Forecasting with Exponential Smoothing The State Space Approach
Exponential smoothing methods have been around since the 1950s, and are the most popular forecasting methods used in business and industry. Recently, exponential smoothing has been revolutionized with the introduction of a complete modeling framework inco
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Springer Series in Statistics Advisors: P. Bickel, P. Diggle, S. Fienberg, U. Gather, I. Olkin, S. Zeger
Rob J. Hyndman, Anne B. Koehler, J. Keith Ord and Ralph D. Snyder
Forecasting with Exponential Smoothing The State Space Approach
Professor Rob Hyndman Department of Econometrics & Business Statistics Monash University Clayton VIC 3800 Australia [email protected]
Professor Anne Koehler Department of Decision Sciences & Management Information Systems Miami University Oxford, Ohio 45056 USA [email protected]
Professor Keith Ord McDonough School of Business Georgetown University Washington DC 20057 USA [email protected]
Associate Professor Ralph Snyder Department of Econometrics & Business Statistics Monash University Clayton VIC 3800 Australia [email protected]
ISBN 978-3-540-71916-8
e-ISBN 978-3-540-71918-2
Library of Congress Control Number: 2008924784 © 2008 Springer-Verlag Berlin Heidelberg This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permissions for use must always be obtained from Springer-Verlag. Violations are liable for prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, 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. Cover design: Deblik, Berlin, Germany Printed on acid-free paper 9 8 7 6 5 4 3 2 1 springer.com
Preface
Exponential smoothing methods have been around since the 1950s, and are still the most popular forecasting methods used in business and industry. Initially, a big attraction was the limited requirements for computer storage. More importantly today, the equations in exponential smoothing methods for estimating the parameters and generating the forecasts are very intuitive and easy to understand. As a result, these methods have been widely implemented in business applications. However, a shortcoming of exponential smoothing has been the lack of a statistical framework that produces both prediction intervals and point forecasts. The innovations state space approach provides this framework while retaining the intuitive nature of exponential smoothing in its measurement and state equations. It provides prediction intervals, maximum likelihood estimation, procedures for model selection, and much more. As a result of this framework, the area of exponential smoothing has undergone a substantial revolution in the past ten years. The new innovations state space framework for exponential smoothing has been discussed in numerous journal art
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