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

Forecasting with Exponential Smoothing (eBook, PDF)

The State Space Approach

• Format: PDF

Produktbeschreibung

Exponential smoothing methods have been around since the 1950s, and are still the most popular forecasting methods used in business and industry. However, a modelling framework incorporating stochastic models, likelihood calculation, prediction intervals and procedures for model selection, was not developed until relatively 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. In short, the book gives an overview of current topics and develops new ideas that have not appeared in the academic literature.

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Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 362
• Erscheinungstermin: 19. Juni 2008
• Englisch
• ISBN-13: 9783540719182
• Artikelnr.: 37357921

Inhaltsangabe

Basic Concepts.- Getting Started.- Essentials.- Linear Innovations State Space Models.- Nonlinear and Heteroscedastic Innovations State Space Models.- Estimation of Innovations State Space Models.- Prediction Distributions and Intervals.- Selection of Models.- 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.- Nonlinear Models for Positive Data.- Models for Count Data.- Vector Exponential Smoothing.- Applications.- Inventory Control Applications.- Conditional Heteroscedasticity and Applications in Finance.- Economic Applications: The Beveridge-Nelson Decomposition.