Question

In the SES forecasting model, as alpha gets larger (i.e., closer to 1), the weight we place on distant past values of our variable of interest gets smaller.

Answer 1

TRUE

Explanation: The simplest of the exponentially smoothing methods is naturally called simple exponential smoothing (SES). This method is suitable for forecasting data with no clear trend or seasonal pattern. Alpha is the smoothing parameter.

For any α between 0 and 1, the weights attached to the observations decrease exponentially as we go back in time, hence the name “exponential smoothing”. If α is small (i.e., close to 0), more weight is given to observations from the more distant past. If α is large (i.e., close to 1), more weight is given to the more recent observations.