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This book provides an overview of the current state-of-the-art of nonlinear time series analysis, richly illustrated with examples, pseudocode algorithms and real-world applications. Avoiding a "theorem-proof" format, it shows concrete applications on a variety of empirical time series. The book can be used in graduate courses in nonlinear time series and at the same time also includes interesting material for more advanced readers. Though it is largely self-contained, readers require an understanding of basic linear time series concepts, Markov chains and Monte Carlo simulation methods.
The book covers time-domain and frequency-domain methods for the analysis of both univariate and multivariate (vector) time series. It makes a clear distinction between parametric models on the one hand, and semi- and nonparametric models/methods on the other. This offers the reader the option of concentrating exclusively on one of these nonlinear time series analysis methods.
To make the book as user friendly as possible, major supporting concepts and specialized tables are appended at the end of every chapter. In addition, each chapter concludes with a set of key terms and concepts, as well as a summary of the main findings. Lastly, the book offers numerous theoretical and empirical exercises, with answers provided by the author in an extensive solutions manual.
The book covers time-domain and frequency-domain methods for the analysis of both univariate and multivariate (vector) time series. It makes a clear distinction between parametric models on the one hand, and semi- and nonparametric models/methods on the other. This offers the reader the option of concentrating exclusively on one of these nonlinear time series analysis methods.
To make the book as user friendly as possible, major supporting concepts and specialized tables are appended at the end of every chapter. In addition, each chapter concludes with a set of key terms and concepts, as well as a summary of the main findings. Lastly, the book offers numerous theoretical and empirical exercises, with answers provided by the author in an extensive solutions manual.
"The book describes main statistical procedures used in modern nonlinear time series analysis. ... Each chapter ends with a section containing various exercises, both theoretical and simulation, which makes the book suitable for a graduate course in nonlinear time series. Each chapter also contains a section with useful information about the existing software (mainly in MATLAB and R) related to the topic of the chapter." (Vytautas Kazakevicius, Mathematical Reviews, January, 2018)
"This is an excellent addition to the library of books on time series analysis. The most attractive feature of this book is that it places importance on developing intuition about nonlinear time series rather than the more formal theorem-proof approach. It is abundant with data examples and simulations that enhance understanding of the stochastic properties of the models. In my opinion, the approach taken is the best pedagogical technique to learn about time series models." (Hernando Ombao, Journal of the American Statistical Association JASA, Vol. 113 (522), 2018)
"For the scientific quality of its content I do not exaggerate if I consider this book as a treasure." (Oscar Busto, zbMATH 1376.62001, 2018)
"This is an excellent addition to the library of books on time series analysis. The most attractive feature of this book is that it places importance on developing intuition about nonlinear time series rather than the more formal theorem-proof approach. It is abundant with data examples and simulations that enhance understanding of the stochastic properties of the models. In my opinion, the approach taken is the best pedagogical technique to learn about time series models." (Hernando Ombao, Journal of the American Statistical Association JASA, Vol. 113 (522), 2018)
"For the scientific quality of its content I do not exaggerate if I consider this book as a treasure." (Oscar Busto, zbMATH 1376.62001, 2018)