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 1-88

3. Time Series Regression and ARIMA Models

   • Robert H. Shumway, David S. Stoffer

   Pages 89-212

4. Spectral Analysis and Filtering

   • Robert H. Shumway, David S. Stoffer

   Pages 213-300

5. State-Space and Multivariate ARMAX Models

   • Robert H. Shumway, David S. Stoffer

   Pages 301-411

6. Statistical Methods in the Frequency Domain

   • Robert H. Shumway, David S. Stoffer

   Pages 413-527

7. Back Matter

   Pages 529-550

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The goals of this book are to develop an appreciation for the richness and versatility of modern time series analysis as a tool for analyzing data, and still maintain a commitment to theoretical integrity, as exemplified by the seminal works of Brillinger (1981) and Hannan (1970) and the texts by Brockwell and Davis (1991) and Fuller (1995). The advent of more powerful computing, es­ pecially in the last three years, has provided both real data and new software that can take one considerably beyond the fitting of·simple time domain mod­ els, such as have been elegantly described in the landmark work of Box and Jenkins (1970). The present book is designed to be useful as a text for courses in time series on several different levels and as a reference work for practition­ ers facing the analysis of time-correlated data in the physical, biological, and social sciences. We believe the book will be useful as a text at both the undergraduate and graduate levels. An undergraduate course can be accessible to students with a background in regression analysis and might include Sections 1. 1-1. 8, 2. 1-2. 9, and 3. 1-3. 8. Similar courses have been taught at the University of California (Berkeley and Davis) in the past using the earlier book on applied time series analysis by Shumway (1988). Such a course is taken by undergraduate students in mathematics, economics, and statistics and attracts graduate students from the agricultural, biological, and environmental sciences.

• Resampling
• Time Series Analysis
• data analysis
• permutation tests
• statistics
• time series

From the reviews of the second edition:

"The book gives an introduction to time series analysis. It is designed as a textbook at both the undergraduate and graduate level and as a reference work for practitioners … . This now available second edition of the book differs from the first … by several substantial changes. … the presentation has improved. The consideration of new material makes it more attractive as well. Moreover, the use of the R package … makes the book more interesting … ." (Wolfgang Schmid, Zentrablatt MATH, Vol. 1096 (22), 2006)

"This is the second edition of a text first published in 2000 … . The text is intended as a course text for a time series analysis class at the graduate level. … I believe that every time series teacher and researcher should own this text." (Robert Lund, Journal of the American Statistical Association, Vol. 102 (479), 2007)

"This is the second edition of a text first published in 2000 … . The book is intended as a course text for a graduate-level time series analysis class. It presents a very readable introduction to time series, and uses numerous examples based on nontrivial data to illustrate the methods. … Altogether, the book offers a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Compared to other established texts, it presents a more modern slice of the discipline." (Rainer Schlittgen, Advances in Statistical Analysis, Vol. 92, 2008)

"A textbook aimed at graduate-level students, while … the book could also serve as an undergraduate introductory course in time series analysis. … The clear division between time and frequency domain methods produces a well balanced and comprehensive treatment of modern time series analysis … . The book certainly fulfils its claim to be suitable as a textbook for courses at both the undergraduate and graduate levels, as tutors can pick and choose from an abundanceof material at different levels of complexity." (Pieter Bastiaan Ober, Journal of Applied Statistics, Vol. 35 (2), 2008)

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

  Robert H. Shumway

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

  David S. Stoffer

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