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Introductory Time Series with R (Use R!), by Paul S.P. Cowpertwait, Andrew V. Metcalfe
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This book gives the reader a step-by-step introduction to analyzing time series using the open source software R. Each time series model is illustrated through practical applications addressing contemporary issues, and is defined in mathematical notation.
- Sales Rank: #421303 in Books
- Published on: 2009-06-09
- Released on: 2009-06-09
- Original language: English
- Number of items: 1
- Dimensions: 9.00" h x .62" w x 6.00" l, .85 pounds
- Binding: Paperback
- 256 pages
Most helpful customer reviews
26 of 26 people found the following review helpful.
A great book on R!
By Pd Farleigh
This is a cracking book on applying R to time series analysis. The best parts of the book are all of the worked examples, the accompanying data sets and several different ways to calculate seasonality.
The book is better than most on time series, because it does not neglect the de-trending process needed to get stationery residuals. If you use just the lm() command in R to do this before, then the real gem in this book is the advice to use the gls() command from the nlme library instead (to get the confidence intervals right).
Overall, a very good book that is applied to R but has enough mathematical backing for the techniques presented. However, this is a book about applying time series analysis in R. If you seek a more algebraic treatment, then this is not the book I'm afraid, but it would be a great supplement!
19 of 19 people found the following review helpful.
Excellent and accessible introduction, suitable for all R users
By Sitting in Seattle
This is an excellent introduction to time series analysis in R, and is suitable for all readers who use R. In contrast to most statistics books, it does not presume an extensive mathematical background. Rather, it is a very much a progressive, didactic text, suitable for leisurely self-learning. The mathematics are presented briefly and appropriately for each topic, but progress and understanding do not depend on absorbing them in depth. It would be suitable, for instance, to social scientists, ecologists, public policy researchers, and so forth who use R.
It is very much a multi-lesson tutorial on the basics of time series analysis, and should be worked through at the computer using R. The topics include decomposition (e.g., extracting seasonality vs. trends), handling autocorrelation, forecasting (e.g., the Bass model in marketing forecasts), regression models, and some more advanced topics such as spectral analysis. In some of the later topics, math is unavoidable and is presented when needed.
There are two limitations to the book. First, as should be obvious from the preceding, some mathematicians and statisticians may be disappointed by the focus on tutorial rather than formal explanation. It has math but that's not the focus, so it would not be suitable for, say, a graduate-level mathematical stats course. Second, it of course cannot cover all aspects of time series analysis. It has examples from many domains (finance, operations, marketing, etc.) but limited depth in any single area; and it presents a variety of core models but does not cover the many advanced topics.
Overall this is an excellent introduction to time series. If you're a general R analyst who wants to get started with time series, it's the best place to begin that I've seen.
14 of 14 people found the following review helpful.
A very positive review from a non-statistician
By popivan
Bought this book about a year ago, with the goal to learn more about time series analysis with R and applications to financial time series. It has been a while since I was more satisfied by a book. It's relatively easy to follow (I am not a statistician), full of examples in R and provides just enough (IMO) details for the math savvy readers to get them started in the theory behind. I keep coming back to this little book again and again!
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