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Forecasting with Exponential Smoothing

The State Space Approach

  • Book
  • © 2008

Overview

Authors:
  1. Rob Hyndman

    1. Department of Econometrics & Business Statistics, Monash University, Clayton, Australia

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    You can also search for this author in PubMed   Google Scholar

  2. Anne Koehler

    1. Department of Decision Sciences & Management Information Systems, Miami University, Oxford, USA

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    You can also search for this author in PubMed   Google Scholar

  3. Keith Ord

    1. McDonough School of Business, Georgetown University, Washington DC, USA

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    You can also search for this author in PubMed   Google Scholar

  4. Ralph Snyder

    1. Department of Econometrics & Business Statistics, Monash University, Clayton, Australia

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    You can also search for this author in PubMed   Google Scholar

  • Provides solid intellectual foundation for exponential smoothing methods
  • Gives overview of current topics and develops new ideas that have not appeared in the academic literature
  • The forecast package for R implements the methods described in the book
  • Includes supplementary material: sn.pub/extras

Part of the book series: Springer Series in Statistics (SSS)

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Table of contents (20 chapters)

  1. Front Matter

    Pages I-XIII
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  2. Introduction

    1. Basic Concepts

      Pages 3-7

    2. Getting Started

      Pages 9-29
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Keywords

About this book

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.

Authors and Affiliations

  • Department of Econometrics & Business Statistics, Monash University, Clayton, Australia

    Rob Hyndman, Ralph Snyder

  • Department of Decision Sciences & Management Information Systems, Miami University, Oxford, USA

    Anne Koehler

  • McDonough School of Business, Georgetown University, Washington DC, USA

    Keith Ord

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