Singular Spectrum Analysis with R
This comprehensive and richly illustrated volume provides up-to-date material on Singular Spectrum Analysis (SSA). SSA is a well-known methodology for the analysis and forecasting of time series. Since quite recently, SSA is also being used to analy
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Nina Golyandina · Anton Korobeynikov Anatoly Zhigljavsky
Singular Spectrum Analysis with R
Use R! Series editors Robert Gentleman
Kurt Hornik
Giovanni Parmigiani
More information about this series at http://www.springer.com/series/6991
Nina Golyandina • Anton Korobeynikov • Anatoly Zhigljavsky
Singular Spectrum Analysis with R
123
Nina Golyandina Faculty of Mathematics and Mechanics Saint Petersburg State University Saint Petersburg, Russia
Anton Korobeynikov Faculty of Mathematics and Mechanics Saint Petersburg State University Saint Petersburg, Russia
Anatoly Zhigljavsky School of Mathematics Cardiff University Cardiff, United Kingdom
ISSN 2197-5736 ISSN 2197-5744 (electronic) Use R! ISBN 978-3-662-57378-5 ISBN 978-3-662-57380-8 (eBook) https://doi.org/10.1007/978-3-662-57380-8 Library of Congress Control Number: 2018940189 Mathematics Subject Classification (2010): 37M10, 68U10 © Springer-Verlag GmbH Germany, part of Springer Nature 2018 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 the registered company Springer-Verlag GmbH, DE part of Springer Nature. The registered company address is: Heidelberger Platz 3, 14197 Berlin, Germany
Preface
Singular spectrum analysis (SSA) is a well-known methodology for analysis and forecasting of time series. Since quite recently, SSA was also used to analyze digital images and other objects that are not necessarily of planar or rectangular form and may contain gaps. SSA is multipurpose and naturally combines both model-free and parametric techniques; this makes it a very special and attractive methodology for solving a wide range of problems arising in diverse areas, most notably those associated with time series and digital images. An effective, comfortable, and accessible implementation of SSA is provided by the R-package Rssa, which
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