evaluation

rank_fva(y_true, y_pred, y_pred_bench=None, scoring=None, descending=False)

Sorts point forecasts in y_pred across entities / time-series by score.

Parameters:

Name	Type	Description	Default
y_true	DataFrame	Panel DataFrame of observed values.	required
y_pred	DataFrame	Panel DataFrame of point forecasts.	required
y_pred_bench	DataFrame	Panel DataFrame of benchmark forecast values.	None
scoring	Optional[metric]	If None, defaults to SMAPE.	None
descending	bool	Sort in descending order. Defaults to False.	False

Returns:

Name	Type	Description
ranks	DataFrame	Cross-sectional DataFrame with two columns: entity name and score.

rank_point_forecasts(y_true, y_pred, sort_by='smape', descending=False)

Sorts point forecasts in y_pred across entities / time-series by score.

Parameters:

Name	Type	Description	Default
y_true	DataFrame	Panel DataFrame of observed values.	required
y_pred	DataFrame	Panel DataFrame of point forecasts.	required
sort_by	str	Method to sort forecasts by: mean median std cv (coefficient of variation / volatility) mae (mean absolute error) mape (mean absolute percentage error) mase (mean absolute scaled error) mse (mean squared error) rmse (root mean squared error) rmsse (root mean scaled squared error) smape (symmetric mean absolute percentage error) smape_original overforecast underforecast	'smape'
descending	bool	Sort in descending order. Defaults to False.	False

Returns:

Name	Type	Description
ranks	DataFrame	Cross-sectional DataFrame with two columns: entity name and score.

rank_residuals(y_resids, sort_by='abs_bias', descending=False)

Sorts point forecasts in y_pred across entities / time-series by score.

Parameters:

Name	Type	Description	Default
y_resids	DataFrame	Panel DataFrame of residuals by splits.	required
sort_by	str	Method to sort residuals by: bias, abs_bias (absolute bias), normality (via skew and kurtosis tests), or autocorr (Ljung-box test for lag = 1). Defaults to abs_bias.	'abs_bias'
descending	bool	Sort in descending order. Defaults to False.	False

Returns:

Name	Type	Description
ranks	DataFrame	Cross-sectional DataFrame with two columns: entity name and score.