EnsembleForecaster

class EnsembleForecaster(forecasters, weights=None, averaging_method='mean', iterative_strategy='component')

Bases: ComposableEstimatorMixin, BaseForecaster, IterativeForecastingMixin

Ensemble forecaster that combines predictions from multiple forecasters.

Fits each component forecaster independently on the same series and combines their point forecasts using a specified averaging method. Equal weights and the mean are used by default; the median is recommended when robustness to a single poorly-fitted component is desired (as in SCUM).

Parameters:

forecasterslist of BaseForecaster or list of (str, BaseForecaster) tuples

Forecaster instances to include in the ensemble. Each element can be a forecaster instance or a (name, estimator) pair. Names must be unique when supplied.

weightsarray-like of float or None, default=None

Per-forecaster weights used when averaging_method="mean". Must be non-negative and have the same length as forecasters. Weights are normalised to sum to one before use. Ignored when averaging_method="median" or averaging_method is callable. If None, equal weights are used.

averaging_method{“mean”, “median”} or callable, default=”mean”

How to average the component forecasts at each horizon: - "mean" : (weighted) arithmetic mean. - "median" : element-wise median; weights is ignored. - callable : receives an array of shape

(n_forecasters, prediction_horizon) (or (n_forecasters,) for a single-step prediction) and must return a scalar or array of shape (prediction_horizon,) respectively.

iterative_strategy{“component”, “ensemble”}, default=”component”

Strategy used by iterative_forecast. If "component", each component forecaster is iterated forward independently using its own forecasts and the resulting forecast paths are aggregated at each horizon. If "ensemble", the ensemble prediction at each step is appended to a shared history and passed to each fitted component’s predict method. The default "component" strategy is the safer general-purpose choice for stateful forecasters, because it delegates multi-step recursion to each component.

Attributes:

forecasters_list of (str, BaseForecaster) tuples

Fitted copies of the component forecasters.

weights_np.ndarray or None

Normalised weights used for combination, or None when averaging_method is not "mean" or when weights was not supplied.

n_forecasters_int

Number of component forecasters.

See also

aeon.forecasting.stats.SCUM

Simple Combination of Univariate Models forecaster.

Notes

Capabilities

Missing Values	No
Multithreading	No
Univariate	Yes
Multivariate	No
Horizon	No
Exogenous	No

References

[1]

Petropoulos, F. and Svetunkov, I. (2020). A simple combination of univariate models. International Journal of Forecasting, 36(1), 110-115.

Examples

>>> import numpy as np
>>> from aeon.forecasting import NaiveForecaster
>>> from aeon.forecasting.ensembles import EnsembleForecaster
>>> y = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
>>> forecasters = [
...     ("last", NaiveForecaster(strategy="last")),
...     ("mean", NaiveForecaster(strategy="mean")),
... ]
>>> ens = EnsembleForecaster(forecasters=forecasters, averaging_method="mean")
>>> preds = ens.iterative_forecast(y, prediction_horizon=3)
>>> preds.shape
(3,)

Methods

clone([random_state])	Obtain a clone of the object with the same hyperparameters.
fit(y[, exog, axis])	Fit forecaster to series y.
forecast(y[, exog, axis])	Forecast the next horizon steps ahead of y.
get_class_tag(tag_name[, raise_error, ...])	Get tag value from estimator class (only class tags).
get_class_tags()	Get class tags from estimator class and all its parent classes.
get_fitted_params([deep])	Get fitted parameters.
get_params([deep])	Get parameters for this estimator.
get_tag(tag_name[, raise_error, ...])	Get tag value from estimator class.
get_tags()	Get tags from estimator.
iterative_forecast(y, prediction_horizon[, ...])	Forecast prediction_horizon steps ahead by combining component forecasts.
predict(y[, exog, axis])	Predict the next horizon steps ahead.
reset([keep])	Reset the object to a clean post-init state.
set_fit_request(*[, axis, exog])	Configure whether metadata should be requested to be passed to the fit method.
set_params(**params)	Set estimator parameters, including nested forecaster parameters.
set_predict_request(*[, axis, exog])	Configure whether metadata should be requested to be passed to the predict method.
set_tags(**tag_dict)	Set dynamic tags to given values.

clone(random_state=None)

Obtain a clone of the object with the same hyperparameters.

A clone is a different object without shared references, in post-init state. This function is equivalent to returning sklearn.clone of self. Equal in value to type(self)(**self.get_params(deep=False)).

Parameters:

random_stateint, RandomState instance, or None, default=None

Sets the random state of the clone. If None, the random state is not set. If int, random_state is the seed used by the random number generator. If RandomState instance, random_state is the random number generator.

Returns:

estimatorobject

Instance of type(self), clone of self (see above)

fit(y, exog=None, axis=1)

Fit forecaster to series y.

Fit a forecaster to predict self.horizon steps ahead using y.

Parameters:

ynp.ndarray

A time series on which to learn a forecaster to predict horizon ahead.

exognp.ndarray, default =None

Optional target-time exogenous values aligned with y.

Returns:

self

Fitted BaseForecaster.

forecast(y, exog=None, axis=1) → float

Forecast the next horizon steps ahead of y.

By default this is simply fit followed by predict.

Parameters:

ynp.ndarray

A time series to predict the next horizon value for. Must be of shape (n_channels, n_timepoints) if a multivariate time series.

exognp.ndarray, default =None

Optional target-time exogenous values aligned with y.

