MAPE (and its complement (1-MAPE) =Forecast Accuracy) are popular in business because of their alleged interpretability. Imagine you have a series of three actual sales and two forecasters:

Actual Sales	Darnell’s forecast	Kade’s forecast
50	100	55
5000	5050	7000
100	50	110

--> Darnell thinks his forecast is better, and Kade thinks their forecast is better. Decide who wins by using Excel, showing all formulas that show calculations.

--> With your Excel model, answer the following questions:

1. What is the MAPE for each forecaster? (0.2 points)

2. What is the forecast accuracy (1-MAPE) for each forecaster? (0.2 points)

3. What is the MAE for each forecaster? (0.2 points)

4. What is the rMSE for each forecaster? (0.2 points)

B. Who is a “better” forecaster? (If you did the above right, one forecast is better at one metric, and the other forecaster is better at the other metric). Your task is to be able to articulate clearly why that is: what is each metric capturing that causes the switch?

C. Why is the rMSE of Kade’s forecast much bigger than that of Darnell’s?

Answer rating: 100% (QA)

To compare Darnells and Kades forecasts well calculate several metrics including MAPE forecast accuracy 1MAPE MAE and rMSE using Excel Lets go through ... View the full answer