The moving average model is used to exclude irregularities in the time series pattern. The model calculates the average of the n last time series values. The average can always be calculated from n values according to formula (1).

Therefore, the new average is calculated from the previous average and the current consumption value weighted with 1/n, minus the oldest consumption value weighted with 1/n.

This procedure is suitable for time series which are constant; that is, for time series with no trend-like or season-like patterns. As all historical data is equally weighted with the factor 1/n, it takes precisely n periods for the forecast to adapt to a possible level change.