MultiplexForecaster

class MultiplexForecaster(forecasters: list, selected_forecaster=None)[source]

MultiplexForecaster for selecting among different models in Auto-ML pipelines.

MultiplexForecaster facilitates a framework for performing automated model selection process over different model classes. It should be used in conjunction with ForecastingGridSearchCV or similar tuners to build an Auto-ML pipeline for forecasters. MultiplexForecaster can be used with univariate and multivariate forecasters.

MultiplexForecaster is specified with a (named) list of forecasters and a selected_forecaster hyper-parameter, which is one of the forecaster names. The MultiplexForecaster then behaves precisely as the forecaster with name selected_forecaster, ignoring functionality in the other forecasters.

When used with ForecastingGridSearchCV, MultiplexForecaster provides an ability to tune across multiple estimators, i.e., to perform Auto-ML, by tuning the ,selected_forecaster, hyper-parameter. This combination will then select one of the passed forecasters via the tuning algorithm.

Parameters:

forecasterslist of sktime forecasters, or

list of tuples (str, estimator) of sktime forecasters MultiplexForecaster can switch (“multiplex”) between these forecasters. These are “blueprint” forecasters, states do not change when fit is called.

selected_forecaster: str or None, optional, Default=None.

Name of the forecaster to be selected from the list of forecasters.

If str, must be one of the forecaster names. If no names are provided, must coincide with auto-generated name strings. To inspect auto-generated name strings, call get_params.
If None, behaves as if the first forecaster in the list is selected. Selects the forecaster as which MultiplexForecaster behaves.

Attributes:

forecaster_sktime forecaster: clone of the selected forecaster used for fitting and forecasting.
_forecasterslist of (str, forecaster) tuples: Forecasters turned into name/est tuples.

Examples

>>> from sktime.forecasting.ets import AutoETS
>>> from sktime.forecasting.model_selection import ForecastingGridSearchCV
>>> from sktime.split import ExpandingWindowSplitter
>>> from sktime.forecasting.compose import MultiplexForecaster
>>> from sktime.forecasting.naive import NaiveForecaster
>>> from sktime.forecasting.theta import ThetaForecaster
>>> from sktime.forecasting.model_evaluation import evaluate
>>> from sktime.datasets import load_shampoo_sales
>>> y = load_shampoo_sales()
>>> forecaster = MultiplexForecaster(forecasters=[
...     ("ets", AutoETS()),
...     ("theta", ThetaForecaster()),
...     ("naive", NaiveForecaster())])
>>> cv = ExpandingWindowSplitter(step_length=12)
>>> gscv = ForecastingGridSearchCV(
...     cv=cv,
...     param_grid={"selected_forecaster":["ets", "theta", "naive"]},
...     forecaster=forecaster)
>>> gscv.fit(y)
ForecastingGridSearchCV(...)

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`([parameter_set])	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.