E-Book Overview
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.
E-Book Content
Springer Series in Statistics Forecasting with Exponential Smoothing The State Space Approach 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