Forecasting with Exponential Smoothing

by Hyndman, Rob; Koehler, Anne B.; Ord, J. Keith; Snyder, Ralph D.

• ISBN13: 9783540719168
• ISBN10: 3540719164
• Format: Paperback
• Copyright: 2008-09-03
• Publisher: Springer Verlag

Summary

"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."--BOOK JACKET.

Author Biography

Ralph D. Snyder is an Associate Professor in the Department of Econometrics and Business Statistics at Monash University, Australia. J. Keith Ord is a Professor in the McDonough School of Business, Georgetown University, Washington DC. Anne B. Koehler is a Professor of Decision Sciences and the Panuska Professor of Business Administration at Miami University, Ohio. 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.

Table of Contents

Introduction
Basic Concepts	p. 3
Getting Started	p. 9
Essentials
Linear Innovations State Space Models	p. 33
Nonlinear and Heteroscedastic Innovations State Space Models	p. 53
Estimation of Innovations State Space Models	p. 67
Prediction Distributions and Intervals	p. 75
Selection of Models	p. 105
Further Topics
Normalizing Seasonal Components	p. 123
Models with Regressor Variables	p. 137
Some Properties of Linear Models	p. 149
Reduced Forms and Relationships with ARIMA Models	p. 163
Linear Innovations State Space Models with Random Seed States	p. 179
Conventional State Space Models	p. 209
Time Series with Multiple Seasonal Patterns	p. 229
Nonlinear Models for Positive Data	p. 255
Models for Count Data	p. 277
Vector Exponential Smoothing	p. 287
Applications
Inventory Control Applications	p. 303
Conditional Heteroscedasticity and Applications in Finance	p. 317
Economic Applications: The Beveridge-Nelson Decomposition	p. 325
References	p. 339
Author Index	p. 349
Data Index	p. 353
Subject Index	p. 355
Table of Contents provided by Blackwell. All Rights Reserved.