HierarchicalProphet

sktime.HierarchicalProphet(
    self,
    trend='linear',
    feature_transformer=None,
    exogenous_effects=None,
    default_effect=None,
    shared_features=None,
    noise_scale=0.05,
    correlation_matrix_concentration=1.0,
    rng_key=None,
    inference_engine=None,
    likelihood=None,
)

A Bayesian hierarchical time series forecasting model based on Meta’s Prophet.

This method forecasts all bottom series in a hierarchy at once, using a MultivariateNormal as the likelihood function and LKJ priors for the correlation matrix.

This forecaster is particularly interesting if you want to fit shared coefficients across series. In that case, shared_features parameter should be a list of feature names that should have that behaviour.

Parameters

Name	Type	Description	Default
trend	Union[BaseEffect, str]	Trend component of the model.	`"linear"`
feature_transformer	BaseTransformer	Transformer for features preprocessing.	`None`
exogenous_effects	optional	Effects to model exogenous variables.	`None`
default_effect	optional	Default effect specification.	`None`
shared_features	optional	Features shared across time series.	`None`
noise_scale	float	Scale parameter for the noise distribution.	`0.05`
correlation_matrix_concentration	float	Concentration parameter for the correlation matrix.	`1.0`
rng_key	optional	Random number generator key.	`None`
inference_engine	optional	Engine used for inference.	`None`

Examples

>>> from sktime.forecasting.naive import NaiveForecaster
>>> from sktime.transformations.hierarchical.aggregate import Aggregator
>>> from sktime.utils._testing.hierarchical import _bottom_hier_datagen
>>> from prophetverse.sktime.multivariate import HierarchicalProphet
>>> agg = Aggregator()
>>> y = _bottom_hier_datagen(
...     no_bottom_nodes=3,
...     no_levels=1,
...     random_seed=123,
...     length=7,
... )
>>> y = agg.fit_transform(y)
>>> forecaster = HierarchicalProphet()
>>> forecaster = forecaster.fit(y)
>>> y_pred = forecaster.predict(fh=[1])

Attributes

Name	Description
n_series	Get the number of series.

Methods

Name	Description
get_test_params	Params to be used in sktime unit tests.
predict_samples	Generate samples for the given exogenous variables and forecasting horizon.

get_test_params

sktime.HierarchicalProphet.get_test_params(parameter_set='default')

Params to be used in sktime unit tests.

Parameters

Name	Type	Description	Default
parameter_set	str	The parameter set to be used (ignored in this implementation)	`'default'`

Returns

Name	Type	Description
	List[dict[str, int]]	A list of dictionaries containing the test parameters.

predict_samples

sktime.HierarchicalProphet.predict_samples(fh, X=None)

Generate samples for the given exogenous variables and forecasting horizon.

Parameters

Name	Type	Description	Default
X	pd.DataFrame	Exogenous variables.	`None`
fh	ForecastingHorizon	Forecasting horizon.	required

Returns

Name	Type	Description
	np.ndarray	Predicted samples.