BACHELOR OF BUSINESS ADMINISTRATION

Detailed explanation-1: -Question: With reference to exponential forecasting models, a parameter that provides the weight given to the most recent time series value in the calculation of the forecast value is known as the smoothing constant.

Detailed explanation-2: -Exponential smoothing is a forecasting method for univariate time series data. This method produces forecasts that are weighted averages of past observations where the weights of older observations exponentially decrease.

Detailed explanation-3: -This approach uses historical data of demand to produce forecasts. It’s different from the moving averages method, and there are some advantages and disadvantages. In exponential smoothing, there’s a value associated as a smoothing constant. This number is between 0 and 1.

Detailed explanation-4: -The exponential smoothing calculation is as follows: The most recent period’s demand multiplied by the smoothing factor. The most recent period’s forecast multiplied by (one minus the smoothing factor). S = the smoothing factor represented in decimal form (so 35% would be represented as 0.35).

Detailed explanation-5: -The exponential smoothing forecast for any period is a weighted average of all the previous actual values for the time series. The mean squared error is influenced much more by large forecast errors than by small errors.