Ebook Download Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics)

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Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics)

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Review

“The authors have to be congratulated for their ability to describe in a book of less than 600 pages such a variety of topics and methods, together with scripts allowing the reproduction of the results, for so many real examples. It is a valuable contribution with a strong statistical orientation and a carefully designed pleasant typography.” (Anna Bartkowiak, ISCB News, iscb.info, Issue 65, June, 2018)“The chapters are nicely structured, well presented and motivated. … it provides sufficient exercise questions making it easier for adoption as a graduate textbook. The book will be equally attractive to graduate students, practitioners, and researchers in the respective fields. … The book contributes stimulating and substantial knowledge for time series analysis for the benefit of a host of community and exhibits the use and practicality of the fabulous subject statistics.” (S. Ejaz Ahmed, Technometrics, Vol. 59 (4), November, 2017)

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Product details

Series: Springer Texts in Statistics

Paperback: 562 pages

Publisher: Springer; 4th ed. 2017 edition (April 11, 2017)

Language: English

ISBN-10: 3319524518

ISBN-13: 978-3319524511

Product Dimensions:

7 x 1.2 x 10 inches

Shipping Weight: 2.4 pounds (View shipping rates and policies)

Average Customer Review:

3.5 out of 5 stars

37 customer reviews

Amazon Best Sellers Rank:

#165,893 in Books (See Top 100 in Books)

A very good advanced introduction to this massive topic. Probably not right for you if you are new to this subject. In that case, Wei's book would be a better place to begin.

Fist off, what this book is not: It is not a Time Series Theory book like Tsay or Brockwell. If all you want is mathematical rigor, go somewhere else.Now, as to what the book is: it is an very easy to read intermediate text with examples drawn from the real world. It is also reasonably complete in building programming examples in R (with exception of Chapter 7, lamentably ... Chapter 6 code is available on the book's website).One other reviewer commented that some of the examples consist of only one line of R code. This is part of the power of R and CRAN that such powerful statistical techniques like ARIMA and Factor Modeling can be represented in a single function call, and not a shortcoming of the book.This book will not replace Tsay or Zivot and Wang on my shelf, but is an accesible, excellent text that does a very good job of covering its intended purpose, including some relatively advanced topics. Publishing code for Chapter 7 would rate this book its fifth star.

Awesome book, will keep for referring !

Even though I am new to Time Series Analysis and not very good at programming in R, I could fallow this book and actually utilized the example codes. Examples for each subjects were chosen very nicely. I have been working on a project and come across a very nice paper written on the subject of one particular form of State Space model. While I was trying to regenerate authors results with their Data, I had difficulty getting the right results. I found out that there was a big mistakes in the way they presented their data. To my surprise, Shumway and Stoffer analyzed the same data as one of the examples for state-Space model without the mistake of the original paper. I realized how relevant their examples to real life problems I am so interested in. As self study guide, this is a very good practice and reference book. It is intermediate level book for TSA. I think I will get more use out of this book than any other Math-statistic books I have ever used. I like to thank to the Authors.

I work in forecasting in the environmental sciences and I work almost exclusively with state space models. This book has been especially useful for understanding and applying state-space modeling to time series data. I have found other books on state-space modeling much more difficult to follow relative to this book. The code on the website (2006 edition) is very helpful also. I recommend that my graduate students to do self-study with this book. Admittedly they find it hard, and it is those with a strong math/stats background that gain the most from it. This is not an introductory text, even through is is mostly text and lighter on equations relative to, say, a pure math book. But this is a GREAT book for someone with a solid math/stats background and some basic time series analysis under their belt.I've noticed a number of negative reviews pertaining to the section on frequency domain analysis. I haven't actually done more than skim those sections as I never do frequency domain analyses only time domain analyses.Other books I use a lot for state-space modeling reference areHarvey (1989) Forecasting, structural time series models and the Kalman filterDurbin and Koopman (2002) Time Series Analysis by State Space Methods

I like this book, because its simplicity. I personally needed something that dealt with more of DLM's, but needed background on the general time series analysis. Its R examples were very helpful in showing the certain functions that are already implemented in R and how to construct your own time series.

The examples are interesting and informative, but it's been a few years since I took a statistics course and I had forgotten some of the basic manipulations necessary to work through the homeworks. It's still early in the course, but I think that the book and R examples will be more than adequate as an assist to lecture.

I like this book especially because it has good examples of R code that can be used. However in general, I think this book is very theoretical for a beginner who just wants to learn about time series. Reading this book requires prior knowledge about time series.

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