Some properties of a simple moving average when applied to forecasting a time series

Simple (equally weighted) moving averages are frequently used to estimate t he current level of a time series; with this value being projected as a for ecast for future observations. A key measure of the effectiveness of the me thod is the sampling error of the estimator, which this paper defines in te rms of characteristics of the data. This enables the optimal length of the average for any steady state model to be established and the lead time fore cast error derived. A comparison of the performance of a simple moving aver age (SMA) with an exponentially weighted moving average (EWMA) is made. It is shown that, for a Steady state model, the variance of the forecast error is typically less than 3% higher than the appropriate EWMA. This relativel y small difference may explain the inconclusive results from the empirical studies about the relative predictive performance of the two methods.