Time Series Analysis and Its Applications: With R Examples

by Robert H. Shumway, David S. Stoffer

The fourth edition of this popular graduate textbook, like its predecessors, 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 as a textbook 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, ARMAX models, stochastic volatility, wavelets, and Markov chain Monte Carlo integration methods. This edition includes R code for each numerical example in addition to Appendix R, which provides a reference for the data sets and R scripts used in the text in addition to a tutorial on basic R commands and R time series. An additional file is available on the book’s website for download, making all the data sets and scripts easy to load into R.

Genres: Mathematics, Economics, Nonfiction, Technical, Textbooks, Finance, Science

575 pages, Paperback

First published February 1, 2000

Reviews

[Robert · Rating 3 out of 5]

I read the first ~100p of this book. I stopped because the subject matter had diverged too far from my area of immediate interest (which was covered in the first chapter) rather than because the book is bad. In fact I think it is a good introduction to the topic for those with an interest and a background covering "normal" statistics to a level most STEM undergrads would have. Perhaps one thing that became obvious to me by inference should have been made explicit at the outset, which is that the fundamental general approach is as follows:

1. Get time series and plot it.
2. Guess any trends and/or periodicities in the data (various methods)
3. Subtract them (various methods)
4. Examine what's left ("residuals") to see if it behaves like noise (i.e. has some known type of random distribution) (various methods)
5. If it does, YAY! You have a usable model of the time series
6. If it does not, either make further guesses about trends/periodicities in the residuals and repeat from step 2 OR
7. Go back to the original time series and start from step 2 with different guesses about the nature of trends/periodicities

A flow chart of this at the beginning of the book would make what the book is actually about crystal clear.

As mentioned in a status update, the book does not assume the reader is scientifically motivated and does not discuss the meaning or validity of any trends, correlations or periodicities discovered. There are applications where this is entirely legitimate, probably the biggest and most utilised being analysis of financial/economic data for purposes of investment or trading: One only needs a model that works and not an explanation of why it works in order to make practical decisions. I would advise budding scientists to approach with caution, however; this form of analysis can only generate empirical models and hypotheses about why they are true are a separate but essential part of the scientific process. So, for example, if one discovers a model of the form, seasonal oscillation + white noise, describing your time series, one can make predictions about the future but there is no explanation of why the seasonal variation occurs. You are only part way there, scientifically.

[Terran M · Rating 2 out of 5]

This book gets generally good reviews but I'm sorry to say I just didn't like it. The authors' ideas of what's simple and what's complicated just don't jibe with mine - I often found that they were using a very complicated derivation to "explain" a simple and intuitive result. I found the density of equations a rather hard slog with no commensurate reward, as other books covered the same material in a way that I found much easier and quicker going.

I would Cowpertwait and Metcalfe instead, or Tsay.

[Ferhat Culfaz · Rating 1 out of 5]

Too mathematical and abstract with not so good examples. Definition and symbols in equation also not clear nor explained just because it was defined in chapter 1. Very theoretical despite R examples with datasets. Theory not explained well at all.

[Mike · Rating 4 out of 5]

Pretty good textbook for introducing oneself to the subject. The chapters are very readable with clear examples and good, tough exercises.

[Mitchell · Rating 4 out of 5]

Good practical overview with lots of examples. Main focus is ARIMA models but frequency domain modeling is also covered.

[Joe Cole · Rating 5 out of 5]

This textbook is 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.