I would like to parse the MAE (Mean Absolute Error) to a percentage value. I know there is the MAPE (Mean Absolute Percentage Error), however it has some drawbacks as going to infinity if one of my values is zero. I had the idea of dividing the MAE by the average of my values, but I could not find any reference on that.

The formula I intended to use is the following, having $y$ as the real value and $\bar y$ as the prediction:

$\frac{MAE}{AVG(y)} = \frac{\frac{\lvert y_1 -\bar y_1\rvert + \lvert y_2 -\bar y_2\rvert}{n}}{\frac{\lvert y_1 + y_2\rvert}{n}} = \frac{\lvert y_1 -\bar y_1\rvert + \lvert y_2 -\bar y_2\rvert}{\lvert y_1 + y_2\rvert} = \sum{\frac{\lvert y -\bar y\rvert}{\sum{\lvert y\rvert}}}$

Using this, the percentage Error could only get to infinity if all y values are 0 which will never happen in my dataset. Please note that I cannot have negative Values for $y$ and $\bar y$ in my dataset, so the difference will always be the true difference.

In the end I want to say for example: "the average error in my prediction is 10%"

Does anyone have any sources that this was done before? Is there some serious drawbacks to it or do I overlook anything?


Shameless piece of self-promotion: Kolassa & Schütz (2007, Foresight) call this quantity the "MAD/Mean" or "weighted MAPE" (because it is) and discuss it.

As to drawbacks, the wMAPE, as a scaled MAD, will reward biased forecasts if your future distribution is asymmetrical, just like the "plain" MAD (Kolassa, 2019, IJF).

(Yes, I do have a thing for forecast accuracy measures.)

Feel free to ping me for the papers on ResearchGate.

  • 1
    $\begingroup$ Hi Stephan, thank you so much for leading me in the right direction. I messaged you on ResearchGate. $\endgroup$ – Michael Villani Sep 4 at 15:31
  • $\begingroup$ Upvoted for the excellent utility of the answer. $\endgroup$ – James Phillips Sep 4 at 16:17

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