Forecasting with exponential smoothing : the state space approach

✍ Scribed by Rob J. Hyndman ... [et al.]

Publisher: Springer
Year: 2008
Tongue: English
Leaves: 356
Series: Springer series in statistics
Edition: English
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

Exponential smoothing methods have been around since the 1950s, and are the most popular forecasting methods used in business and industry. This book brings together various results on the state space framework for exponential smoothing. It is of interest to people wanting to apply the methods in their own area of interest.

Basic concepts --

Getting started --

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 --

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 --

Inventory control applications --

Conditional heteroscedasticity and applications in finance --

Economic applications : the Beveridge-Nelson decomposition --

References --

Author index --

Data index --

Subject index

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