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(More customer reviews)This work provides for exponential smoothing what Box and Jenkins "Time Series Analysis: Forecasting and Control" provided for ARIMA models -- an accessible theoretical framework.
Exponential smoothing is a widely used forecasting method that does well in forecasting competitions because it's robust and flexible.
The fact that Hyndman also has a nice R package implementing this framework is an added plus. [...]
Click Here to see more reviews about: Forecasting with Exponential Smoothing: The State Space Approach (Springer Series in Statistics)
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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