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	Metric name.	'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', alpha=0.05, 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'
max_lags	int	Number of lags to test.	required
alpha	float	To compute (1.0 - alpha) confidence interval.	0.05
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.