Determining appropriate smoothing constants

The smoothing constants determine the level at which past observations influence the forecast.

You can enter weights, or smoothing parameters, for the level, trend, and seasonal components. The default weights are 0.2 and you can enter values between 0 and 1. Because an equivalent ARIMA model exists only for a very restricted form of the Holt-Winters model, Minitab does not compute optimal parameters for Winters' method as it does for single and double exponential smoothing.

Regardless of the component, large weights result in more rapid changes in that component; small weights result in less rapid changes. The components in turn affect the smoothed values and the predicted values. Thus, small weights are usually recommended for a series with a high noise level around the signal or pattern. Large weights are usually recommended for a series with a small noise level around the signal.