HierarchicalProphet

class HierarchicalProphet(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)[source]

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:

trendUnion[str, BaseEffect], optional, default=”linear”

Type of trend to use. Can also be a custom effect object.

changepoint_intervalint, optional, default=25

Number of potential changepoints to sample in the history.

changepoint_rangeUnion[float, int], optional, default=0.8

Proportion of the history in which trend changepoints will be estimated.

If float, must be between 0 and 1 (inclusive). The range will be that proportion of the training history.
If int, can be positive or negative. Absolute value must be less than the number of training points. The range will be that number of points. A negative int indicates the number of points counting from the end of the history, a positive int from the beginning.

changepoint_prior_scalefloat, optional, default=0.001

Regularization parameter controlling the flexibility of the automatic changepoint selection.

offset_prior_scalefloat, optional, default=0.1

Scale parameter for the prior distribution of the offset. The offset is the constant term in the piecewise trend equation.

capacity_prior_scalefloat, optional, default=0.2

Scale parameter for the prior distribution of the capacity.

capacity_prior_locfloat, optional, default=1.1

Location parameter for the prior distribution of the capacity.

feature_transformerBaseTransformer or None, optional, default=None

A transformer to preprocess the exogenous features.

exogenous_effectslist of AbstractEffect or None, optional, default=None

A list defining the exogenous effects to be used in the model.

default_effectAbstractEffect or None, optional, default=None

The default effect to be used when no effect is specified for a variable.

shared_featureslist, optional, default=[]

List of features shared across all series in the hierarchy.

mcmc_samplesint, optional, default=2000

Number of MCMC samples to draw.

mcmc_warmupint, optional, default=200

Number of warmup steps for MCMC.

mcmc_chainsint, optional, default=4

Number of MCMC chains.

inference_methodstr, optional, default=’map’

Inference method to use. Either “map” or “mcmc”.

optimizer_namestr, optional, default=’Adam’

Name of the optimizer to use.

optimizer_kwargsdict or None, optional, default={‘step_size’: 1e-4}

Additional keyword arguments for the optimizer.

optimizer_stepsint, optional, default=100_000

Number of optimization steps.

noise_scalefloat, optional, default=0.05

Scale parameter for the noise.

correlation_matrix_concentrationfloat, optional, default=1.0

Concentration parameter for the correlation matrix.

rng_keyjax.random.PRNGKey or None, optional, default=None

Random number generator key.

Attributes:

cutoff: Cut-off = “present time” state of forecaster.
fh: Forecasting horizon that was passed.
is_fitted: Whether fit has been called.
state: State of the estimator.

Examples

>>> from sktime.forecasting.naive import NaiveForecaster
>>> from sktime.transformations.hierarchical.aggregate import Aggregator
>>> from sktime.utils._testing.hierarchical import _bottom_hier_datagen
>>> from sktime.forecasting.prophetverse 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.fit(y)
>>> forecaster.predict(fh=[1])

Methods

`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(y[, X, fh])	Fit forecaster to training data.
`fit_predict`(y[, X, fh, X_pred])	Fit and forecast time series at future horizon.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_pretrained_params`([deep])	Get pretrained parameters of this estimator.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the skbase object.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`predict`([fh, X])	Forecast time series at future horizon.
`predict_interval`([fh, X, coverage])	Compute/return prediction interval forecasts.
`predict_proba`([fh, X, marginal])	Compute/return fully probabilistic forecasts.
`predict_quantiles`([fh, X, alpha])	Compute/return quantile forecasts.
`predict_residuals`([y, X])	Return residuals of time series forecasts.
`predict_var`([fh, X, cov])	Compute/return variance forecasts.
`pretrain`(y[, X, fh])	Pre-train forecaster on panel (global) data.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`score`(y[, X, fh])	Scores forecast against ground truth, using MAPE (non-symmetric).
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.
`update`(y[, X, update_params])	Update cutoff value and, optionally, fitted parameters.
`update_predict`(y[, cv, X, update_params, ...])	Make predictions and update model iteratively over the test set.
`update_predict_single`([y, fh, X, update_params])	Update model with new data and make forecasts.