Estimating forecast variance with exponential smoothing Some new results

This note compares MAD and MSE smoothing approaches to estimating the forecast variance for the simple exponential smoothing forecast model. Using simulation techniques, the MSE approach is found to be more efficient than the MAD approach. These results hold for a wide assortment of cases in which both the mean and variance of the underlying demand series are potentially non-stationary. The results are found to be robust even in the presence of outliers. Several heuristic rules are developed for selection of a smoothing coefficient for the variance term, the most significant one being based on an inverse relationship between the optimal smoothing coefficient for the mean and the optimal coefficient for the variance.