Forecasting with exponential smoothing: the state space approach

Exponential smoothing methods have been around since the 1950s, and are stillthe 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 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. Part 1 provides an introduction to exponential smoothing and the underlying models. Theessential 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, extensions of the modelsfor specific problems, and applications to particular domains. Provides solidintellectual 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 Many graphics INDICE: I. Introduction: Basic concepts.- Getting started. II. Essentials:Linear innovation state space models.- Non-linear and heteroscedastic innovation state space models.- Estimation of innovation state space models.- Prediction distributions and intervals.- Model selection. III. Further topics: Normalizing seasonal components.- Model properties.- Reduced forms and relationshipswith ARIMA models.- Linear innovation state space models with random seed states.- Alternative state space model formulations.- Models with regressor variables.- Multiple seasonality.- Non-linear models for positive data.- Models forcount data.- Vector exponential smoothing.- Bayesian estimation.- Inventory control application.- Economic applications.- Conditional heteroscedasticity and finance applications.

• ISBN: 978-3-540-71916-8
• Editorial: Springer
• Encuadernacion: Rústica
• Páginas: 360
• Fecha Publicación: 01/07/2008
• Nº Volúmenes: 1
• Idioma: Inglés