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Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics) Hardcover – November 25, 2010

ISBN-13: 978-1441978646 ISBN-10: 144197864X Edition: 3rd ed. 2011

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Product Details

  • Series: Springer Texts in Statistics
  • Hardcover: 596 pages
  • Publisher: Springer; 3rd ed. 2011 edition (November 25, 2010)
  • Language: English
  • ISBN-10: 144197864X
  • ISBN-13: 978-1441978646
  • Product Dimensions: 6.1 x 1.3 x 9.2 inches
  • Shipping Weight: 2.2 pounds (View shipping rates and policies)
  • Average Customer Review: 3.8 out of 5 stars  See all reviews (29 customer reviews)
  • Amazon Best Sellers Rank: #74,116 in Books (See Top 100 in Books)

Editorial Reviews

Review

From the reviews of the third edition:

“This is the third edition of a textbook first published in 2000. The text is intended as a course text for a time series analysis class at the graduate level. … the appendix includes everything that is necessary to understand the mathematics of time series analysis. As such, there is no way to describe the whole philosophy of the last half century to time series models better than this book.” (Wolfgang Polasek, International Statistical Review, Vol. 81 (2), 2014)

“The book is organised in 7 chapters and 4 appendices. … the book is a valuable resource for students at undergraduate and graduate levels and researchers. The R code for almost all the numerical examples, and the appendices with tutorials containing basic R and R time series commands, are helpful for a better understanding of the theoretical concepts by bringing the theory into a more practical context.” (Irina Ioana Mohorianu, zbMATH, Vol. 1276, 2014)

From the Back Cover

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 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 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. 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, stochastic volatility, wavelets and Markov chain Monte Carlo integration methods.  The third edition includes a new section on testing for unit roots and the material on state-space modeling, ARMAX models, and regression with autocorrelated errors has been expanded.

Also new to this edition is the enhanced use of the freeware statistical package R.  In particular, R code is now included in the text for nearly all of the numerical examples.  Data sets and additional R scripts are now provided in one file that may be downloaded via the World Wide Web.  This R supplement is a small compressed file that can be loaded easily into R making all the data sets and scripts available to the user with one simple command.  The website for the text includes the code used in each example so that the reader may simply copy-and-paste code directly into R.  Appendix R, which is new to this edition, provides a reference for the data sets and our R scripts that are used throughout the text. In addition, Appendix R includes a tutorial on basic R commands as well as an R time series tutorial.  

Customer Reviews

I like this book especially because it has good examples of R code that can be used.
Esar
Now, as to what the book is: it is an very easy to read intermediate text with examples drawn from the real world.
B. Peterson
Although a lot of books have been written on time series analysis, most of them just aren't very good.
Genevieve Hayes

Most Helpful Customer Reviews

49 of 51 people found the following review helpful By Genevieve Hayes on March 24, 2008
Format: 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.
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27 of 28 people found the following review helpful By Amazon-buyer-for-13yrs on February 13, 2010
Format: Hardcover
The book is OK but it falls behind other available texts at comparable or lower prices. I agree with others that the book is not the best introduction and neither a must-have rigorous reference. The main contribution is that it does account for some topics not typically found in most time series textbooks as mentioned in Dr. Chernick's review. The new edition of the classic by Box et al and the introductory text by Brockwell and Davis (ITSF) are muchl superior to Shumway and Stoffer in terms of introducing the core subject (ARIMA modelling) though not using R. If one wants R material (which by the way has powerful time series resources) than the book by Cryer and Chan does a much better job. If one wants more theory and technical detail, and also a solid introduction to multivariate methods, then the theoretical book by Brockwell and Davis (TSTM) and Hamilton's text are way better than this book. Applied economists wanting intro material should check Ender's applied text and engineers serious about time series cannot do better than owning Box et al and the (frequency domain) book by Percival and Walden. Statisticians and advanced readers can go to the two theoretical books I mentioned before.
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28 of 33 people found the following review helpful By Hui on February 16, 2007
Format: Hardcover
If you expect this book to partly serve as a R manual for time series models, you'd be disappointed. In the last two hundred pages, namely chapter 6 "state space models" and chapter 7 "statistical models in the frequency domain", there are only mathematical formulas and no single line of R code. In chapter 5, the R code is only a few lines of calling garch function. Many of the R code in previous chapters are limited to "scan" and "plot" and one line of calling simple functions like arima. If you already learned time series from other books, you are better off reading CRAN website to find the relevant R function rather than buying this book
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12 of 13 people found the following review helpful By Cathy on September 26, 2007
Format: Hardcover
As mentioned by some other reviewers, this book may be a good book in content, but it is very badly organized. The author references figures or equations from everywhere in the book. You have to go through chapters back and forth. Some important definitions are not clearly defined. They were just written into normal passages.
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10 of 11 people found the following review helpful By Daniel G on January 25, 2010
Format: Hardcover
An absolutely catastrophic mix of ambiguous notation, muddled exposition and doubtful structure. The book frequently skips from one equation to the next with little or no explanation, and seems to routinely generalise from simple cases to more complicated ones without explanation or justification. Needless to say, it also lacks rigour in a most deplorable way.
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25 of 32 people found the following review helpful By Nicholas White on February 19, 2007
Format: Hardcover
As the title implies, the book is a text on time series analysis and its applications. It is a modern treatment of time series analysis with a slant toward applications. The applications are interesting and involve current topics such as global warming. The examples are broad in range, including data from various fields such as biology, economics, engineering, environmental science, and medicine. The book is interesting and accessible, and it provides an excellent introduction various aspects of the analysis of time series. The text covers both the spectral and time domains, including a thorough presentation of state-space models. The basic requirement for being able to understand most of the text is knowing the material that would be covered in introductory courses on regression and mathematical statistics.

The book has many interesting and "real" (as opposed to "toy") examples and, as the subtitle explains, many of the examples have associated R code. This makes for a positive experience because you can replicate the analyses. Accordingly, there is no guessing as to what was done to obtain the results of an example. It is completely wrong to say that "R is not relevant". But you do not have to take my word for it! Just go to the website for the text at StatLib and all the R code in the text is posted there. In addition, as the authors' state in the Preface (which is also available for viewing at the website for the text), R code for the state-space chapter (Chapter 6) is on the website for the text. There you will find code for the Kalman filter and smoothing algorithms, as well as the EM algorithm and some examples for maximum likelihood estimation. The website for the text also has a small tutorial for a quick start on using R to do time series analysis.
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