Modeling and Stochastic Learning for Forecasting in High Dimensions
The chapters in this volume stress the need for advances in theoretical understanding to go hand-in-hand with the widespread practical application of forecasting in industry.Forecasting and time series prediction have enjoyed considerable attention over t
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Anestis Antoniadis Jean-Michel Poggi Xavier Brossat Editors
Modeling and Stochastic Learning for Forecasting in High Dimensions
Lecture Notes in Statistics Edited by P. Bickel, P. Diggle, S.E. Fienberg, U. Gather, I. Olkin, S. Zeger
217
More information about this series at http://www.springer.com/series/694
Anestis Antoniadis • Jean-Michel Poggi • Xavier Brossat Editors
Modeling and Stochastic Learning for Forecasting in High Dimensions
123
Editors Anestis Antoniadis Department of Statistics University Joseph Fourier Grenoble, France
Jean-Michel Poggi Laboratoire de Mathématiques Université Paris-Sud Orsay Cedex, France
Xavier Brossat Electricité de France R & D, OSIRIS Clamart Cedex, France
ISSN 0930-0325 Lecture Notes in Statistics ISBN 978-3-319-18731-0 DOI 10.1007/978-3-319-18732-7
ISSN 2197-7186 (electronic) ISBN 978-3-319-18732-7 (eBook)
Library of Congress Control Number: 2015941890 Springer Cham Heidelberg New York Dordrecht London © Springer International Publishing Switzerland 2015 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. Printed on acid-free paper Springer International Publishing AG Switzerland is part of Springer Science+Business Media (www.springer.com)
Preface
Forecasting and time series prediction have seen a great deal of development and attention over the last few decades, fostered by an impressive improvement in observational capabilities and measurement procedures. Time series prediction is a challenge in many fields. In finance, one forecasts stock exchange or stock market indices; data processing specialists forecast the flow of information on their networks; producers of electricity forecast the electric load, and hydrologists forecast river floods. Many methods designed for time series prediction and forecasting perform well (depending on the complexity of the problem) on a rather short-term horizon but are rather poor on a longer-term one. This is due to the fact that these methods are usually designed to optimize the perf
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