hierarchicalforecast.probabilistic_methods
Normality
sampler
input as other HierarchicalForecast
reconciliation classes.
Given base forecasts under a normal distribution:
The reconciled forecasts are also normally distributed:
Args:
S
(Union[np.ndarray, sp.spmatrix]): np.array, summing matrix of size (base
, bottom
).P
(Union[np.ndarray, sp.spmatrix]): np.array, reconciliation matrix of size (bottom
, base
).y_hat
(np.ndarray): Point forecasts values of size (base
, horizon
).W
(Union[np.ndarray, sp.spmatrix]): np.array, hierarchical covariance matrix of size (base
, base
).sigmah
(np.ndarray): np.array, forecast standard dev. of size (base
, horizon
).seed
(int, optional): int, random seed for numpy generator’s replicability. Default is 0."Probabilistic forecast reconciliation
: Properties, evaluation and score optimisation”. European Journal of Operational Research.](https://www.sciencedirect.com/science/article/pii/S0377221722006087)__init__
get_prediction_levels
res
(dict): Results dictionary to update.level
(list): Confidence levels.dict
: Updated results dictionary.get_prediction_quantiles
res
(dict): Results dictionary to update.quantiles
(np.ndarray): Quantiles to compute.dict
: Updated results dictionary.get_samples
num_samples
(int): number of samples generated from coherent distribution.np.ndarray
: samples: Coherent samples of size (base
, horizon
, num_samples
).Bootstrap
sampler
input as other HierarchicalForecast
reconciliation classes.
Given a boostraped set of simulated sample paths:
The reconciled sample paths allow for reconciled distributional forecasts:
Args:
S
: np.array, summing matrix of size (base
, bottom
).P
: np.array, reconciliation matrix of size (bottom
, base
).y_hat
: Point forecasts values of size (base
, horizon
).y_insample
: Insample values of size (base
, insample_size
).y_hat_insample
: Insample point forecasts of size (base
, insample_size
).num_samples
: int, number of bootstraped samples generated.seed
: int, random seed for numpy generator’s replicability."Probabilistic Forecast Reconciliation"](https
: //bridges.monash.edu/articles/thesis/Probabilistic_Forecast_Reconciliation_Theory_and_Applications/11869533)
"Probabilistic forecast reconciliation
: Properties, evaluation and score optimisation”. European Journal of Operational Research.](https://www.sciencedirect.com/science/article/pii/S0377221722006087)__init__
get_prediction_levels
get_prediction_quantiles
get_samples
num_samples
: int, number of samples generated from coherent distribution.samples
: Coherent samples of size (base
, horizon
, num_samples
).PERMBU
S
: np.array, summing matrix of size (base
, bottom
).tags
: Each key is a level and each value its S
indices.y_insample
: Insample values of size (base
, insample_size
).y_hat_insample
: Insample point forecasts of size (base
, insample_size
).sigmah
: np.array, forecast standard dev. of size (base
, horizon
).num_samples
: int, number of normal prediction samples generated.seed
: int, random seed for numpy generator’s replicability.International conference on machine learning ICML.](https
: //proceedings.mlr.press/v70/taieb17a.html)__init__
get_prediction_levels
get_prediction_quantiles
get_samples
num_samples
: int, number of samples generated from coherent distribution.samples
: Coherent samples of size (base
, horizon
, num_samples
).