to present a reasonably comprehensive view of Bayesian ideas and methods in m- elling and forecasting, particularly to provide a solid reference source for advanced university students and research workers.
Categories: Mathematics

Multiple Time Series Models

Multiple Time Series Models

Multiple Time Series Models introduces researchers and students to the different approaches to modeling multivariate time series data including simultaneous equations, ARIMA, error correction models, and vector autoregression.

Author: Patrick T. Brandt

Publisher: SAGE

ISBN: 9781412906562

Category: Mathematics

Page: 99

View: 117

Multiple Time Series Models introduces researchers and students to the different approaches to modeling multivariate time series data including simultaneous equations, ARIMA, error correction models, and vector autoregression. Authors Patrick T. Brandt and John T. Williams focus on vector autoregression (VAR) models as a generalization of these other approaches and discuss specification, estimation, and inference using these models.
Categories: Mathematics

Time Series Analysis by State Space Methods

Time Series Analysis by State Space Methods

This is a comprehensive treatment of the state space approach to time series analysis.

Author: James Durbin

Publisher: Oxford University Press

ISBN: 9780199641178

Category: Business & Economics

Page: 346

View: 526

This new edition updates Durbin & Koopman's important text on the state space approach to time series analysis providing a more comprehensive treatment, including the filtering of nonlinear and non-Gaussian series. The book provides an excellent source for the development of practical courses on time series analysis.
Categories: Business & Economics

Maximum Entropy and Bayesian Methods

Maximum Entropy and Bayesian Methods

The reader may have detected the fact that many of the models described above
are not necessarily associated with Bayesian time series . The reader may also
have detected that the observation is irrelevant . Good , useful models can be ...

Author: Kenneth M. Hanson

Publisher: Springer Science & Business Media

ISBN: UCSD:31822023890585

Category: Mathematics

Page: 451

View: 798

This volume contains the proceedings of the Fifteenth International Workshop on Maximum Entropy and Bayesian Methods, held in Santa Fe, New Mexico, U.S.A., from July 31-August 4, 1995. Maximum entropy and Bayesian methods are widely applied to statistical data analysis and scientific inference in the natural and social sciences, engineering and medicine. Practical applications include, among others, parametric model fitting and model selection, ill-posed inverse problems, image reconstruction signal processing, decision making, and spectrum estimation. Fundamental applications include the common foundations for statistical inference, statistical physics and information theory. Specific sessions during the workshop focused on time series analysis, machine learning, deformable geometric models, and data analysis of Monte Carlo simulations, as well as reviewing the relation between maximum entropy and information theory. Audience: This book should be of interest to scientists, engineers, medical professionals, and others engaged in such topics as data analysis, statistical inference, image processing, and signal processing.
Categories: Mathematics

Time Series

Time Series

Along with core models and methods, the book represents state-of-the art approaches to analysis and forecasting in challenging time series problems.

Author: Raquel Prado

Publisher: Chapman & Hall/CRC

ISBN: 1498747027

Category: Time-series analysis

Page: 99999

View: 264

Focusing on Bayesian approaches and computations using analytic and simulation-based methods for inference, Time Series: Modeling, Computation, and Inference, Second Edition integrates mainstream approaches for time series modeling with significant recent developments in methodology and applications of time series analysis. It encompasses a graduate-level account of Bayesian time series modeling, analysis and forecasting, a broad range of references to state-of-the-art approaches to univariate and multivariate time series analysis, and contacts research frontiers in multivariate time series modeling and forecasting. It presents overviews of several classes of models and related methodology for inference, statistical computation for model fitting and assessment, and forecasting. It explores the connections between time- and frequency-domain approaches and develop various models and analyses using Bayesian formulations and computation, including use of computations based on Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods. It illustrates the models and methods with examples and case studies from a variety of fields, including signal processing, biomedicine, environmental science, and finance. Along with core models and methods, the book represents state-of-the art approaches to analysis and forecasting in challenging time series problems. It also demonstrates the growth of time series analysis into new application areas in recent years, and contacts recent and relevant modeling developments and research challenges. New in the second edition: Expanded on aspects of core model theory and methodology. Multiple new examples and exercises. Detailed development of dynamic factor models. Updated discussion and connections with recent and current research frontiers.
Categories: Time-series analysis

Smoothness Priors Analysis of Time Series

Smoothness Priors Analysis of Time Series

Smoothness Priors Analysis of Time Series addresses some of the problems of modeling stationary and nonstationary time series primarily from a Bayesian stochastic regression "smoothness priors" state space point of view.

