Hey @illmatic! Based on the header of your list table, if your expression is this:
abs('forecast'n - 'actuals'n)
Then you should be good.
Based on what you have, it looks like Total is a part of your list table, which is why it's aggregating that way; however, this is the correct calculation for your overall forecast. Consider the following hypothetical forecast where the Total row is the sum of each column:
Product
Forecast
Actual
Error
Abs Error
A
0
100
-100
100
B
300
200
100
100
C
300
300
0
0
Total
600
600
0
200
Total is a bottom-up forecast created by summing up products A, B, and C. The individual forecasts of A and B are off by -100 and 100 respectively, but the overall bottom-up forecast has an error of 0: the errors in products A and B cancel each other out to create a perfect overall forecast. If you're trying to figure out how good your individual forecasts are, this can be a deceptive metric if your individual forecasts aren't great. The sum of the absolute errors tells us there are 200 total errors among all of the individual forecasts, but it does not mean that the overall forecast has an absolute error of 200.
If you want to capture the total number of absolute errors of all forecasts, you need to remove the Total row and have Visual Analytics calculate the sum for you with the Totals option in a list table:
You can filter out your Total row with an object filter so you do not need to reload the data:
However, summing up all of the absolute errors is a relative measure and doesn't necessarily tell the whole story. One forecast could have a lot of errors while other forecasts could have a few. It's one indicator that there may be a problem with one or more forecasts, but it cannot be used to judge the accuracy of all hierarchical forecasts, especially if one hierarchy has large values while another hierarchy has low values. If you would like some good tips on how to judge the average accuracy of your hierarchical forecasts, I would recommend posting in the Forecasting and Econometrics forum.
Generally I have always looked at MASE or MAPE values and sorted them in ascending order to view how well individual forecasts are doing.
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