Bayesian Exponential Smoothing

In this paper, a Bayesian version of the exponential smoothing method of forecasting is proposed. The approach is based on a state space model containing only a single source of error for each time interval. This model allows an improvement to current practices in exponential smoothing by providing both point predictions and measures of the uncertainty surrounding them. The method proposed calculates posterior prediction and parameter distributions via Monte Carlo composition. We evaluate the method with a Monte Carlo simulation study and apply it to forecasting car part demand. The main advantage of the approach is that it produces exact, small sample prediction distributions. It also works very quickly on modern computing machines.