Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Vidējā absolūtā skalotā kļūda (MASE)× | Vidējā absolūtā kļūda (MAE)× | |
|---|---|---|
| Nozare | Modeļu novērtēšana | Modeļu novērtēšana |
| Saime | MCDM | MCDM |
| Izcelsmes gads≠ | 2006 | 1799 |
| Autors≠ | Rob J. Hyndman and Anne B. Koehler | Pierre-Simon Laplace |
| Tips≠ | Scale-independent baseline comparison metric | Robust distance-based metric |
| Pirmavots≠ | Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688. DOI ↗ | Laplace, P. S. (1799). Traité de Mécanique Céleste. Paris: J.B.M. Duprat. link ↗ |
| Citi nosaukumi≠ | MASE | MAE, L1 error, mean absolute deviation |
| Saistītās≠ | 4 | 3 |
| Kopsavilkums≠ | Mean Absolute Scaled Error is a scale-independent metric that measures prediction accuracy relative to a simple baseline (naive forecast). Introduced by Hyndman and Koehler (2006), MASE directly compares model performance to a reference method, overcoming limitations of MAPE and other percentage-based metrics. | Mean Absolute Error is a robust metric that measures the average absolute magnitude of prediction errors in regression models. Dating back to Pierre-Simon Laplace's work on observational errors (1799), MAE quantifies typical prediction deviation by averaging the absolute differences between observed and predicted values. |
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