Time Series Analysis and Its Applications

1. Front Matter

   Pages i-xiii

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2. Characteristics of Time Series

   • Robert H. Shumway, David S. Stoffer

   Pages 1-44

3. Time Series Regression and Exploratory Data Analysis

   • Robert H. Shumway, David S. Stoffer

   Pages 45-74

4. ARIMA Models

   • Robert H. Shumway, David S. Stoffer

   Pages 75-163

5. Spectral Analysis and Filtering

   • Robert H. Shumway, David S. Stoffer

   Pages 165-239

6. Additional Time Domain Topics

   • Robert H. Shumway, David S. Stoffer

   Pages 241-287

7. State Space Models

   • Robert H. Shumway, David S. Stoffer

   Pages 289-384

8. Statistical Methods in the Frequency Domain

   • Robert H. Shumway, David S. Stoffer

   Pages 385-471

9. Back Matter

   Pages 473-562

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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, nonlinear models, resampling techniques, GARCH models, ARMAX models, stochastic volatility, wavelets, and Markov chain Monte Carlo integration methods.

This edition includes R code for each numerical example in addition to Appendix R, which provides a reference for the data sets and R scripts used in the text in addition to a tutorial on basic R commands and R time series. An additional file is available on the book’s website for download, making all the data sets and scripts easy to load into R.

• ARIMA models
• dynamic linear models
• R package
• spectral analysis
• categorical time series analysis
• multivariate spectral methods
• long memory series
• nonlinear models
• resampling techniques
• GARCH models
• state-space analysis
• stochastic volatility
• wavelets integration method
• Markov chain Monte Carlo integration method

“The authors have to be congratulated for their ability to describe in a book of less than 600 pages such a variety of topics and methods, together with scripts allowing the reproduction of the results, for so many real examples. It is a valuable contribution with a strong statistical orientation and a carefully designed pleasant typography.” (Anna Bartkowiak, ISCB News, iscb.info, Issue 65, June, 2018)

“The chapters are nicely structured, well presented and motivated. … it provides sufficient exercise questions making it easier for adoption as a graduate textbook. The book will be equally attractive to graduate students, practitioners, and researchers in the respective fields. … The book contributes stimulating and substantial knowledge for time series analysis for the benefit of a host of community and exhibits the use and practicality of the fabulous subject statistics.” (S. Ejaz Ahmed, Technometrics, Vol. 59 (4), November, 2017)

  

• Department of Statistics, University of California, Davis, Davis, USA

  Robert H. Shumway

• Department of Statistics, University of Pittsburgh, Pittsburgh, USA

  David S. Stoffer

Robert H. Shumway, PhD, is Professor Emeritus of Statistics at the University of California, Davis. He is a Fellow of the American Statistical Association and a member of the International Statistical Institute. He won the 1986 American Statistical Association Award for Outstanding Statistical Application and the 1992 Communicable Diseases Center Statistics Award; both awards were for joint papers on time series applications. He is also the author of a Prentice-Hall text on applied time series analysis and served as a Departmental Editor for the Journal of Forecasting and Associate Editor for the Journal of the American Statistical Association.

David S. Stoffer, PhD, is Professor of Statistics at the University of Pittsburgh. He is a Fellow of the American Statistical Association and has made seminal contributions to the analysis of categorical time series. David won the 1989 American Statistical Association Award for Outstanding Statistical Application in a joint paper analyzing categorical time series arising in infant sleep-state cycling. He is currently a Departmental Editor of the Journal of Forecasting and an Associate Editor of the Annals of Statistical Mathematics. He has served as Program Director in the Division of Mathematical Sciences at the National Science Foundation and as Associate Editor for the Journal of the American Statistical Association.