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Introductory Time Series with R (Use R!)
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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.
Product details
Series: Use R!
Paperback: 272 pages
Publisher: Springer; 2009 edition (June 9, 2009)
Language: English
ISBN-10: 0387886974
ISBN-13: 978-0387886978
Product Dimensions:
6.1 x 0.6 x 9.2 inches
Shipping Weight: 1 pounds (View shipping rates and policies)
Average Customer Review:
3.6 out of 5 stars
35 customer reviews
Amazon Best Sellers Rank:
#441,952 in Books (See Top 100 in Books)
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.
I love statistics books and have consumed a great number of books that vary in depth, approach, topic, and intended audience. I was looking forward to reading some useful texts on time series analysis, and along with another professor, assigned this book to read over a couple weeks in a class for graduate students. Although there were some great sections and potentially useful resources, the book fell short on depth and usability, and I do not think I'd recommend it to graduate students or others who are looking to gain insight and proficiency in approaches to time series analyses.First, some good points:1. If you have no prior experience with R, the book gives you some useful tidbits and you can use the code to jump start various analyses.2. Most of the book consists of sections that are stand-alone resources, so if there is a topic of particular interest, such as spectral analysis, you can go to that section and get useful advice.3. The problems at the end of each section are very helpful, and an answer file is available via a simple internet search.4. Most of the code works, but the link to the data is old - a simple internet search yields the current link.5. Chapter 3 (Holt Winters) was interesting and useful.6. The breadth of the book is quite good, but for each topic, there is absolutely no depth, which yields numerous additional problems….Negatives:1. Detailed descriptions of important concepts are lacking, and explanations are shallow. For example, the explanation of stationary processes (and similar fundamental issues) is woefully inadequate. Also, entire chapters, such as spectral analysis chapter, are incredibly shallow and do not provide sufficient information for analyses - the introduction for that chapter is only 2 pages and leaves out substantive background material.2. The equations throughout the book need more development and explanation and should be more closely tied to the R code. One route towards this goal would be to show more "under the hood" programming, rather than blanket functions or packages that do not provide insight into how analyses are completed.3. The overall organization needs more thought, for example, cross-referencing between chapters, providing an overview of concepts in a broad introductory chapter, and including a synthesis to put everything all together.4. The graduate group interested in this topic was frustrated by a lack of clarity - even a tool as simple as a glossary could have cut down on the excessive amount of consulting other resources, or internet searches of terms and concepts for more detail.5. The analyses of simulated or actual data were a particular strength of the book, but these were used less frequently as the book progressed, and throughout the book, there was very little discussion of what the results mean.6. It is unfortunate, given the state of big data, that researchers (such as ecologists) who require rigorous time series analyses have hesitated to utilize these approaches; this book could have been part of a turning point for more scientists to enter a time series boon, but it is not.
The website for the sample data in the book has changed. Search under Paul Cowpertwait and you will find the new location. I was thinking I had to return the book because I could not find the sample data, but luckily I found it before I returned it. The power of the book is in the examples, so be aware of the change and it will spare you a little frustration.
Book is comprehensive but accessible to the non-statistician. Plenty of workable examples with simple code. Though not an R coding book, authors use pretty good coding practice and give some practical ideas for implementation. These guys clear up areas where I've previously struggled to get at the root of a method. In short, if you want to write TS proofs and academic papers, get another book. If you want to begin including more sophisticated TS models with your other work, this is the book for you.Only one knock, have been through several of the examples and there are some coding typos; nothing major but stay on your toes. Also, some of the algorithms may have changed since the book was written so you need to make use of the R help files to clear up any discrepancies with the author's work...some probably won't get cleared up because a more complex algorithm has been changed.This is a book I've been looking for.
I like this book, but some of the exercises and code in the book is problematic such as exercise 4.6 and exercise 5.2. I had to fix the harmonic series code to get it to work, and there was still some code that still did not work. At times, it was difficult to follow. There were some enlightening exercises that helped me understand the content better.
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