
Time Series
Modeling, Computation, and Inference
Chapman & Hall/CRC (Publisher)
1st Edition
Published on 21. May 2010
Book
Hardback
368 pages
978-1-4200-9336-0 (ISBN)
Article exhausted; check for reprint
Description
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 (R) 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.
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 (R) 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.
Reviews / Votes
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 2012
My 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 literature, including 85 references from 2008 and later. It is well-written and I spotted very few typos. This textbook can undoubtedly work as a reference manual for anyone entering the field or looking for an update. ... I am certain there is more than enough material within Time Series to fill an intense one-semester course.
-International Statistical Review (2011), 79
More details
Series
Language
English
Place of publication
Oxford
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Professional
Product notice
Paper over boards
Dimensions
Height: 234 mm
Width: 156 mm
Weight
635 gr
ISBN-13
978-1-4200-9336-0 (9781420093360)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions

Raquel Prado | Marco A. R. Ferreira | Mike West
Time Series
Modeling, Computation, and Inference, Second Edition
Book
07/2021
2nd Edition
Chapman & Hall/CRC
€134.10
Shipment within 15-20 days
Persons
Raquel Prado is an associate professor in the Department of Applied Mathematics and Statistics at the University of California, Santa Cruz.
Mike West is the Arts & Sciences Professor of Statistical Science in the Department of Statistical Science at Duke University.
Mike West is the Arts & Sciences Professor of Statistical Science in the Department of Statistical Science at Duke University.
Content
Notation, Definitions, and Basic Inference. Traditional Time Domain Models. The Frequency Domain. Dynamic Linear Models. State-Space Time-Varying Autoregressive Models. Sequential Monte Carlo Methods for State-Space Models. Mixture Models in Time Series. Topics and Examples in Multiple Time Series. Vector AR and ARMA Models. Multivariate DLMs and Covariance Models. Indices. Bibliography.