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.
Inhalt
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 BeveridgeNelson Decomposition.
Titel
Forecasting with Exponential Smoothing
Untertitel
The State Space Approach
EAN
9783540719182
ISBN
978-3-540-71918-2
Format
E-Book (pdf)
Hersteller
Herausgeber
Veröffentlichung
19.06.2008
Digitaler Kopierschutz
Wasserzeichen
Dateigrösse
2.89 MB
Anzahl Seiten
362
Jahr
2008
Untertitel
Englisch
Unerwartete Verzögerung
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