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ReconcilerForecaster

ReconcilerForecaster

class ReconcilerForecaster(forecaster, method='mint_shrink', return_totals=True, alpha=0)[source]

Hierarchical reconciliation forecaster.

Reconciliation is applied to make the forecasts in a hierarchy of time-series sum together appropriately.

The base forecasts are first generated for each member separately in the hierarchy using any forecaster. The base forecasts are then reonciled so that they sum together appropriately. This reconciliation step can often improve the skill of the forecasts in the hierarchy.

Please refer to [1] for further information.

Parameters:
forecasterestimator

Estimator to generate base forecasts which are then reconciled

method{“mint_cov”, “mint_shrink”, “ols”, “wls_var”, “wls_str”, “bu”, “td_fcst”}, default=”mint_shrink”

The reconciliation approach applied to the forecasts based on:

  • "mint_cov" - sample covariance

  • "mint_shrink" - covariance with shrinkage

  • "ols" - ordinary least squares

  • "wls_var" - weighted least squares (variance)

  • "wls_str" - weighted least squares (structural)

  • "bu" - bottom-up

  • "td_fcst" - top down based on forecast proportions

return_totalsbool

Whether the predictions returned by predict and predict-like methods should include the total values in the hierarchy, stored at the __total index levels.

  • If True, prediction data frames include total values at __total levels

  • If False, prediction data frames are returned without __total levels

alpha: float default=0

Optional regularization parameter to avoid singular covariance matrix

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.

See also

Aggregator
Reconciler

References

Examples

>>> from sktime.forecasting.naive import NaiveForecaster
>>> from sktime.forecasting.reconcile import ReconcilerForecaster
>>> from sktime.transformations.hierarchical.aggregate import Aggregator
>>> from sktime.utils._testing.hierarchical import _bottom_hier_datagen
>>> agg = Aggregator()
>>> y = _bottom_hier_datagen(
...     no_bottom_nodes=3,
...     no_levels=1,
...     random_seed=123,
...     length=7,
... )
>>> y = agg.fit_transform(y)
>>> forecaster = NaiveForecaster(strategy="drift")
>>> reconciler = ReconcilerForecaster(forecaster, method="mint_shrink")
>>> reconciler.fit(y)
ReconcilerForecaster(...)
>>> prds_recon = reconciler.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()

Return testing parameter settings for the estimator.

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.