Author: Genshiro Kitagawa

Publisher: Springer Science & Business Media

ISBN: 9781461207610

Category: Mathematics

Page: 280

View: 115

Smoothness Priors Analysis of Time Series addresses some of the problems of modeling stationary and nonstationary time series primarily from a Bayesian stochastic regression "smoothness priors" state space point of view. Prior distributions on model coefficients are parametrized by hyperparameters. Maximizing the likelihood of a small number of hyperparameters permits the robust modeling of a time series with relatively complex structure and a very large number of implicitly inferred parameters. The critical statistical ideas in smoothness priors are the likelihood of the Bayesian model and the use of likelihood as a measure of the goodness of fit of the model. The emphasis is on a general state space approach in which the recursive conditional distributions for prediction, filtering, and smoothing are realized using a variety of nonstandard methods including numerical integration, a Gaussian mixture distribution-two filter smoothing formula, and a Monte Carlo "particle-path tracing" method in which the distributions are approximated by many realizations. The methods are applicable for modeling time series with complex structures.
Categories: Mathematics

Applied Bayesian Forecasting and Time Series Analysis

Applied Bayesian Forecasting and Time Series Analysis

Accessible to undergraduates, this unique volume also offers complete guidelines valuable to researchers, practitioners, and advanced students in statistics, operations research, and engineering.

Author: Andy Pole

Publisher: CRC Press

ISBN: 9781482267433

Category: Business & Economics

Page: 432

View: 448

Practical in its approach, Applied Bayesian Forecasting and Time Series Analysis provides the theories, methods, and tools necessary for forecasting and the analysis of time series. The authors unify the concepts, model forms, and modeling requirements within the framework of the dynamic linear mode (DLM). They include a complete theoretical development of the DLM and illustrate each step with analysis of time series data. Using real data sets the authors: Explore diverse aspects of time series, including how to identify, structure, explain observed behavior, model structures and behaviors, and interpret analyses to make informed forecasts Illustrate concepts such as component decomposition, fundamental model forms including trends and cycles, and practical modeling requirements for routine change and unusual events Conduct all analyses in the BATS computer programs, furnishing online that program and the more than 50 data sets used in the text The result is a clear presentation of the Bayesian paradigm: quantified subjective judgements derived from selected models applied to time series observations. Accessible to undergraduates, this unique volume also offers complete guidelines valuable to researchers, practitioners, and advanced students in statistics, operations research, and engineering.
Categories: Business & Economics

Time Series Analysis

Time Series Analysis

The book is also a useful reference for researchers and practitioners in time series analysis, econometrics, and finance.

Author: Wilfredo Palma

Publisher: John Wiley & Sons

ISBN: 9781118634233

Category: Mathematics

Page: 616

View: 467

A modern and accessible guide to the analysis of introductory time series data Featuring an organized and self-contained guide, Time Series Analysis provides a broad introduction to the most fundamental methodologies and techniques of time series analysis. The book focuses on the treatment of univariate time series by illustrating a number of well-known models such as ARMA and ARIMA. Providing contemporary coverage, the book features several useful and newlydeveloped techniques such as weak and strong dependence, Bayesian methods, non-Gaussian data, local stationarity, missing values and outliers, and threshold models. Time Series Analysis includes practical applications of time series methods throughout, as well as: Real-world examples and exercise sets that allow readers to practice the presented methods and techniques Numerous detailed analyses of computational aspects related to the implementation of methodologies including algorithm efficiency, arithmetic complexity, and process time End-of-chapter proposed problems and bibliographical notes to deepen readers’ knowledge of the presented material Appendices that contain details on fundamental concepts and select solutions of the problems implemented throughout A companion website with additional data fi les and computer codes Time Series Analysis is an excellent textbook for undergraduate and beginning graduate-level courses in time series as well as a supplement for students in advanced statistics, mathematics, economics, finance, engineering, and physics. The book is also a useful reference for researchers and practitioners in time series analysis, econometrics, and finance. Wilfredo Palma, PhD, is Professor of Statistics in the Department of Statistics at Pontificia Universidad Católica de Chile. He has published several refereed articles and has received over a dozen academic honors and awards. His research interests include time series analysis, prediction theory, state space systems, linear models, and econometrics. He is the author of Long-Memory Time Series: Theory and Methods, also published by Wiley.
Categories: Mathematics

Time Series

Time Series

+ - SO + the first differences of a random walk model and an MA ( 1 ) model to be
equivalent ( assuming normal noises ) ... 6.8 BAYESIAN TIME SERIES MODELS
A class of models has been developed which appears to resemble the general ...

Author: G. J. Janacek

Publisher: Ellis Horwood Limited

ISBN: UCSD:31822036020055

Category: Mathematics

Page: 331

View: 616

This introduction to time series analysis has been written for undergraduates and postgraduates, and assumes some basic statistical knowledge. Using a general state space model, the authors draw together methodologies to enable the development of methods for estimation and forecasting.
Categories: Mathematics

Nonlinear Econometric Modeling in Time Series

Nonlinear Econometric Modeling in Time Series

This book presents some of the more recent developments in nonlinear time series, including Bayesian analysis and cointegration tests.

