Consider monthly demand for the ABC Corporation as shown in the table below. With these demand values, estimate a static (non adaptive) linear regression to identify static values of level, trend, and seasonality. To do this you will need to follow these steps we went through in class: de-seasonalize demand, calculate and run the linear regression for the de-seasonalized demand, calculate the seasonal factors, and estimate the forecast that incorporates these seasonal factors into the de-seasonalized regression.
Please present a table with your results below
Based on the level, trend, and seasonality values that you calculated and the changes that demand has experienced over time, what would be the simplest adaptive (dynamic) forecasting approach would you suggest ABC should follow to predict future demand as accurately as possible? Please answer this question after presenting your results' table below. Please justify this part of your answer conceptually. You do not need to perform any calculations.
|
Monthly Demand for ABC Corporation
|
|
Sales
|
Year1
|
Year 2
|
Year 3
|
Year 4
|
Year 5
|
|
January
|
2,000
|
3,000
|
2,000
|
5,000
|
5,000
|
|
February
|
3,000
|
4,000
|
5,000
|
4,000
|
2,000
|
|
March
|
3,000
|
3,000
|
5,000
|
4,000
|
3,000
|
|
April
|
3,000
|
5,000
|
3,000
|
2,000
|
2,000
|
|
May
|
4,000
|
5,000
|
4,000
|
5,000
|
7,000
|
|
June
|
6,000
|
8,000
|
6,000
|
7,000
|
6,000
|
|
July
|
7,000
|
3,000
|
7,000
|
10,000
|
8,000
|
|
August
|
6,000
|
8,000
|
10,000
|
14,000
|
10,000
|
|
September
|
10,000
|
12,000
|
15,000
|
16,000
|
20,000
|
|
October
|
12,000
|
12,000
|
15,000
|
16,000
|
20,000
|
|
November
|
14,000
|
16,000
|
18,000
|
20,000
|
22,000
|
|
December
|
8,000
|
10,000
|
8,000
|
12,000
|
8,000
|
|
|
|
|
|
|
|
|
Total
|
78,000
|
89,000
|
98,000
|
115,000
|
113,000
|