Rating: 
Summary: Horrible book. Buyers should think again before buying.
Review: 1. Authors need better technical writing skills, which is shameful as they came from good universities. Grammatical and structural errors that make discussion difficult to understand. Excessive use of 'bold words' especially on unimportant words hence it's difficult to identify key concepts.
2. Assume readers to have strong background in the field prior to reading. Horrible text for beginners and grad. students who are new to the topic. Beginners should avoid this book like the plague!
3. Unorganized discussion in the book. The authors love to skip all over the book. I bet Confucius will be even more confused after reading through the book.
4. Lack of explanation as to how each equation is derived or concluded. It makes readers wonder where the equations came from and whether the authors simply copied it from somewhere without checking the validity of each equation.
5. References are organized in a terrible way which cause readers to have problem doing further reading.
Rating: 
Summary: An Oscar Winning Book on Time Series Analysis
Review: 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.
Rating:
Summary: The book for practitioners
Review: 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.
Rating:
Summary: The book for practitioners
Review: 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.
Rating:
Summary: Worst time series book you'll ever pick up!
Review: I am writing this review from a perspective of a graduate student taking a statistical time series class for the first time. Below you'll see a lot of good review of the book. But the question you should ask is how many of these reviewers used this book for their first time series classes?Despite the claims of the authors, this is not a book for the beginners. It requires quite a bit of mathematical maturity and an in-depth knowledge of statistical methods. Here's a summary: Disadvantages of the book:
1. It is a difficult and frustrating read.
2. Development of difference equations (fundamental tool in analyses of time series) is scattered everywhere and weak at best.
3. The material is not presented in a cohesive manner.
4. The author constantly relegates important theorems to the end of the chapter sections (which supposedly could be skipped on the first reading) and refers to these theorems in subsequent sections.
5. This book contains lots of typos.
6. Important results that must be discussed within the text material are left as exercises.
7. The notation is strange. Example: A random variable is universally represented by a capital letter such as an X. Author uses lower case letters to represent random variables.
8. The coverage of frequency domain is appalling. The author does a ghastly job of introducing Fourier Series and Transform. An entire chapter (chapter 3) is on frequency domain analysis. The question after reading the chapter is: so what???
9. No solutions or hints are provided so this book is practically useless for self-study. Advantages:
1. It covers some recent developments in time series.
2. Its associated website has some decent data and S code.
3. It has a nice book cover. There are plenty of other books better, or I should say much superior to this useless book:
1. Time Series Analysis by Hamilton
2. Introduction to Time Series and Forecasting by Brockwell
3. Applied Econometric Time Series by Enders (A bit outdated but very readable)
4. Analysis of Time Series by Chatfield (Lower level but a good book) Conclusion: There are lots of other alternatives. This is a horrible book. It may be popular but I believe its popularity is due to good marketing and possibly good connections the authors may have.
Rating:
Summary: Worst time series book you'll ever pick up!
Review: I am writing this review from a perspective of a graduate student taking a statistical time series class for the first time. Below you'll see a lot of good review of the book. But the question you should ask is how many of these reviewers used this book for their first time series classes? Despite the claims of the authors, this is not a book for the beginners. It requires quite a bit of mathematical maturity and an in-depth knowledge of statistical methods. Here's a summary: Disadvantages of the book:
1.It is a difficult and frustrating read.
2.Development of difference equations (fundamental tool in analyses of time series) is scattered everywhere and weak at best.
3.The material is not presented in a cohesive manner.
4.The author constantly relegates important theorems to the end of the chapter sections (which supposedly could be skipped on the first reading) and refers to these theorems in subsequent sections.
5.This book contains lots of typos.
6.Important results that must be discussed within the text material are left as exercises.
7.The notation is strange. Example: A random variable is universally represented by a capital letter such as an X. Author uses lower case letters to represent random variables.
8.The coverage of frequency domain is appalling. The author does a ghastly job of introducing Fourier Series and Transform. An entire chapter (chapter 3) is on frequency domain analysis. The question after reading the chapter is: so what???
9.No solutions or hints are provided so this book is practically useless for self-study. Advantages:
1.It covers some recent developments in time series.
2.Its associated website has some decent data and S code.
3.It has a nice book cover. There are plenty of other books better, or I should say much superior to this useless book:
1.Time Series Analysis by Hamilton
2.Introduction to Time Series and Forecasting by Brockwell
3.Applied Econometric Time Series by Enders (A bit outdated but very readable)
4.Analysis of Time Series by Chatfield (Lower level but a good book) Conclusion: There are lots of other alternatives. This is a horrible book. It may be popular but I believe its popularity is due to good marketing and possibly good connections the authors may have.
Rating: 
Summary: Very good book
Review: I don't believe that the book deserves an oscar, but it is very good. I learned time series from Brockwell & Davis, and this book is less rigorous, but easier to understand and well motivated. The touches on the more modern stuff (long memory, bootstrap, etc) are slight, and a reader seeking info on these topics will need to turn to other sources, but an in depth treatment of long memory in an introductory book can not be expected. I have not found any typos yet, and it reads very well. Strongly recommended for anybody who wants to learn mathematical time series analysis ( I say mathematical, because it still has a decent amount of rigour, theorems, proofs, definitions, etc.), but does not intend to become a PhD in the field (in which case Brockwell & Davis might be a better choice).
Rating:
Summary: A great book for students
Review: 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!
Rating:
Summary: Not good for new Grads
Review: I would say this is the worst textbook for any student who want get an idea of time series even you were an Statistics expert. This book is very bad orgnaized. The ideas jump away here and there to just make you comfusing. And they keep changing notions without outstanding notice. I would recommend the Time Series by Hamilton instead of this one for you if you are an starter.
Rating:
Summary: Excellent
Review: This book is something... I've read it twice and still return to some chapter from time to time. It really requires patience and a strong mathematical background to get through some chapter but it gives you a knowledge and confidence in the modern time series statistics. The text is quite dense and concentrated but I like it.
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