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Introductory Time Series with R (Use R!) 2009th Edition

4.1 out of 5 stars 29 customer reviews
ISBN-13: 978-0387886978
ISBN-10: 0387886974
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Editorial Reviews

Review

From the reviews:

“The book…gives a very broad and practical overview of the most common models for time series analysis in the time domain and in the frequency domain, with emphasis on how to implement them with base R and existing R packages such as Rnlme, MASS, tseries, fracdiff, mvtnorm, vars, and sspir. The authors explain the models by first giving a basic theoretical introduction followed by simulation of data from a particular model and fitting the latter to the simulated data to recover the parameters. After that, they fit the class of models to either environmental, finance, economics, or physics data. There are many applications to climate change and oceanography. The R programs for the simulations are given even if there are R functions that would do the simulation. All examples given can be reproduced by the reader using the code provided…in all chapters. Exercises at the end of each chapter are interesting, involving simulation, estimation, description, graphical analysis, and some theory. Data sets used throughout the book are available in a web site or come with base R or the R packages used. The book is a great guide to those wishing to get a basic introduction to modern time series modeling in practice, and in a short amount of time. …” (Journal of Statistical Software, January 2010, Vol. 32, Book Review 4)

“Later year undergraduates, beginning graduate students, and researchers and graduate students in any discipline needing to explore and analyse time series data. This very readable text covers a wide range of time series topics, always however within a theoretical framework that makes normality assumptions. The range of models that are discussed is unusually wide for an introductory text. … The mathematical theory is remarkably complete … . This text is recommended for its wide-ranging and insightful coverage of time series theory and practice.” (John H. Maindonald, International Statistical Review, Vol. 78 (3), 2010)

“The authors present a textbook for students and applied researchers for time series analysis and linear regression analysis using R as the programming and command language. … The book is written for students with knowledge of a first-year university statistics course in New-Zealand and Australia, but it also might serve as a useful tools for applied researchers interested in empirical procedures and applications which are not menu driven as it is the case for most econometric software packages nowadays.” (Herbert S. Buscher, Zentralblatt MATH, Vol. 1179, 2010)

From the Back Cover

Yearly global mean temperature and ocean levels, daily share prices, and the signals transmitted back to Earth by the Voyager space craft are all examples of sequential observations over time known as time series. This book gives you a step-by-step introduction to analysing time series using the open source software R. Each time series model is motivated with practical applications, and is defined in mathematical notation. Once the model has been introduced it is used to generate synthetic data, using R code, and these generated data are then used to estimate its parameters. This sequence enhances understanding of both the time series model and the R function used to fit the model to data. Finally, the model is used to analyse observed data taken from a practical application. By using R, the whole procedure can be reproduced by the reader. All the data sets used in the book are available on the website http://staff.elena.aut.ac.nz/Paul-Cowpertwait/ts/.

The book is written for undergraduate students of mathematics, economics, business and finance, geography, engineering and related disciplines, and postgraduate students who may need to analyse time series as part of their taught programme or their research.

Paul Cowpertwait is an associate professor in mathematical sciences (analytics) at Auckland University of Technology with a substantial research record in both the theory and applications of time series and stochastic models. Andrew Metcalfe is an associate professor in the School of Mathematical Sciences at the University of Adelaide, and an author of six statistics text books and numerous research papers. Both authors have extensive experience of teaching time series to students at all levels.

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Product Details

  • Series: Use R!
  • Paperback: 256 pages
  • Publisher: Springer; 2009 edition (June 9, 2009)
  • Language: English
  • ISBN-10: 0387886974
  • ISBN-13: 978-0387886978
  • Product Dimensions: 6 x 0.6 x 9 inches
  • Shipping Weight: 1 pounds (View shipping rates and policies)
  • Average Customer Review: 4.1 out of 5 stars  See all reviews (29 customer reviews)
  • Amazon Best Sellers Rank: #173,268 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

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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.
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Format: Paperback Verified Purchase
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!
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Format: Paperback
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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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.
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Differently from many other books in the "Use R!" series, this one is very didatic and comprehensive. It covers all important functions and applications in time series analysis, ad it's good for both the graduate and undergraduate students or the casual researcher.
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Full marks on coverage and "technical vs. accessible" trade-off; a concise, rigorous and user-friendly introduction to time series analysis in R, helpful for both statisics and R beginners, and an appealing complementary textbook in a graduate course.
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I loved reading this book. For one of my research paper on sales projections for Indian companies, I needed a book on time series analysis using R and this book served my purpose pretty well. The only problem with the book is that - the website of the book has changed. However, from the new website of the author, one can download all the data files and learn the subject.
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This is anything except introductory in my point of view. There are lots of mathematical explanations and even proofing of theories about time series. Overall I liked the book because it offer you as much insight as you like to grasp, but I think the tile is a little bit misguiding.
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