SETARTree

class SETARTree(lag=10, max_depth=10, stopping_criteria='both', significance=0.05, significance_divider=2, error_threshold=0.03, n_thresholds=15, scale=False, seq_significance=True, fixed_lag=False, external_lag=0, feature_subset=None)

Bases: BaseForecaster, IterativeForecastingMixin

SETAR-Tree: A tree algorithm for global time series forecasting.

The SETAR-Tree forecaster is a global time series model trained across collections of time series, enabling it to learn cross-series patterns. This implementation is based on the codebase associated with the work by Godahewa et al. [1].

Parameters:

lagint, default=10

The number of past lags to use as features for forecasting.

max_depthint, default=10

The maximum depth of the tree.

stopping_criteria{“lin_test”, “error_imp”, “both”}, default=”both”

The criteria to use for stopping tree growth: - “lin_test”: Uses a statistical F-test for linearity. - “error_imp”: Uses a minimum error reduction threshold. - “both”: Uses both linearity test and error improvement.

significancefloat, default=0.05

The initial significance level (alpha) for the linearity F-test.

significance_dividerint, default=2

The factor by which to divide the significance level at each tree depth.

error_thresholdfloat, default=0.03

The minimum percentage of error reduction required to make a split.

n_thresholdsint, default=15

The maximum number of candidate thresholds when trying to find the best split point for a single lag.

scalebool, default=False

Whether to scale the time series by their mean before processing.

seq_significancebool, default=True

Whether to decrease significance level with tree depth.

fixed_lagbool, default=False

Whether to use a fixed lag (e.g., Lag{external_lag}) for splitting.

external_lagint, default=0

The specific lag to use if fixed_lag is True.

feature_subsetlist[int] or None, default=None

Optional, allowed feature (lag) indices used by the SETARForest.

Notes

Capabilities

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

References

[1]

Godahewa, Rakshitha, et al. “SETAR-Tree: a novel and accurate

tree algorithm for global time series forecasting.” Machine Learning 112.7 (2023): 2555-2591.

Methods

clone([random_state])	Obtain a clone of the object with the same hyperparameters.
fit(y[, exog, axis])	Fit forecaster to series y.
fit_from_embedded(embedded[, feature_indices])	Public hook used by SETARForest: train from a pre-bagged embedded matrix.
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 using a single model fit on y.
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 the parameters of this estimator.
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.

fit_from_embedded(embedded: ndarray, feature_indices: list[int] | None = None)

Public hook used by SETARForest: train from a pre-bagged embedded matrix.

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.

Parameters:

deepbool, default=True

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

Returns:

paramsdict

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) → ndarray

Forecast prediction_horizon steps using a single model fit on y.

This function implements the iterative forecasting strategy (also called recursive or iterated). This involves a single model fit on y which is then used to make prediction_horizon ahead forecasts using its own predictions as inputs for future forecasts. This is done by taking the prediction at step i and feeding it back into the model to help predict for step i+1. The basic contract of iterative_forecast is that fit is only ever called once.

ynp.ndarray

The time series to make forecasts about. Must be of shape (n_channels, n_timepoints) if a multivariate time series.

prediction_horizonint

The number of future time steps to forecast.

exognp.ndarray or None, default=None

Target-time exogenous training data aligned with y. If provided, future_exog must also be provided.

future_exognp.ndarray or None, default=None

Target-time future exogenous data aligned with the forecast horizon. If provided, exog must also be provided. These values are passed one row at a time to predict and are not concatenated onto exog.

Returns:

np.ndarray

An array of shape (prediction_horizon,) containing the forecasts for each horizon.

Raises:

ValueError

If prediction_horizon is less than 1.

ValueError

If only one of exog and future_exog is provided.

ValueError

If exog is not aligned with y.

ValueError

If future_exog is not aligned with prediction_horizon.

Examples

>>> from aeon.forecasting import RegressionForecaster
>>> y = np.array([1.0, 2.0, 3.0, 4.0, 3.0, 2.0, 1.0, 2.0, 3.0, 4.0])
>>> f = RegressionForecaster(window=3)
>>> f.iterative_forecast(y, 2)
array([3., 2.])

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 = '$UNCHANGED$') → SETARTree

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 the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

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

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.