ThetaLinesTransformer

class ThetaLinesTransformer(theta=(0, 2))[source]

Decompose the original data into two or more Theta-lines.

Implementation of decomposition for Theta-method [1] as described in [2].

Overview: Input univariate series of length “n” and ThetaLinesTransformer modifies the local curvature of the time series using Theta-coefficient values passed through the parameter theta.

Each Theta-coefficient is applied directly to the second differences of the input series. The resulting transformed series (Theta-lines) are returned as a pd.DataFrame of shape len(input series) * len(theta).

Parameters:

thetasequence of float, default=(0,2): Theta-coefficients to use in transformation.

Attributes:

is_fitted: Whether fit has been called.

Notes

Depending on the value of the Theta-coefficient, Theta-lines either augment the long-term trend (0 < Theta < 1) or the the short-term behaviour (Theta > 1).

Special cases:

Theta == 0 : deflates input data to linear trend
Theta == 1 : returns data unchanged
Theta < 0 : transforms time series and mirrors it along the linear trend.

References

[1]

V.Assimakopoulos et al., “The theta model: a decomposition approach to forecasting”, International Journal of Forecasting, vol. 16, pp. 521-530, 2000.

[2]

E.Spiliotis et al., “Generalizing the Theta method for automatic forecasting “, European Journal of Operational Research, vol. 284, pp. 550-558, 2020.

Examples

>>> from sktime.transformations.theta import ThetaLinesTransformer
>>> from sktime.datasets import load_airline
>>> y = load_airline()
>>> transformer = ThetaLinesTransformer([0, 0.25, 0.5, 0.75])
>>> y_thetas = transformer.fit_transform(y)

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`(X[, y])	Fit transformer to X, optionally to y.
`fit_transform`(X[, y])	Fit to data, then transform it.
`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_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.
`inverse_transform`(X[, y])	Inverse transform X and return an inverse transformed version.
`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.
`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.
`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.
`transform`(X[, y])	Transform X and return a transformed version.
`update`(X[, y, update_params])	Update transformer with X, optionally y.