Time Series Analysis and Its Applications (Springer Texts in Statistics) [Hardcover]

Robert H. Shumway (Author), David S. Stoffer (Author)

Book Description

ISBN-10: 0387989501 | ISBN-13: 978-0387989501 | Publication Date: March 1, 2005

Time Series Analysis and Its Applications 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 or monitoring a nuclear test ban treaty. 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. Theory and methodology are separated to allow presentations on different levels. Material from the earlier 1988 Prentice-Hall text Applied Statistical Time Series Analysis has been updated by adding modern developments involving categorical time sries analysis and the spectral envelope, multivariate spectral methods, long memory series, nonlinear models, longitudinal data analysis, resampling techniques, ARCH models, stochastic volatility, wavelets and Monte Carlo Markov chain integration methods. These add to a classical coverage of time series regression, univariate and multivariate ARIMA models, spectral analysis and state-space models. The book is complemented by ofering accessibility, via the World Wide Web, to the data and an exploratory time series analysis program ASTSA for Windows that can be downloaded as Freeware. Robert H. Shumway is Professor of Statistics at the University of California, Davis. He is a Fellow of the American Statistical Association and a member of the Inernational 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 is currenlty a Departmental Editor for the Journal of Forecasting. David S. Stoffer is Professor of Statistics at the University of Pittsburgh. 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 an Associate Editor of the Journal of Forecasting and has served as an Associate Editor for the Journal fo the American Statistical Association.

Editorial Reviews

Review

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)

--This text refers to an alternate Hardcover edition.

From the Back Cover

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.

R.H. Shumway is Professor 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.

D.S. Stoffer is Professor of Statistics at the University of Pittsburgh. 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 for the Journal of Forecasting and Associate Editor of the Annals of the Institute of Statistical Mathematics.

 

--This text refers to an alternate Hardcover edition.

See all Editorial Reviews

Product Details

• Hardcover: 572 pages
• Publisher: Springer (March 1, 2005)
• Language: English
• ISBN-10: 0387989501
• ISBN-13: 978-0387989501
• Product Dimensions: 9.6 x 6.4 x 1.3 inches
• Shipping Weight: 2 pounds
• Average Customer Review: 3.5 out of 5 stars  See all reviews (32 customer reviews)
• Amazon Best Sellers Rank: #1,814,906 in Books (See Top 100 in Books)

More About the Author

Discover books, learn about writers, read author blogs, and more.

Customer Reviews

Most Helpful Customer Reviews

24 of 25 people found the following review helpful:

4.0 out of 5 stars The best of a bad bunch, March 24, 2008

By 

Genevieve Hayes (Australia) - See all my reviews
(REAL NAME)   

This review is from: Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics) (Hardcover)

Although a lot of books have been written on time series analysis, most of them just aren't very good. "Time Series Analysis and its Applications" is one of the better time series text books. It's not a brilliant book, but all of the other time series books that I have seen are worse.

This book covers all of the main areas of time series analysis such as ARIMA, GARCH and ARMAX models and spectral analysis and it does a pretty good job of it. Most of the explanations are clear enough for a beginner (with some statistical background) and are accompanied by worked examples (something which seems to be omitted in a lot of time series texts). Exercises are also provided at the end of each chapter, although no solutions are provided in the book (a colleague of mine informed me that the solutions are provided on the author's website, but that a large portion of these are either wrong or poorly explained).

Prospective purchasers of this book should be aware, however, that there are a number of errors throughout this book (corrections can be found on the author's website) and that although the title suggests that there are "R examples" in this book, these examples are few and far between and are not well explained. If you are looking for a manual for the R time series functions, then this is not the book for you.

I am a university lecturer and set this book as a supplementary text for an undergraduate statistics unit I teach, which includes a time series component. I believe that this is the best book available for this purpose. However, if you are a lecturer who is thinking of setting this book as a text for your class, please be aware of its limitations, and make sure that your students are also aware of them.

31 of 34 people found the following review helpful:

4.0 out of 5 stars modern time series with applications, February 8, 2008

By 

Michael R. Chernick "statman31147" (Holland PA) - See all my reviews
(REAL NAME)   

This review is from: Time Series Analysis and Its Applications (Springer Texts in Statistics) (Hardcover)

This is a modern book on time series analysis with many interesting and useful examples. It has a practical orientation much like Shumway's earlier book. The material has been tested in courses given by the authors at UC Berkeley and UC Davis. Good for both undergraduate and graduate level students. It covers most of the basics from both the time and frequency domain approaches. Although one reviewer suggests that it is light on theory compared to the Brockwell and Davis book, there is an adequate amount of theory presented which makes the level intermediate. It does require some advanced mathematics. Interesting topics not commonly found in competitor books include long memory ARMA models, the multivariate ARMAX models and their state space representation, applications of ARMAX models to longitudinal data analysis, bootstrapping state space models and the use of frequency domain time series methods applied to discriminant analysis, clustering and various other common multivariate statistical techniques. It also has a nice list of references. It definitely deserves 5 stars and possibly an oscar!

70 of 84 people found the following review helpful:

1.0 out of 5 stars Worst time series book you'll ever pick up!, February 27, 2003

By 

K. Hamidieh "azeri" (Ann Arbor, Michigan, USA) - See all my reviews
(REAL NAME)   

This review is from: Time Series Analysis and Its Applications (Springer Texts in Statistics) (Hardcover)

I am writing this review from a perspective of a graduate student taking a statistical time series class for the first time. Below you'll see a lot of good review of the book. But the question you should ask is how many of these reviewers used this book for their first time series classes?

Despite the claims of the authors, this is not a book for the beginners. It requires quite a bit of mathematical maturity and an in-depth knowledge of statistical methods. Here's a summary:

Disadvantages of the book:
1. It is a difficult and frustrating read.
2. Development of difference equations (fundamental tool in analyses of time series) is scattered everywhere and weak at best.
3. The material is not presented in a cohesive manner.
4. The author constantly relegates important theorems to the end of the chapter sections (which supposedly could be skipped on the first reading) and refers to these theorems in subsequent sections.
5. This book contains lots of typos.
6. Important results that must be discussed within the text material are left as exercises.
7. The notation is strange. Example: A random variable is universally represented by a capital letter such as an X. Author uses lower case letters to represent random variables.
8. The coverage of frequency domain is appalling. The author does a ghastly job of introducing Fourier Series and Transform. An entire chapter (chapter 3) is on frequency domain analysis. The question after reading the chapter is: so what???
9. No solutions or hints are provided so this book is practically useless for self-study.

Advantages:
1. It covers some recent developments in time series.
2. Its associated website has some decent data and S code.
3. It has a nice book cover.

There are plenty of other books better, or I should say much superior to this useless book:
1. Time Series Analysis by Hamilton
2. Introduction to Time Series and Forecasting by Brockwell
3. Applied Econometric Time Series by Enders (A bit outdated but very readable)
4. Analysis of Time Series by Chatfield (Lower level but a good book)

Conclusion: There are lots of other alternatives. This is a horrible book. It may be popular but I believe its popularity is due to good marketing and possibly good connections the authors may have.