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Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics) (Englisch) Gebundene Ausgabe – 4. Juli 2006


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Produktbeschreibungen

Pressestimmen

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)

Synopsis

"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.


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Amazon.com: 29 Rezensionen
49 von 51 Kunden fanden die folgende Rezension hilfreich
The best of a bad bunch 24. März 2008
Von Genevieve Hayes - Veröffentlicht auf Amazon.com
Format: Gebundene Ausgabe
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.
28 von 29 Kunden fanden die folgende Rezension hilfreich
There are much better options 13. Februar 2010
Von Amazon-buyer-for-13yrs - Veröffentlicht auf Amazon.com
Format: Gebundene Ausgabe
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.
29 von 35 Kunden fanden die folgende Rezension hilfreich
R is not that relevant here 16. Februar 2007
Von Hui - Veröffentlicht auf Amazon.com
Format: Gebundene Ausgabe
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
13 von 14 Kunden fanden die folgende Rezension hilfreich
Not Reader-Friendly 27. September 2007
Von Cathy - Veröffentlicht auf Amazon.com
Format: Gebundene Ausgabe
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.
27 von 34 Kunden fanden die folgende Rezension hilfreich
A review from someone who has actually read the book 19. Februar 2007
Von Nicholas White - Veröffentlicht auf Amazon.com
Format: Gebundene Ausgabe
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. The tutorial is great for a beginner.

At the end of the day, the text is not an R manual. It never says it is and I do not understand why anyone would think it should be an R manual. It is also not a manual for making wine and it will not help you train your dog. It is, however, an accessible modern introduction to time series analysis with many interesting examples that have associated R code. And, while you are learning time series analysis, you will also learn how to use R for analyzing time series.
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