What is sMAPE and why is it used?
mediumAnswer
- Symmetric MAPE = mean(|| / ) * 100%.
- Bounded in [0%, 200%] and symmetric in over-/under-prediction: over-forecasting and under-forecasting by the same absolute amount give equal error.
- Still undefined when both y and are 0.
- Better than MAPE for time series forecasting competitions (used in M-competitions).
- Not a proper scoring rule for probabilistic forecasts — MASE (mean absolute scaled error) is a better alternative in many cases.
Check yourself — multiple choice
- sMAPE is always identical to MAPE
- Symmetric MAPE — bounded in [0%, 200%], symmetric in over-/under-prediction
- sMAPE is only for classification
- sMAPE ignores the sign of the error
sMAPE symmetrizes MAPE and bounds the error — better for forecasting competitions.
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