StationarityKPSSArch

class StationarityKPSSArch(lags=None, trend='c', p_threshold=0.05)[source]

Test for stationarity via the Kwiatkowski-Phillips-Schmidt-Shin Unit Root Test.

Direct interface to KPSS test from the arch package. Does not assume ARCH process, naming is due to the use of the arch package.

Uses arch.unitroot.KPSS as a test for trend-stationarity, and derives a boolean statement whether a series is (trend-)stationary.

Also returns test results for the unit root test as fitted parameters.

Parameters:

lagsint, optional

The number of lags to use in the Newey-West estimator of the long-run covariance. If omitted or None, the number of lags is calculated with the data-dependent method of Hobijn et al. (1998). See also Andrews (1991), Newey & West (1994), and Schwert (1989). Set lags=-1 to use the old method that only depends on the sample size, 12 * (nobs/100) ** (1/4).

trend{“c”, “ct”}, optional

The trend component to include in the ADF test: “c” - Include a constant (Default) “ct” - Include a constant and linear time trend

Attributes:

stationary_bool: whether the series in fit is stationary according to the test more precisely, whether the null of the KPSS test is accepted at p_threshold
test_statistic_float: The KPSS test statistic, of running KPSS on y in fit
pvalue_floatfloat: p-value obtained when running KPSS on y in fit
usedlag_int: The number of lags used in the test.

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.param_est.stationarity import StationarityKPSSArch
>>>
>>> X = load_airline()
>>> sty_est = StationarityKPSSArch()
>>> sty_est.fit(X)
StationarityKPSSArch(...)
>>> sty_est.get_fitted_params()["stationary"]
True

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 estimator and estimate parameters.
`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.
`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.
`update`(X[, y])	Update fitted parameters on more data.