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Time Series Analysis and Its Applications, Second Edition, presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Numerous examples using non-trivial data illustrate solutions to problems such as evaluating pain perception experiments using magnetic resonance imaging, monitoring a nuclear test ban treaty, evaluating the volatility of an asset, or finding a gene in a DNA sequence. The book is designed to be useful as a text 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.Material from the first edition of the text has been updated by adding examples and associated code based on the freeware R statistical package.As in the first edition, modern developments involving categorical time series analysis and the spectral envelope, multivariate spectral methods, long memory series, nonlinear models, longitudinal data analysis, resampling techniques, GARCH models, stochastic volatility models, wavelets, and Monte Carlo Markov chain integration methods are incorporated in the text. In this edition, the material has been divided into smaller chapters, and the coverage of financial time series, including GARCH and stochastic volatility models, has been expanded. These topics add to a classical coverage of time series regression, univariate and multivariate ARIMA models, spectral analysis and state-space models.
Characteristics of Time Series * Time Series Regression and ARIMA Models * Dynamic Linear Models and Kalman Filtering * Spectral Analysis and Its Applications
Robert H. Shumway 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 the author of a previous 1988 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 is Professor of Statistics at the University of Pittsburgh. He is a Fellow of the American Statistical Association. He has made seminal contributions to the analysis of categorical time series and 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 a Program Director in the Division of Mathematical Sciences at the National Science Foundation and as an Associate Editor for the Journal of the American Statistical Association
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 abundance of material at different levels of complexity." (Pieter Bastiaan Ober, Journal of Applied Statistics, Vol. 35 (2), 2008)
