Free Kindle Introductory Time Series With R (Use R!) ebooks Download

                
              
Free Kindle 
Introductory Time Series With R (Use R!) ebooks Download
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.
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:  3.9 out of 5 stars  See all reviews (31 customer reviews)
Best Sellers Rank: #203,881 in Books (See Top 100 in Books)   #92 in Books > Business & Money > Economics > Econometrics   #122 in Books > Computers & Technology > Software > Mathematical & Statistical   #165 in Books > Business & Money > Marketing & Sales > Marketing > Research
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!
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.
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!
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.
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.
I used this in a graduate level course and found the structure of the subjects useful and the addition of R syntax marginally synthetic, particularly in appendices. However, many of the topics covered in chapter exercises include tasks in following chapters; this is NOT useful! Additionally, some of the syntax used in Exhibits are inconclusive and only marginally helpful e.g. incomplete script [clearly a editorial oversight]. When prescribing to this text be prepared to use Cran's R directories references for assistance; R proficiency dependent. This is a sound text likely as supplemental material but not exclusively.
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.
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