Forecasting with Exponential Smoothing

Forecasting with Exponential Smoothing : The State Space Approach

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Exponential smoothing methods have been around since the 1950s, and are still the most popular forecasting methods used in business and industry. However, a modeling framework incorporating stochastic models, likelihood calculation, prediction intervals and procedures for model selection, was not developed until recently. This book brings together all of the important new results on the state space framework for exponential smoothing. It will be of interest to people wanting to apply the methods in their own area of interest as well as for researchers wanting to take the ideas in new directions. Part 1 provides an introduction to exponential smoothing and the underlying models. The essential details are given in Part 2, which also provide links to the most important papers in the literature. More advanced topics are covered in Part 3, including the mathematical properties of the models and extensions of the models for specific problems. Applications to particular domains are discussed in Part 4.
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Product details

  • Paperback | 362 pages
  • 155 x 235 x 22.86mm | 581g
  • Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Berlin, Germany
  • English
  • 2008 ed.
  • 47 Tables, black and white; XIII, 362 p.
  • 3540719164
  • 9783540719168
  • 1,324,319

Back cover copy

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 incorporating innovations state space models, likelihood calculation, prediction intervals and procedures for model selection. In this book, all of the important results for this framework are brought together in a coherent manner with consistent notation. In addition, many new results and extensions are introduced and several application areas are examined in detail.

Rob J. Hyndman is a Professor of Statistics and Director of the Business and Economic Forecasting Unit at Monash University, Australia. He is Editor-in-Chief of the International Journal of Forecasting, author of over 100 research papers in statistical science, and received the 2007 Moran medal from the Australian Academy of Science for his contributions to statistical research.

Anne B. Koehler is a Professor of Decision Sciences and the Panuska Professor of Business Administration at Miami University, Ohio. She has numerous publications, many of which are on forecasting models for seasonal time series and exponential smoothing methods.

J.Keith Ord is a Professor in the McDonough School of Business, Georgetown University, Washington DC. He has authored over 100 research papers in statistics and its applications and ten books including Kendall's Advanced Theory of Statistics.

Ralph D. Snyder is an Associate Professor in the Department of Econometrics and Business Statistics at Monash University, Australia. He has extensive publications on business forecasting and inventory management. He has played a leading role in the establishment of the class of innovations state space models for exponential smoothing.
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Table of contents

I. Introduction: Basic concepts.- Getting started. II. Essentials: Linear innovations state space models.- Non-linear and heteroscedastic innovations state space models.- Estimation of innovations state space models.- Prediction distributions and intervals.- Selection of models. III. Further topics: Normalizing seasonal components.- Models with regressor variables.- Some properties of linear models.- Reduced forms and relationships with ARIMA models.- Linear innovations state space models with random seed states.- Conventional state space models.- Time series with multiple seasonal patterns.- Non-linear models for positive data.- Models for count data.- Vector exponential smoothing. IV. Applications: Inventory control application.- Conditional heteroscedasticity and finance applications.- Economic applications: the Beveridge-Nelson decomposition.
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