Time Series: Modeling, Computation, and Inference

Raquel Prado, Mike West

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May 21, 2010 by Chapman and Hall/CRC
Textbook - 368 Pages - 83 B/W Illustrations
ISBN 9781420093360 - CAT# C9336
Series: Chapman & Hall/CRC Texts in Statistical Science

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Features

  • Covers the major areas of modern time series models and theory, including time and spectral domain and univariate and multivariate time series methods
  • Presents analyses of real time series data in numerous examples and case studies to illustrate the flexibility and practical impact of the models and methods
  • Emphasizes model-based, computationally intensive analysis of structured time series
  • Discusses recent techniques for modeling time series data, such as dynamic graphical models, SMC methods, and nonlinear/non-Gaussian dynamic models
  • Includes a collection of end-of-chapter exercises
  • Offers many of the data sets, R and MATLAB® code, and other material on the authors’ websites

Summary

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.