TopdownReconciler

TopdownReconciler

class TopdownReconciler(method='td_fcst')[source]

Apply Topdown hierarchical reconciliation.

Forecast proportions keep the original series during transform, and propagate the “proportions” of each forecast with respect to its total during inverse_transform.

Topdown share, on the other hand, transforms the series to share the forecast with respect to their parent, and then uses the total forecast to multiply the shares.

For more information, see “Single level approaches” in [1].

Parameters:

methodstr, default=”td_fcst”

The method to use for reconciliation. - td_fcst: Forecast Proportions. - td_share: Topdown Share.

Attributes:

is_fitted

Whether fit has been called.

References

[1]

Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.

Examples

>>> from sktime.transformations.hierarchical.reconcile import (
...     TopdownReconciler)
>>> from sktime.utils._testing.hierarchical import _make_hierarchical
>>> from sktime.forecasting.naive import NaiveForecaster
>>> y = _make_hierarchical()
>>> pipe = TopdownReconciler() * NaiveForecaster()
>>> pipe = pipe.fit(y)
>>> y_pred = pipe.predict(fh=[1,2,3])

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(X[, y])	Fit transformer to X, optionally to y.
fit_transform(X[, y])	Fit to data, then transform it.
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_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 test parameters.
inverse_transform(X[, y])	Inverse transform X and return an inverse transformed version.
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.
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.
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.
transform(X[, y])	Transform X and return a transformed version.
update(X[, y, update_params])	Update transformer with X, optionally y.