» » Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics)

Download Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics) epub

by Robert H. Shumway,David S. Stoffer

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.

Download Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics) epub
ISBN: 0387293175
ISBN13: 978-0387293172
Category: Science
Subcategory: Biological Sciences
Author: Robert H. Shumway,David S. Stoffer
Language: English
Publisher: Springer; 2nd edition (May 25, 2006)
Pages: 575 pages
ePUB size: 1307 kb
FB2 size: 1387 kb
Rating: 4.5
Votes: 322
Other Formats: lit azw lrf mbr

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.
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 are
Harvey (1989) Forecasting, structural time series models and the Kalman filter
Durbin 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.
Time Series Analysis and Its Applications: With R Examples is well-written and packaged good. Nice book with reasonable price, wonderful reference for Stat courses time series analysis. Thank you so much!