Performance metrics

The aeon.performance_metrics module contains metrics for evaluating and tuning time series models.

Metrics for assessing model performance.

Forecasting

mean_absolute_scaled_error(y_true, y_pred[, ...])	Mean absolute scaled error (MASE).
median_absolute_scaled_error(y_true, y_pred)	Median absolute scaled error (MdASE).
mean_squared_scaled_error(y_true, y_pred[, ...])	Mean squared scaled error (MSSE) or root mean squared scaled error (RMSSE).
median_squared_scaled_error(y_true, y_pred)	Median squared scaled error (MdSSE) or root median squared scaled error (RMdSSE).
mean_absolute_error(y_true, y_pred[, ...])	Mean absolute error (MAE).
mean_squared_error(y_true, y_pred[, ...])	Mean squared error (MSE) or root mean squared error (RMSE).
median_absolute_error(y_true, y_pred[, ...])	Median absolute error (MdAE).
median_squared_error(y_true, y_pred[, ...])	Median squared error (MdSE) or root median squared error (RMdSE).
geometric_mean_absolute_error(y_true, y_pred)	Geometric mean absolute error (GMAE).
geometric_mean_squared_error(y_true, y_pred)	Geometric mean squared error (GMSE) or Root geometric mean squared error (RGMSE).
mean_absolute_percentage_error(y_true, y_pred)	Mean absolute percentage error (MAPE) or symmetric version.
median_absolute_percentage_error(y_true, y_pred)	Median absolute percentage error (MdAPE) or symmetric version.
mean_squared_percentage_error(y_true, y_pred)	Mean squared percentage error (MSPE) or square root version.
median_squared_percentage_error(y_true, y_pred)	Median squared percentage error (MdSPE) or square root version.
mean_relative_absolute_error(y_true, y_pred)	Mean relative absolute error (MRAE).
median_relative_absolute_error(y_true, y_pred)	Median relative absolute error (MdRAE).
geometric_mean_relative_absolute_error(...)	Geometric mean relative absolute error (GMRAE).
geometric_mean_relative_squared_error(...[, ...])	Geometric mean relative squared error (GMRSE).
mean_asymmetric_error(y_true, y_pred[, ...])	Calculate mean of asymmetric loss function.
mean_linex_error(y_true, y_pred[, a, b, ...])	Calculate mean linex error.
relative_loss(y_true, y_pred[, ...])	Relative loss of forecast versus benchmark forecast for a given metric.

Segmentation

count_error(true_change_points, ...)	Error counting the difference in the number of change points.
hausdorff_error(true_change_points, ...[, ...])	Compute the Hausdorff distance between two sets of change points.
prediction_ratio(true_change_points, ...)	Prediction ratio is the ratio of number of predicted to true change points.

Anomaly Detection

range_precision(y_true, y_pred[, alpha, ...])	Compute the range-based precision metric.
range_recall(y_true, y_pred[, alpha, ...])	Compute the range-based recall metric.
range_f_score(y_true, y_pred[, beta, ...])	Compute the F-score using the range-based recall and precision metrics.
roc_auc_score(y_true, y_score)	Compute the ROC AUC score.
pr_auc_score(y_true, y_score)	Compute the precision-recall AUC score.
rp_rr_auc_score(y_true, y_score[, ...])	Compute the AUC-score of the range-based precision-recall curve.
f_score_at_k_points(y_true, y_score[, k])	Compute the F-score at k based on single points.
f_score_at_k_ranges(y_true, y_score[, k])	Compute the range-based F-score at k based on anomaly ranges.
range_pr_roc_auc_support(y_true, y_score[, ...])	Compute the range-based PR and ROC AUC.
range_roc_auc_score(y_true, y_score[, ...])	Compute the range-based area under the ROC curve.
range_pr_auc_score(y_true, y_score[, ...])	Compute the area under the range-based PR curve.
range_pr_vus_score(y_true, y_score[, ...])	Compute the range-based PR VUS score.
range_roc_vus_score(y_true, y_score[, ...])	Compute the range-based ROC VUS score.

AD Thresholding

percentile_threshold(y_score, percentile)	Calculate a threshold based on a percentile of the anomaly scores.
sigma_threshold(y_score[, factor])	Calculate a threshold based on the standard deviation of the anomaly scores.
top_k_points_threshold(y_true, y_score[, k])	Calculate a threshold such that at least k anomalous points are found.
top_k_ranges_threshold(y_true, y_score[, k])	Calculate a threshold such that at least k anomalies are found.