ForecastByLevel

class ForecastByLevel(forecaster, groupby='local')[source]

Forecast by instance or panel.

Used to apply multiple copies of forecaster by instance or by panel.

If groupby="global", behaves like forecaster. If groupby="local", fits a clone of forecaster per time series instance. If groupby="panel", fits a clone of forecaster per panel (first non-time level).

The fitted forecasters can be accessed in the forecasters_ attribute, if more than one clone is fitted, otherwise in the forecaster_ attribute.

Parameters:

forecastersktime forecaster used in ForecastByLevel: A “blueprint” forecaster, state does not change when fit is called.
groupbystr, one of [“local”, “global”, “panel”], optional, default=”local”: level on which data are grouped to fit clones of forecaster “local” = unit/instance level, one reduced model per lowest hierarchy level “global” = top level, one reduced model overall, on pooled data ignoring levels “panel” = second lowest level, one reduced model per panel level (-2) if there are 2 or less levels, “global” and “panel” result in the same if there is only 1 level (single time series), all three settings agree

Attributes:

forecaster_sktime forecaster, present only if groupby is “global”: clone of forecaster used for fitting and forecasting
forecasters_pd.DataFrame of sktime forecaster, present otherwise: entries are clones of forecaster used for fitting and forecasting

Examples

>>> from sktime.forecasting.naive import NaiveForecaster
>>> from sktime.forecasting.compose import ForecastByLevel
>>> from sktime.utils._testing.hierarchical import _make_hierarchical
>>> y = _make_hierarchical()
>>> f = ForecastByLevel(NaiveForecaster(), groupby="local")
>>> f.fit(y)
ForecastByLevel(...)
>>> fitted_forecasters = f.forecasters_
>>> fitted_forecasters_alt = f.get_fitted_params()["forecasters"]

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 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.