The number of averaging period in the simple moving average method of forecasting is increased for greater smoothing but at the cost of

Explanation:

Exponential smoothing method:

\({F_t} = {F_{t - 1}} + α \left[ {{D_{t - 1}} - {F_{t - 1}}} \right]\)

where α = smoothing constant.

Moving Average Method:

The moving average method uses the average of the most recent 'n' data values in the time series as the forecast for the next period.

\({F_{t + 1}} = \frac{{{D_t} + {D_{t - 1}} + \ldots + {D_{t - n + 1}}}}{n}\)

Note that the 'n' past observations are equally weighted.

The simple moving average model described above has the undesirable property that it treats the last 'k' observations equally and completely ignores all preceding observations.

Intuitively, past data should be discounted in a more gradual fashion -- for example, the most recent observation should get a little more weight than 2^nd most recent, and the 2^nd most recent should get a little more weight than the 3rd most recent, and so on. The simple exponential smoothing model accomplishes this.

Thus, the simple exponential smoothing forecast is somewhat superior to the simple moving average forecast because it places relatively more weight on the most recent observation i.e., it is slightly more "responsive to changes" occurring in the recent past.