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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.
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