Author: International Symposium in Economic Theory and Econometrics

Publisher: Cambridge University Press

ISBN: 0521594243

Category: Business & Economics

Page: 227

View: 207

This book presents some of the more recent developments in nonlinear time series, including Bayesian analysis and cointegration tests.
Categories: Business & Economics

Nonstationary Time Series Analysis and Cointegration

Nonstationary Time Series Analysis and Cointegration

fixed format models . For ten out of the thirteen series considered here the Bayes
models not only improve on the forecasts of more richly parameterised models
but also encompass those forecasts . In effect , the predictive distribution of the ...

Author: Colin P. Hargreaves

Publisher: Oxford University Press, USA

ISBN: STANFORD:36105009676706

Category: Business & Economics

Page: 308

View: 849

Nonstationary Time Series Analysis and Cointegration shows major developments in the econometric analysis of the long run (of nonstationarity and cointegration) - a field which has developed dramatically over the last twelve years to have a profound effect on econometric analysis in general. The papers here describe and evaluate new methods, provide useful overviews, and show detailed implementations helpful to practitioners. Papers include two substantive analyses of economic forecasting, based around an integral understanding of integration and cointegration and an evaluation of real business cycle models. There is an evaluation of different cointegration estimators and a new test for cointegration. There is a discussion of the effects of seasonality, looking at seasonal unit roots and at encompassing modelling with seasonally unadjusted versus adjusted data. A different style of nonstationarity is raised in a discussion of testing for inflationary bubbles and for time-varying transition probabilities in Hamilton's Markov switching model. This volume provides wide-ranging coverage of the literature, showing the importance of nonstationarity and cointegration.
Categories: Business & Economics

Three Essays Involving Time Series Analysis

Three Essays Involving Time Series Analysis

Traditional Box - Jenkins time series models rely heavily on the acquired
experience of the practitioner . Traditional Bayesian time series methods can
often involve mathematical operations too complex for all but a few specialists .
There is a ...

Author: Jeffrey H. Dorfman

Publisher:

ISBN: UCAL:X39294

Category: Agriculture

Page: 204

View: 295

Categories: Agriculture

Regression Models for Time Series Analysis

Regression Models for Time Series Analysis

This book introduces the reader to newer developments and more diverse regression models and methods for time series analysis.

Author: Benjamin Kedem

Publisher: John Wiley & Sons

ISBN: 9780471461685

Category: Mathematics

Page: 360

View: 669

A thorough review of the most current regression methods in timeseries analysis Regression methods have been an integral part of time seriesanalysis for over a century. Recently, new developments have mademajor strides in such areas as non-continuous data where a linearmodel is not appropriate. This book introduces the reader to newerdevelopments and more diverse regression models and methods fortime series analysis. Accessible to anyone who is familiar with the basic modern conceptsof statistical inference, Regression Models for Time SeriesAnalysis provides a much-needed examination of recent statisticaldevelopments. Primary among them is the important class of modelsknown as generalized linear models (GLM) which provides, under someconditions, a unified regression theory suitable for continuous,categorical, and count data. The authors extend GLM methodology systematically to time serieswhere the primary and covariate data are both random andstochastically dependent. They introduce readers to variousregression models developed during the last thirty years or so andsummarize classical and more recent results concerning state spacemodels. To conclude, they present a Bayesian approach to predictionand interpolation in spatial data adapted to time series that maybe short and/or observed irregularly. Real data applications andfurther results are presented throughout by means of chapterproblems and complements. Notably, the book covers: * Important recent developments in Kalman filtering, dynamic GLMs,and state-space modeling * Associated computational issues such as Markov chain, MonteCarlo, and the EM-algorithm * Prediction and interpolation * Stationary processes
Categories: Mathematics

Handbook of Discrete Valued Time Series

Handbook of Discrete Valued Time Series

The book then provides a guide to modern methods for discrete-valued spatio-temporal data, illustrating how far modern applications have evolved from their roots. The book ends with a focus on multivariate and long-memory count series.

Author: Richard A. Davis

Publisher: Chapman and Hall/CRC

ISBN: 1466577738

Category: Mathematics

Page: 488

View: 516

Model a Wide Range of Count Time Series Handbook of Discrete-Valued Time Series presents state-of-the-art methods for modeling time series of counts and incorporates frequentist and Bayesian approaches for discrete-valued spatio-temporal data and multivariate data. While the book focuses on time series of counts, some of the techniques discussed can be applied to other types of discrete-valued time series, such as binary-valued or categorical time series. Explore a Balanced Treatment of Frequentist and Bayesian Perspectives Accessible to graduate-level students who have taken an elementary class in statistical time series analysis, the book begins with the history and current methods for modeling and analyzing univariate count series. It next discusses diagnostics and applications before proceeding to binary and categorical time series. The book then provides a guide to modern methods for discrete-valued spatio-temporal data, illustrating how far modern applications have evolved from their roots. The book ends with a focus on multivariate and long-memory count series. Get Guidance from Masters in the Field Written by a cohesive group of distinguished contributors, this handbook provides a unified account of the diverse techniques available for observation- and parameter-driven models. It covers likelihood and approximate likelihood methods, estimating equations, simulation methods, and a Bayesian approach for model fitting.
Categories: Mathematics