HierarchyEnsembleForecaster

class HierarchyEnsembleForecaster(forecasters, by='level', default=None, backend=None, backend_params=None)[source]

Aggregates hierarchical data, fit forecasters and make predictions.

Can apply different univariate forecaster either on different level of aggregation or on different hierarchical nodes.

HierarchyEnsembleForecaster is passed forecaster/level or forecaster/node pairs. Level can only be int >= 0 with 0 signifying the topmost level of aggregation. Node can only be a tuple of strings or list of tuples.

Behaviour in fit, predict: For level pairs f_i, l_i passed, applies forecaster f_i to level l_i. For node pairs f_i, n_i passed, applies forecaster f_i on each node of n_i. If default argument is passed, applies default forecaster on the remaining levels/nodes which are not mentioned in argument forecasters. predict results are concatenated to one container with same columns as in fit.

Parameters:

forecasterssktime forecaster, or list of tuples

(str, estimator, int or list of tuple/s) if forecaster, clones of forecaster are applied to all aggregated levels. if list of tuples, with name = str, estimator is forecaster, level/node as int/tuples respectively. all levels/nodes must be present in forecasters attribute if default attribute is None

by{‘node’, ‘level’, default=’level’}

if 'level', applies a univariate forecaster on all the hierarchical nodes within a level of aggregation if 'node', applies separate univariate forecaster for each hierarchical node.

defaultsktime forecaster {default = None}

if passed, applies default forecaster on the nodes/levels not mentioned in the forecaster argument.

backendstring, by default “None”.

Parallelization backend to use for runs. Runs parallel evaluate if specified and strategy="refit".

“None”: executes loop sequentially, simple list comprehension
“loky”, “multiprocessing” and “threading”: uses joblib.Parallel loops
“joblib”: custom and 3rd party joblib backends, e.g., spark
“dask”: uses dask, requires dask package in environment
“dask_lazy”: same as “dask”, but changes the return to (lazy) dask.dataframe.DataFrame.
“ray”: uses ray, requires ray package in environment

Recommendation: Use “dask” or “loky” for parallel evaluate. “threading” is unlikely to see speed ups due to the GIL and the serialization backend (cloudpickle) for “dask” and “loky” is generally more robust than the standard pickle library used in “multiprocessing”.

backend_paramsdict, optional

additional parameters passed to the backend as config. Directly passed to utils.parallel.parallelize. Valid keys depend on the value of backend:

“None”: no additional parameters, backend_params is ignored
“loky”, “multiprocessing” and “threading”: default joblib backends any valid keys for joblib.Parallel can be passed here, e.g., n_jobs, with the exception of backend which is directly controlled by backend. If n_jobs is not passed, it will default to -1, other parameters will default to joblib defaults.
“joblib”: custom and 3rd party joblib backends, e.g., spark. any valid keys for joblib.Parallel can be passed here, e.g., n_jobs, backend must be passed as a key of backend_params in this case. If n_jobs is not passed, it will default to -1, other parameters will default to joblib defaults.
“dask”: any valid keys for dask.compute can be passed, e.g., scheduler
“ray”: The following keys can be passed:
- “ray_remote_args”: dictionary of valid keys for ray.init
- “shutdown_ray”: bool, default=True; False prevents ray from shutting
  down after parallelization.
- “logger_name”: str, default=”ray”; name of the logger to use.
- “mute_warnings”: bool, default=False; if True, suppresses warnings

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.compose import HierarchyEnsembleForecaster
>>> from sktime.forecasting.naive import NaiveForecaster
>>> from sktime.forecasting.trend import PolynomialTrendForecaster, TrendForecaster
>>> from sktime.utils._testing.hierarchical import _bottom_hier_datagen
>>> y = _bottom_hier_datagen(
...     no_bottom_nodes=7,
...     no_levels=2,
...     random_seed=123,
... )

>>> # Example of by = 'level'
>>> forecasters = [
...     ('naive', NaiveForecaster(), 0),
...     ('trend', TrendForecaster(), 1),
... ]
>>> forecaster = HierarchyEnsembleForecaster(
...     forecasters=forecasters,
...     by='level',
...     default=PolynomialTrendForecaster(degree=2),
... )
>>> forecaster.fit(y, fh=[1, 2, 3])
HierarchyEnsembleForecaster(...)
>>> y_pred = forecaster.predict()

>>> # Example of by = 'node'
>>> forecasters = [
...     ('trend', TrendForecaster(), [("__total", "__total")]),
...     ('poly', PolynomialTrendForecaster(degree=2), [('l2_node01', 'l1_node01')]),
... ]
>>> forecaster = HierarchyEnsembleForecaster(
...                 forecasters=forecasters,
...                 by='node', default=NaiveForecaster()
... )
>>> forecaster.fit(y, fh=[1, 2, 3])
HierarchyEnsembleForecaster(...)
>>> y_pred = forecaster.predict()

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 parameters of estimator.
`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 composite.
`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`(**kwargs)	Set the parameters of estimator.
`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.