| Stationary R-squared | Measures how well the model fits for the stationary part of the time series | 0–1 (higher is better) | Close to 1 suggests a good fit for stationary data |
| R-squared | Indicates proportion of the variance in the dependent variable that is predicTable from independent variables | 0–1 (higher is better) | Close to 1 for models with high explanatory power |
| RMSE | Root mean square error, measuring the average magnitude of residuals | Non-negative real numbers | Close to zero indicates minimal prediction error |
| MAPE | Mean absolute percentage error, measuring accuracy as a percentage | 0.0%–100.0% | Values below 10.0% are considered as good |
| MaxAPE | Maximum absolute percentage error, shows the largest absolute percentage error | 0.0%–100.0% | Lower values (close to zero) are better |
| MAE | Mean absolute error, providing the average absolute difference between predicted and observed values | Non-negative real numbers | Close to zero indicates lower average error |
| MaxAE | Maximum absolute error, indicating the largest absolute error observed | Non-negative real numbers | Lower values (close to zero) are better |
| Normalized BIC | Bayesian information criterion (BIC), adjusted for model comparison on a standardized scale | Real numbers (lower is better) | The lowest number among compared models is preferred |