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.
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.
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.