' x 1 /




In the simple exponential smoothing model, each smoothed estimate of a given level is computed as a weighted average of the current observation and past data. Each weight decreases in an exponential pattern. The rate of decrease in the influence of past levels depends on the size of the smoothing parameter that controls the model's relative sensitivity to newer versus older data. The larger the value of the smoothing parameter, the more emphasis is placed on recent versus distant observations. However, if the smoothing parameter is very small, then a large number of data points receive nearly equal weights. In this case, the forecast model displays a long "memory" of past values.

two-parameter (Holt) exponential smoothing

Method for forecasting stable growth

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