Automatic trend estimation
Our book introduces a method to evaluate the accuracy of trend estimation algorithms under conditions similar to those encountered in real time series processing. This method is based on Monte Carlo experiments with artificial time series numerically gene
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Editorial Board Egor Babaev, University of Massachusetts, USA Malcolm Bremer, University of Bristol, Sussex, UK Xavier Calmet, University of Sussex, Sussex, UK Francesca Di Lodovico, Queen Mary University of London, London, UK Maarten Hoogerland, Universiy of Auckland, Auckland, New Zealand Eric Le Ru, Victoria University of Wellington, Wellington, New Zealand James Overduin, Towson University, USA Vesselin Petkov, Concordia University, Canada Charles H.-T. Wang, University of Aberdeen, UK Andrew Whitaker, Queen’s University Belfast, UK
For further volumes: http://www.springer.com/series/8902
Ca˘lin Vamosß Maria Cra˘ciun •
Automatic Trend Estimation
123
Ca˘lin Vamosß ‘‘Tiberiu Popoviciu’’ Institute of Numerical Analysis Romanian Academy Cluj-Napoca Romania
ISSN 2191-5423 ISBN 978-94-007-4824-8 DOI 10.1007/978-94-007-4825-5
Maria Cra˘ciun ‘‘Tiberiu Popoviciu’’ Institute of Numerical Analysis Romanian Academy Cluj-Napoca Romania
ISSN 2191-5431 (electronic) ISBN 978-94-007-4825-5 (eBook)
Springer Dordrecht Heidelberg New York London Library of Congress Control Number: 2012942924 Ó The Author(s) 2012 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. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. 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. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Preface
Huge amount of information is available as time series in many scientific fields: geophysics, astronom
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