Smape vs mape4/30/2023 ![]() ![]() There seems little point using the sMAPE except that it makes it easy to compare the performance of a new forecasting algorithm against the published M3 results. Personally, I would much prefer that either the original MAPE be used (when it makes sense), or the mean absolute scaled error (MASE) be used instead. However, I can’t match the published results for any definition of sMAPE, so I’m not sure how the calculations were actually done. ![]() In the M3 competition, all data were positive, but some forecasts were negative, so the differences are important. But more generally, the last definition above from Chen and Yang is clearly the most sensible, if the sMAPE is to be used at all. If all data and forecasts are non-negative, then the same values are obtained from all three definitions of sMAPE. The Wikipedia page on sMAPE contains several as well, which a reader might like to correct. Perhaps this is the definition that Makridakis and Armstrong intended all along, although neither has ever managed to include it correctly in one of their papers or books.Īs will be clear by now, the literature on this topic is littered with errors. The range of this version of sMAPE is (0,2). \text_t is zero, but is undefined when both are zero. The MAPE (mean absolute percentage error) is a popular measure for forecast accuracy and is defined as ![]()
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