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

This book has very good examples and is extremely well written. It is always a pleasure to read parts of this book.

I used this book for my time series class. This content is clear with a lot of examples.

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.

The book was fine, and I rented it medium-used, so it came damaged. However, when I returned it, they charged me a damage fee, even though I returned it with no further damage than what it came with. Would encourage users to take a picture of the book if damaged when they receive it, to prove later that they did not in fact misuse the book.

I had a course from this text last year and I think this is a great book for students. We covered parts of Chapters 1-4 including ARMA models, spectral analysis and state-space models. It seems like most texts on time series explain a concept and then use a trite example to demonstrate the concept. With this text, the emphasis is on the applications. Concepts are presented as part of an analysis of a substantive data set. In addition to fundamental ideas, the authors discuss topics in modern time series analysis such as modern regression, long memory, GARCH, and MCMC. I found the material easy to read and I thought the problems were at an appropriate level.I found most texts on time series to be either theory oriented or watered down and simple. Many texts concentrate on only the time domain or only the spectral domain. This text is somewhere in the middle, giving enough theory about a wide scope of topics to understand concepts at a deep enough level to apply the material with confidence.I wouldn't usually post a review, but I liked this book so much that I felt a duty to rebut some of the nasty things said about the text by other students. For example, the time domain is basically difference equations. One reviewer said that difference equations are spread out throughout the text. Well, since three chapters are on time domain topics I would guess that difference equation ideas would be spread out in the three chapters. Also, the trend in time series texts, maybe starting with Box and Jenkins, is to use lower case letters for random variables. And who cares if you use a lower case letter an upper case letter or a picture of a dog to represent a random variable? If the notation is consistent, that is all that is needed. I do agree that you have to fill in some of the details in problems yourself. But isn't that what education is all about? You don't want everything spoonfed to you- you won't learn anything that way!Finally, this is a wonderful text that covers a wide range of modern topics at an accessible level for most students with a basic knowledge of mathematical statistics. I agree with the reviewer who said this book deserves an oscar!

Extremely well written book for practitioners of time series analysis. The books reads easily and little theoretical background is needed for understanding the concepts in the book, while considerate background may be needed for applying those concepts in the real world. This book should be highly regarded by scientists that do forecasting in the environmental or hydroclimatic field. Detailed examples are used for explanation of the concepts in the book, where the models used include ARIMA; ARMAX; Transfer Function Models; and State Space Models.

This is a modern book on time series analysis with many interesting and useful examples. It has a practical orientation much like Shumway's earlier book. The material has been tested in courses given by the authors at UC Berkeley and UC Davis. Good for both undergraduate and graduate level students. It covers most of the basics from both the time and frequency domain approaches. Although one reviewer suggests that it is light on theory compared to the Brockwell and Davis book, there is an adequate amount of theory presented which makes the level intermediate. It does require some advanced mathematics. Interesting topics not commonly found in competitor books include long memory ARMA models, the multivariate ARMAX models and their state space representation, applications of ARMAX models to longitudinal data analysis, bootstrapping state space models and the use of frequency domain time series methods applied to discriminant analysis, clustering and various other common multivariate statistical techniques. It also has a nice list of references. It definitely deserves 5 stars and possibly an oscar!

Dr Shumway and Dr Stoffer have produced a book upon time series analysis that will become an industry and academic standard. All those mathematical and diagnostic frighteners that have been sidestepped by many other authors have been introduced by the authors and used in such a simplifying way that students of all sciences, not only economics, will richly enjoy reading and putting into use. Garch,Bootstrapping, State-Space, Long-memory, if it is modern then it is covered in detail with plenty of top-notch examples. I give the book 5 stars and an Oscar.

A very in depth book with a lot of mathemtical proofs given but also R code so you can recreate the graphs in the text.Need some understanding of statistics to get the most from this book, could do with more examples of real world problems

Muy bueno, como todos los libros springer en r de estadística

Graphs are in black and white only..No other colurs