ConditionalDeseasonalizer

class ConditionalDeseasonalizer(seasonality_test=None, sp=1, model='additive')[source]

Remove seasonal components from time series, conditional on seasonality test.

Fit tests for seasonality and if the passed time series has a seasonal component it applies seasonal decomposition provided by statsmodels <https://www.statsmodels.org> to compute the seasonal component. If the test is negative _seasonal is set to all ones (if model is “multiplicative”) or to all zeros (if model is “additive”).

Transform aligns seasonal components stored in seasonal_ with the time index of the passed series and then subtracts them (“additive” model) from the passed series or divides the passed series by them (“multiplicative” model).

Parameters:

seasonality_testcallable or None, default=None: Callable that tests for seasonality and returns True when data is seasonal and False otherwise. If None, 90% autocorrelation seasonality test is used.
spint, default=1: Seasonal periodicity.
model{“additive”, “multiplicative”}, default=”additive”: Model to use for estimating seasonal component.

Attributes:

seasonal_array of length sp: Seasonal components.
is_seasonal_bool: Return value of seasonality_test. True when data is seasonal and False otherwise.

Notes

For further explanation on seasonal components and additive vs. multiplicative models see Forecasting: Principles and Practice. Seasonal decomposition is computed using `statsmodels

<https://www.statsmodels.org/stable/generated/statsmodels.tsa.seasonal.seasonal_decompose.html>`_.

Examples

>>> from sktime.transformations.detrend import ConditionalDeseasonalizer
>>> from sktime.datasets import load_airline
>>> y = load_airline()
>>> transformer = ConditionalDeseasonalizer(sp=12)
>>> 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.