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.
Table des matières
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.
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