Elements of Nonlinear Time Series Analysis and Forecasting
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 applicati
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Jan G. De Gooijer
Elements of Nonlinear Time Series Analysis and Forecasting
Springer Series in Statistics
Series editors Peter Bickel, CA, USA Peter Diggle, Lancaster, UK Stephen E. Fienberg, Pittsburgh, PA, USA Ursula Gather, Dortmund, Germany Ingram Olkin, Stanford, CA, USA Scott Zeger, Baltimore, MD, USA
More information about this series at http://www.springer.com/series/692
Jan G. De Gooijer
Elements of Nonlinear Time Series Analysis and Forecasting
123
Jan G. De Gooijer University of Amsterdam Amsterdam, The Netherlands
ISSN 0172-7397 ISSN 2197-568X (electronic) Springer Series in Statistics ISBN 978-3-319-43251-9 ISBN 978-3-319-43252-6 (ebook) DOI 10.1007/978-3-319-43252-6 Library of Congress Control Number: 2017935720 © Springer International Publishing Switzerland 2017 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
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Preface
Empirical time series analysis and modeling has been deviating, over the last 40 years or so, from the linear paradigm with the aim of incorporating nonlinear features. Indeed, there are various occasions when subject-matter, theory or data suggests that a time series is generated by a nonlinear stochastic process. If theory could provide some understanding of the nonlinear phenomena underlying the data, the modeling process would be relatively easy, with estimation of the model parameters being all that is required. However, this option is rarely available in practice. Alternatively, a particular nonlinear model may be selected, fitted to the data and subjected to a battery of diagnostic tests to check for features that the model has failed adequately to approximate. Although this appro
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