Respuesta :
The formula for the exponential smoothing is as follows: Demand for the most recent period times the smoothing factor.
The forecast for the most recent time period multiplied by (one minus the smoothing factor). S is the smoothing factor, which is expressed in decimal form (35% would be 0.35). A time series forecasting technique called exponential smoothing can be expanded to support data with a systematic trend or seasonal component. It is a potent forecasting technique that can be employed in place of the well-liked Box-Jenkins ARIMA family of techniques. A forecasting technique for univariate time series data is exponential smoothing. With this strategy, forecasts are weighted averages of historical observations, with the weights of older observations decreasing exponentially. Alpha, the smoothing constant, is 0.20. (Option a)
Detailed explanation:
To begin resolving this issue, we first list the simple exponential smoothing in succession:
Where xt is the real demand for period t, alpha is the smoothing constant, s(t) is the forecast for period t, s(t-1) is the forecast for period (t-1), and
There are no unknowns other than the alpha constant: s(t)=109.2, s(t-2)=110, xt=110-4=106.
After that, we may determine alpha as:
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