Exponential Smoothing - Choosing the Best Value for Parameter α (Alpha)
Gardner (1985) discusses various theoretical and empirical arguments for selecting an appropriate smoothing parameter. Obviously, looking at the formula presented above, α should fall into the interval between 0 (zero) and 1 (although, see Brenner et al., 1968, for an ARIMA perspective, implying 0<α<2). Gardner (1985) reports that among practitioners, an α smaller than .30 is usually recommended. However, in the study by Makridakis et al. (1982), α values above .30 frequently yielded the best forecasts. After reviewing the literature on this topic, Gardner (1985) concludes that it is best to estimate an optimum α from the data (see below), rather than to "guess" and set an artificially low value.
Estimating the best a value from the data
In practice, the smoothing parameter is often chosen by a grid search of the parameter space; that is, different solutions for α are tried starting, for example, with α = 0.1 to α = 0.9, with increments of 0.1. Then α is chosen so as to produce the smallest sums of squares (or mean squares) for the residuals (i.e., observed values minus one-step-ahead forecasts; this mean squared error is also referred to as ex post mean squared error, ex post MSE for short). The Time Series module provides an option to perform a grid search, and also allows you to automatically search for the best α parameter via a general function minimization procedure.