Returns:

float

single prediction self.horizon steps ahead of y.

classmethod get_class_tag(tag_name, raise_error=True, tag_value_default=None)

Get tag value from estimator class (only class tags).

Parameters:

tag_namestr

Name of tag value.

raise_errorbool, default=True

Whether a ValueError is raised when the tag is not found.

tag_value_defaultany type, default=None

Default/fallback value if tag is not found and error is not raised.

Returns:

tag_value

Value of the tag_name tag in cls. If not found, returns an error if raise_error is True, otherwise it returns tag_value_default.

Raises:

ValueError

if raise_error is True and tag_name is not in self.get_tags().keys()

Examples

>>> from aeon.classification import DummyClassifier
>>> DummyClassifier.get_class_tag("capability:multivariate")
True

classmethod get_class_tags()

Get class tags from estimator class and all its parent classes.

Returns:

collected_tagsdict

Dictionary of tag name and tag value pairs. Collected from _tags class attribute via nested inheritance. These are not overridden by dynamic tags set by set_tags or class __init__ calls.

get_fitted_params(deep=True)

Get fitted parameters.

State required:

Requires state to be “fitted”.

Parameters:

deepbool, default=True

If True, will return the fitted parameters for this estimator and contained subobjects that are estimators.

Returns:

fitted_paramsdict

Fitted parameter names mapped to their values.

get_params(deep=True)

Get parameters for this estimator.

Returns the parameters given in the constructor as well as the estimators contained within the composable estimator if deep.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsmapping of string to any

Parameter names mapped to their values.

get_tag(tag_name, raise_error=True, tag_value_default=None)

Get tag value from estimator class.

Includes dynamic and overridden tags.

Parameters:

tag_namestr

Name of tag to be retrieved.

raise_errorbool, default=True

Whether a ValueError is raised when the tag is not found.

tag_value_defaultany type, default=None

Default/fallback value if tag is not found and error is not raised.

Returns:

tag_value

Value of the tag_name tag in self. If not found, returns an error if raise_error is True, otherwise it returns tag_value_default.

Raises:

ValueError

if raise_error is True and tag_name is not in self.get_tags().keys()

Examples

>>> from aeon.classification import DummyClassifier
>>> d = DummyClassifier()
>>> d.get_tag("capability:multivariate")
True

get_tags()

Get tags from estimator.

Includes dynamic and overridden tags.

Returns:

collected_tagsdict

Dictionary of tag name and tag value pairs. Collected from _tags class attribute via nested inheritance and then any overridden and new tags from __init__ or set_tags.

iterative_forecast(y, prediction_horizon, exog=None, *, future_exog=None)

Forecast prediction_horizon steps ahead by combining component forecasts.

The iterative_strategy parameter controls the recursive semantics. With "component", each component forecaster produces its own full forecast path, then paths are aggregated horizon by horizon. With "ensemble", the ensemble produces one aggregated prediction at a time and appends that prediction to a shared history passed to predict. Components are fitted once on the original history; whether the extended context affects later steps depends on the component’s predict implementation.

Parameters:

ynp.ndarray

The in-sample series.

prediction_horizonint

Number of future steps to forecast.

exognp.ndarray or None, default=None

Exogenous series are not currently supported by EnsembleForecaster; non-None values are rejected during validation.

future_exognp.ndarray or None, default=None

Future exogenous values are not currently supported by EnsembleForecaster; non-None values are rejected during validation.

Returns:

np.ndarray

Shape (prediction_horizon,) combined forecast.

predict(y, exog=None, axis=1) → float

Predict the next horizon steps ahead.

Parameters:

ynp.ndarray

A time series to predict the next horizon value for.

exognp.ndarray, default =None

Optional exogenous values for the prediction target. For models fitted with exogenous variables, this should contain the exogenous values needed to make the next prediction.

Returns:

float

single prediction self.horizon steps ahead of y.

reset(keep=None)

Reset the object to a clean post-init state.

After a self.reset() call, self is equal or similar in value to type(self)(**self.get_params(deep=False)), assuming no other attributes were kept using keep.

Detailed behaviour:

removes any object attributes, except:

hyper-parameters (arguments of __init__) object attributes containing double-underscores, i.e., the string “__”

runs __init__ with current values of hyperparameters (result of get_params)

Not affected by the reset are:

object attributes containing double-underscores class and object methods, class attributes any attributes specified in the keep argument

Parameters:

keepNone, str, or list of str, default=None

If None, all attributes are removed except hyperparameters. If str, only the attribute with this name is kept. If list of str, only the attributes with these names are kept.

Returns:

selfobject

Reference to self.

Raises:

TypeError

If ‘keep’ is not a string or a list of strings.

set_fit_request(*, axis: bool | None | str = '$UNCHANGED$', exog: bool | None | str = '$UNCHANGED$') → EnsembleForecaster

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to fit.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

axisstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for axis parameter in fit.

exogstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for exog parameter in fit.

Returns:

selfobject

The updated object.

set_params(**params)

Set estimator parameters, including nested forecaster parameters.

set_predict_request(*, axis: bool | None | str = '$UNCHANGED$', exog: bool | None | str = '$UNCHANGED$') → EnsembleForecaster

Configure whether metadata should be requested to be passed to the predict method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to predict if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to predict.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

axisstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for axis parameter in predict.

exogstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for exog parameter in predict.

Returns:

selfobject

The updated object.

set_tags(**tag_dict)

Set dynamic tags to given values.

Parameters:

**tag_dictdict

Dictionary of tag name and tag value pairs.

Returns:

selfobject

Reference to self.