Reconciler

class Reconciler(method='bu')[source]

Hierarchical reconciliation transformer.

Hierarchical reconciliation is a transformation which is used to make the predictions in a hierarchy of time-series sum together appropriately.

The methods implemented in this class only require the structure of the hierarchy or the forecasts values for reconciliation.

These functions are intended for transforming hierarchical forecasts, i.e. after prediction. However they are general and can be used to transform hierarchical time-series data.

For reconciliation methods that require historical values in addition to the forecasts, such as MinT, see the ReconcilerForecaster class.

For more versatile and efficient reconciliation in pipelines, see BottomUpReconciler, TopdownReconciler, OptimalReconciler, NonNegativeOptimalReconciler, MiddleOutReconciler, that apply reconciliation as preprocessing and postprocessing steps.

For further information on the methods, see [1].

Parameters:

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

The reconciliation approach applied to the forecasts

"bu" - bottom-up
"ols" - ordinary least squares
"wls_str" - weighted least squares (structural)
"td_fcst" - top down based on (forecast) proportions

Attributes:

is_fitted: Whether fit has been called.

References

[1]

https://otexts.com/fpp3/hierarchical.html

Examples

>>> from sktime.forecasting.trend import PolynomialTrendForecaster
>>> from sktime.transformations.hierarchical.reconcile import Reconciler
>>> 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,
... )
>>> y = agg.fit_transform(y)
>>> forecaster = PolynomialTrendForecaster()
>>> forecaster.fit(y)
PolynomialTrendForecaster(...)
>>> prds = forecaster.predict(fh=[1])
>>> # reconcile forecasts
>>> reconciler = Reconciler(method="ols")
>>> prds_recon = reconciler.fit_transform(prds)

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`()	Return testing parameter settings for the estimator.
`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.