Answer: e. 1
Step-by-step explanation: a key problem in exponential smoothing is the choice of the values used for smoothing constants. It is easy to understand and quite easy to use, making it one of the most popular methods for forecasting. The forecast Ft+1 for the upcoming period is the estimate of average level Lt at the end of period t.
where α, the smoothing constant, is between 0 and 1. We can interpret the new forecast as the old forecast adjusted by some fraction of the forecast error. The new estimate of level as a weighted average of Dt (the most recent information on average level) and Ft (our previous estimate of that level). Lt (and Ft+1 ) can be written in terms of all previous demand.
Thus, Ft+1 is a weighted average of all previous demand with the weight on Di given by α(1-α)t-i where t is the period
that just ended. As t increases the sum of these weights tends to 1.
Based on the last-value forecast, the smoothing constant that would make a exponential smoothing forecast equivalent to the aforementioned is e. 1.
In time period (t + 1), the exponential smoothing forecast would be:
S (t - 1) = Smoothing constant x Yt + ( 1 - smoothing constant) x St
The last value forecast is:
S(t + 1) = Yt
To make the exponential smoothing forecast to the last-value forecast therefore, the best smoothing constant would be 1:
S (t - 1) = 1 x Yt + ( 1 - 1) x St
S(t + 1) = Yt
Find out more on smoothing constants at https://brainly.com/question/14240309.
