A New Look at Models for Exponential Smoothing

Exponential smoothing (ES) forecasting methods are widely used but are often discussed without recourse to a formal statistical framework. This paper reviews and compares a variety of potential models for ES. As well as autoregressive integrated moving average and structural models, a promising class of dynamic non-linear state space models is described that allows for a changing variance. The richness of possible models helps to explain why ES methods seem to be robust in practice. A modelling approach can enhance the forecaster's ability to identify pertinent components of time series variation, and to obtain more reliable estimates of prediction error variances. The paper should be of particular interest to those engaged in forecasting applications where strategies that allow for risk and uncertainty are needed.