| Indicator | Name | Key Characteristics |
|---|---|---|
| MAE | Mean Absolute Error | Measures average error magnitude with equal weighting, robust to outliers |
| RMSE | Root Mean Squared Error | Emphasizes larger errors through squaring, maintains original units |
| HUBER | Huber Loss | Combines MSE for small errors and MAE for large errors, balancing sensitivity and robustness |
| MSE | Mean Squared Error | Heavily weights large errors, produces squared units |
| ME | Mean Error | Preserves error direction to reveal systematic bias |
| MAPE | Mean Absolute Percentage Error | Expresses errors as percentages, enabling cross-scale comparison |
| SMAPE | Symmetric Mean Absolute Percentage Error | Improves on MAPE with symmetric treatment of errors |
| MPE | Mean Percentage Error | Reveals directional bias in percentage terms |
| MAPD | Mean Absolute Percentage Difference | Treats both series equally using average as denominator |
| MSLE | Mean Squared Logarithmic Error | Focuses on proportional rather than absolute errors |
| RMSLE | Root Mean Squared Logarithmic Error | Square root of MSLE, maintains better interpretability |
| MASE | Mean Absolute Scaled Error | Scales errors against naive forecast performance |
| RAE | Relative Absolute Error | Normalizes absolute errors against baseline model |
| RSE | Relative Squared Error | Normalizes squared errors against baseline model |
| RSQUARED | R-Squared | Measures proportion of variance explained by the model |
| DIRTY | Dirty Data Injection | Simulates missing data to test indicator robustness |