This is the first book that integrates useful parametric and nonparametric techniques with time series modeling and prediction, the two important goals of time series analysis. A distinct feature of this book is that it applies many modern nonparametric estimation and testing ideas to time series modeling and model identification, while outlines many useful ideas from more traditional time series analysis. This will enable readers to use modern data-analytic techniques while keeping in touch with traditional approaches, and make the book self-contained. Such a book will benefit researchers and practitioners in various fields such as econometricians, meteorologists, biologists, among others who wish to learn useful time series methods within a short period of time. The book also intends to serve as a reference or text book for graduate students in statistics and econometrics.
From the reviews:
"...the authors should be congratulated for writing a coherent monograph on modern time series analysis with a focus on nonparametric approaches. I believe that this book will become a standard reference in this area and remain so for a long time. Graduate students in statistics, economics, and financial engineering should be happy to have a much-needed textbook on modern time series methods, which covers not only ARIMA models, but also the newer and more flexible nonlinear and nonparametric techniques."
Technometrics, February 2004
"This is a book that one can read as a beginner or as an expert. Although there are plenty of theorems, there are also plenty of numerical examples, with both real and simulated data, and lots of pictures and graphics (SPLUS-style). The topics are very fully explained and discussed, and there are many pointers to the literature for further study (with about six hundred references listed)."
ISI Short Book Reviews, Vol. 24/1, Apr. 2004
"Fan and Yao's book has a lot to offer. First, it is readable, even by those with limited knowledge of time-series analysis, as the authors spend time on all the basic concepts. Second, it is self-contained so you do not need other books to understand it. Third, it contains many examples and illustrations to explain the intuition behind the concepts. Fourth, it is up to date and has the latest cutting-edge methods to handle nonlinear time series." Quantitative Finance, 2004
"...The book has much that is interesting and useful...it could serve as a the focus of a graduate reading course or as a source of supplemental teaching materials for an advanced time series class." JASA, March 2005
"This book brings together some of the most recent tools of nonparametric methods as they apply to time series analysis. Wherever possible, there are data examples, simulations and visuals to elucidate the matter. Overall the treatment is very comprehensive. Clear indications are given of the latest developments. ... It is an extremely useful book for teachers of time series courses and will also be extremely handy for researchers in time series and nonparametrics ... . this book is destined to be a classic." (Arup Bose, Sankhya: The Indian Journal of Statistics, Vol. 66 (2), 2004)
"This monograph is the first book that integrates useful parametric and nonparametric techniques with time series modeling and forecasting, the two main goals of time series analysis. ... The monograph will be useful for graduate students, application-oriented time series analysts, and new and experienced researchers. It will have value both within the statistical community and across a broad spectrum of other fields such as econometrics, empirical finance, biometrics, and ecology." (Yurij S. Kharin, Mathematical Reviews, 2004a)
"This is both a monograph and a textbook on time series analysis. ... Given the fact that this research area has grown so fast, the authors have done an excellent job in summarizing some of the recent research work. ... Overall, I think that the authors should be congratulated for writing a coherent monograph ... . I believe that this book will become a standard reference in this area and remain so for a long time." (Z.-Q. John Lu, Technometrics, Vol. 46 (1), 2004)
"The book is aimed at a broad readership, the prerequisites being just a grounding in probability ... . This is a book that one can read as a beginner or as an expert. Although there are plenty of theorems, there are also plenty of numerical examples, with both real simulated data, and lots of pictures and graphics (SPLUS-style). The topics are fully explained and discussed, and there are many pointers to the literature for further study (with about six hundred references listed)." (M. J. Crowder, Short Book Reviews, Vol. 24 (1), 2004)
"This is a book on (modern) time series analysis, covering standard linear models, and nonlinear models, with emphasis on the latter. ... the authors present a useful collection of nonlinear time series models, many of which are treated in the contemporary literature. The interested reader will find a wealth of procedures and suggestions for implementation." (R. Mentz, Zentralblatt MATH, Vol. 1014, 2003)
"This book is perhaps best described as propagating the integration of nonparametric and parametric approaches to analyzing time series data. ... Evidently, the coverage is impressive, as is further underlined by a 50 page bibliography comprising some 700 items. The authors succeeded in maintaining a good balance between methodology and numerical illustrations. Given the technicalities of the field, the book is also much more readable ... . All in all, this book is ... suitable as a reference ... ." (Christian Kleiber, Statistical Papers, Vol. 46 (3), 2005)
