A state space framework for automatic forecasting using exponential smoothing methods

A new approach to automatic business forecasting based on an extended range of exponential smoothing methods that allows the easy calculation of the likelihood, the AIC and other model selection criteria, and the computation of prediction intervals for each method.

Abstract

We provide a new approach to automatic forecasting based on an extended range of exponential smoothing methods. Each method in our taxonomy of exponential smoothing methods provides forecasts that are equivalent to forecasts from a state space model. This equivalence allows: (1) easy calculation of the likelihood, the AIC and other model selection criteria; (2) computation of prediction intervals for each method; and (3) random simulation from the underlying state space model. We demonstrate the methods by applying them to the data from the M-competition and the M3-competition. The method provides forecast accuracy comparable to the best methods in the competitions; it is particularly good for short forecast horizons with seasonal data.