Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| MAPE symétrique (sMAPE)× | Erreur quadratique moyenne (RMSE)× | |
|---|---|---|
| Domaine | Évaluation de modèles | Évaluation de modèles |
| Famille | MCDM | MCDM |
| Année d'origine≠ | 1985 | 1809 |
| Auteur d'origine≠ | J. Scott Armstrong | Carl Friedrich Gauss |
| Type≠ | Symmetric percentage-based evaluation metric | Distance-based evaluation metric |
| Source fondatrice≠ | Armstrong, J. S. (1985). Long-range forecasting: from crystal ball to computer (2nd ed.). New York: John Wiley & Sons. ISBN: 978-0471082010 | Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser. link ↗ |
| Alias | sMAPE, SMAPE, symmetric MAPE | RMSE, RMS error, quadratic mean error |
| Apparentées | 4 | 4 |
| Résumé≠ | Symmetric Mean Absolute Percentage Error is a refinement of MAPE that addresses its asymmetry by using the average of actual and predicted values as the denominator. Proposed by J. Scott Armstrong and refined by Makridakis (1993) and Hyndman & Koehler (2006), sMAPE treats over- and under-predictions symmetrically. | Root Mean Squared Error is a widely used metric that measures the average magnitude of prediction errors in regression models. Originating from Carl Friedrich Gauss's work on least-squares estimation (1809), RMSE quantifies how far predictions deviate from observed values by averaging the squared differences and taking the square root. |
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