Detrender

class Detrender(forecaster=None, model='additive')[source]

Remove a trend from a series.

This transformer uses any forecaster and returns the in-sample residuals of the forecaster’s predicted values.

The Detrender works as follows: in “fit”, the forecaster is fit to the input data, i.e., forecaster.fit(y=X). in “transform”, returns forecast residuals of forecasts at the data index. That is, transform(X) returns X - forecaster.predict(fh=X.index) (additive) or X / forecaster.predict(fh=X.index) (multiplicative detrending). Depending on time indices, this can generate in-sample or out-of-sample residuals.

For example, to remove the linear trend of a time series:: forecaster = PolynomialTrendForecaster(degree=1) transformer = Detrender(forecaster=forecaster) yt = transformer.fit_transform(y_train)

The detrender can also be used in a pipeline for residual boosting, by first detrending and then fitting another forecaster on residuals.

Parameters:

forecastersktime forecaster, follows BaseForecaster, default = None.

The forecasting model to remove the trend with: (e.g. PolynomialTrendForecaster).

If forecaster is None, PolynomialTrendForecaster(degree=1) is used. Must be a forecaster to which fh can be passed in predict.

model{“additive”, “multiplicative”}, default=”additive”

If model="additive" the forecaster.transform subtracts the trend, i.e., transform(X) returns X - forecaster.predict(fh=X.index) If model="multiplicative" the forecaster.transform divides by the trend, i.e., transform(X) returns X / forecaster.predict(fh=X.index)

Attributes:

forecaster_Fitted forecaster: Forecaster that defines the trend in the series.

Examples

>>> from sktime.transformations.detrend import Detrender
>>> from sktime.forecasting.trend import PolynomialTrendForecaster
>>> from sktime.datasets import load_airline
>>> y = load_airline()
>>> transformer = Detrender(forecaster=PolynomialTrendForecaster(degree=1))
>>> y_hat = 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.