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Elements of nonlinear time series an...

Gooijer, Jan G. De.

Elements of nonlinear time series analysis and forecasting

Record Type:	Electronic resources : Monograph/item
Title/Author:	Elements of nonlinear time series analysis and forecastingby Jan G. De Gooijer.
Author:	Gooijer, Jan G. De.
Published:	Cham :Springer International Publishing :2017.
Description:	xxi, 618 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Time-series analysis.
Online resource:	http://dx.doi.org/10.1007/978-3-319-43252-6
ISBN:	9783319432526$q(electronic bk.)

Elements of nonlinear time series analysis and forecasting
Gooijer, Jan G. De.

Elements of nonlinear time series analysis and forecasting[electronic resource] /by Jan G. De Gooijer. - Cham :Springer International Publishing :2017. - xxi, 618 p. :ill., digital ;24 cm. - Springer series in statistics,0172-7397. - Springer series in statistics..

Introduction and Some Basic Concepts -- Classic Nonlinear Models -- Probabilistic Properties -- Frequency-Domain Tests -- Time-Domain Linearity Tests -- Model Estimation, Selection and Checking -- Tests for Serial Independence -- Time-Reversibility -- Semi- and Nonparametric Forecasting -- Forecasting Vector Parametric Models and Methods -- Vector Semi- and Nonparametric Methods.

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.

ISBN: 9783319432526$q(electronic bk.)

Standard No.: 10.1007/978-3-319-43252-6doiSubjects--Topical Terms:

181890
Time-series analysis.

LC Class. No.: QA280

Dewey Class. No.: 519.55

Elements of nonlinear time series analysis and forecasting
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245 1 0 $a Elements of nonlinear time series analysis and forecasting $h [electronic resource] / $c by Jan G. De Gooijer.
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505 0 $a Introduction and Some Basic Concepts -- Classic Nonlinear Models -- Probabilistic Properties -- Frequency-Domain Tests -- Time-Domain Linearity Tests -- Model Estimation, Selection and Checking -- Tests for Serial Independence -- Time-Reversibility -- Semi- and Nonparametric Forecasting -- Forecasting Vector Parametric Models and Methods -- Vector Semi- and Nonparametric Methods.
520 $a 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.
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