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What are the shortcomings of the Mean Absolute Percentage Error (MAPE)? The MAPE, as a percentage, only makes sense for values where divisions and ratios make sense It doesn't make sense to calculate percentages of temperatures, for instance, so you shouldn't use the MAPE to calculate the accuracy of a temperature forecast
forecasting - ARIMA: How to interpret MAPE? - Cross Validated I interpreted the MAPE like, "on average, the forecast if off by 14%", which sounds fine for me But on the other side the MASE is greater than 1, which means the model is worse than a naive model
RMSE or MAPE? which one to choose for accuracy? If you truly want to find a MAPE-optimal forecast, you should also use the MAPE to fit your model I am not aware of any off-the-shelf forecasting software that does this (if you use an ML pipeline, you may be able to specify any fitting criterion and choose the MAPE), and I have major doubts as to the usefulness of a MAPE-minimal forecast
time series - Is there any standard criteria of good forecast . . . I found an interesting reference about the criteria of a good forecast result based on MAPE from Lewis (1982): but in my case, I can't use MAPE for some dataset because there is some zero actual demand so it can't be used as denominator in MAPE
Why is the MAPE exceptionally high [closed] - Cross Validated The first two scores are MSE and MAE, and the last one is MAPE, how is this possible ? As a side question which might help answer, my dataset contains a good number of examples where the "good" answer is 0
time series - Is MAPE a good error measurement statistic? And what . . . It is correct that the over-forecast and under-forecast by the same amount results in the same MAPE value for a fixed actual However, imagine a situation where the actual can be 1 or 3 with 1 2 probability each In this case, the forecast which minimises the expected MAPE is 1 5 rather than a seemingly more obvious 2, hence the "asymmetry" label
Alternative to mean absolute percentage error (MAPE) MAPE metric has problems when the actual value to be predicted is very small In the extreme when the actual value is 0 then MAPE will be infinity (if the prediction is not exactly 0) What about t