Customer Reviews

Introductory Time Series with R (Use R!)

5 star:		(6)
4 star:		(3)
3 star:		(1)
2 star:		(0)
1 star:		(1)

 

 

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!

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.

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.

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!

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.

This is an introduction only. It's OK but not very substantive, either from the standpoint of R or time series theory. For anyone in grad school, you need more than this.

I am enjoying learning from this book about time series. However, I feel that the coverage is more intermediate than introductory. Have not found, for example, anything about the structure of a time series object or how frequency of observations is set or maintained.

Good introduction to time series analysis, with links to datasets and R code that one can use to work through the examples.

A good book, although not presents a friendly and logical order to the subject. It has a good data set to students work throughout book. A summary of R code is shown at the end of chapters.

Um bom livro para começar estudar séries temporais. Apesar de não ter uma sequência bem definida nos assuntos abordados, encontramos muito conteúdo no livro. Recomendável, principalmente por ser bem fácil obter o R a partir da internet, o que facilita o aprendizado quando não se dispõe de softwares consagrados, mas muito caros.

The book was delivered quickly and in very good condition. I was extremely excited to receive it. Thank you!