Question

Maverick Jeans' demand manager decided to evaluate exponential smoothing. To maintain comparability, she used the data from problem 6.

a. Use a starting forecast of 20 for month 4 to develop forecasts for months 5−12 and the first month of next year. Use smoothing constants (α) of 0.2,0.5, and 0.8 . Calculate the average forecast error and mean absolute error.
b. What observations do you have? How does exponential smoothing compare to the results for moving averages from problem 6 ?

Answer 1

The student is required to apply exponential smoothing to make forecasts using different smoothing constants and compare these to previous moving averages. The forecast error and mean absolute error need to be calculated for each constant.

Step-by-step explanation:

The student's question involves exponential smoothing which is a method used for making forecasts in time series data. Starting with a smoothing constant α, we would use it to calculate the forecast for the next period by combining the actual value of the previous period and the forecasted value of that period. Using different values of α changes how much weight is put on the most recent observations. The student is asked to calculate forecasts using α values of 0.2, 0.5, and 0.8 and then compare the results to those obtained with moving averages in their previous work.

To calculate the average forecast error and mean absolute error, you would subtract the forecast values from the actual values to find the errors, calculate their average, and find the mean of their absolute values for each α value across the given months.