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Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics)
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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.
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Product details
Series: Springer Texts in Statistics
Hardcover: 596 pages
Publisher: Springer; 3rd ed. 2011 edition (November 24, 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
Average Customer Review:
3.5 out of 5 stars
37 customer reviews
Amazon Best Sellers Rank:
#906,480 in Books (See Top 100 in Books)
A very good advanced introduction to this massive topic. Probably not right for you if you are new to this subject. In that case, Wei's book would be a better place to begin.
Fist off, what this book is not: It is not a Time Series Theory book like Tsay or Brockwell. If all you want is mathematical rigor, go somewhere else.Now, as to what the book is: it is an very easy to read intermediate text with examples drawn from the real world. It is also reasonably complete in building programming examples in R (with exception of Chapter 7, lamentably ... Chapter 6 code is available on the book's website).One other reviewer commented that some of the examples consist of only one line of R code. This is part of the power of R and CRAN that such powerful statistical techniques like ARIMA and Factor Modeling can be represented in a single function call, and not a shortcoming of the book.This book will not replace Tsay or Zivot and Wang on my shelf, but is an accesible, excellent text that does a very good job of covering its intended purpose, including some relatively advanced topics. Publishing code for Chapter 7 would rate this book its fifth star.
Awesome book, will keep for referring !
Even though I am new to Time Series Analysis and not very good at programming in R, I could fallow this book and actually utilized the example codes. Examples for each subjects were chosen very nicely. I have been working on a project and come across a very nice paper written on the subject of one particular form of State Space model. While I was trying to regenerate authors results with their Data, I had difficulty getting the right results. I found out that there was a big mistakes in the way they presented their data. To my surprise, Shumway and Stoffer analyzed the same data as one of the examples for state-Space model without the mistake of the original paper. I realized how relevant their examples to real life problems I am so interested in. As self study guide, this is a very good practice and reference book. It is intermediate level book for TSA. I think I will get more use out of this book than any other Math-statistic books I have ever used. I like to thank to the Authors.
I work in forecasting in the environmental sciences and I work almost exclusively with state space models. This book has been especially useful for understanding and applying state-space modeling to time series data. I have found other books on state-space modeling much more difficult to follow relative to this book. The code on the website (2006 edition) is very helpful also. I recommend that my graduate students to do self-study with this book. Admittedly they find it hard, and it is those with a strong math/stats background that gain the most from it. This is not an introductory text, even through is is mostly text and lighter on equations relative to, say, a pure math book. But this is a GREAT book for someone with a solid math/stats background and some basic time series analysis under their belt.I've noticed a number of negative reviews pertaining to the section on frequency domain analysis. I haven't actually done more than skim those sections as I never do frequency domain analyses only time domain analyses.Other books I use a lot for state-space modeling reference areHarvey (1989) Forecasting, structural time series models and the Kalman filterDurbin and Koopman (2002) Time Series Analysis by State Space Methods
I like this book, because its simplicity. I personally needed something that dealt with more of DLM's, but needed background on the general time series analysis. Its R examples were very helpful in showing the certain functions that are already implemented in R and how to construct your own time series.
The examples are interesting and informative, but it's been a few years since I took a statistics course and I had forgotten some of the basic manipulations necessary to work through the homeworks. It's still early in the course, but I think that the book and R examples will be more than adequate as an assist to lecture.
I like this book especially because it has good examples of R code that can be used. However in general, I think this book is very theoretical for a beginner who just wants to learn about time series. Reading this book requires prior knowledge about time series.
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