SSA for Forecasting, Interpolation, Filtering and Estimation
The applications of SSA dealt with in Chap. 3 require the use of models and hence SSA of Chap. 3 is mostly model-based. As the main model, the assumption that the components of the original time series, which are extracted by SSA, satisfy (at least, local
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Nina Golyandina Anatoly Zhigljavsky
Singular Spectrum Analysis for Time Series Second Edition
123
SpringerBriefs in Statistics
More information about this series at http://www.springer.com/series/8921
Nina Golyandina Anatoly Zhigljavsky •
Singular Spectrum Analysis for Time Series Second Edition
123
Nina Golyandina Faculty of Mathematics and Mechanics St. Petersburg State University St. Petersburg, Russia
Anatoly Zhigljavsky School of Mathematics Cardiff University Cardiff, UK
ISSN 2191-544X ISSN 2191-5458 (electronic) SpringerBriefs in Statistics ISBN 978-3-662-62435-7 ISBN 978-3-662-62436-4 (eBook) https://doi.org/10.1007/978-3-662-62436-4 1st edition: © The Author(s) 2013 2nd edition: © The Author(s), under exclusive license to Springer-Verlag GmbH, DE, part of Springer Nature 2020 This work is subject to copyright. All rights are solely and exclusively licensed 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, expressed 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. 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 to the Second Edition
Singular spectrum analysis (SSA) is a technique of time series analysis and forecasting. It combines elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. SSA can be very useful in solving a variety of problems such as forecasting, imputation of missing values, decomposition of the original time series into a sum of a small number of interpretable components such as a slowly varying trend, oscillatory components and a ‘structureless’ noise. For applying the core SSA algorithms, neither a parametric model nor stationarity-type conditions have to be assumed. This makes some versions of SSA model-free, which enables SSA to have a very wide range of applicability. The rapidly increasing number of new appli
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