Question

What do both the time series decomposition and Winters’ exponential smoothing have in common when dealing with seasonality?

Multiple Choice

Both calculate the seasonality by summing the periods and dividing by the number of periods.

Both assume stationary data.

Both utilize seasonal indices.

Both average the most recent values to deseasonalize the data.

Answer 1

The common answer for both is :

Both use seasonal indices

A) Both calcluate seasonality by summing periods and dividing by no of periods : FALSE since that is incorrect formula

B) Both assume stationary data - False, since seasonal adjustment is performed to negate the seasonality in both.

D) Both average the most recent values to deseasonalize - that would be incorrect to assume the recent data is non-seasonal, and that is an incorrect way of deaseasonalizing.

Hence C is the common answer.