StationarityKPSS

class StationarityKPSS(p_threshold=0.05, regression='c', nlags='auto')[source]

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

Uses statsmodels.tsa.stattools.kpss as a test for trend-stationarity, and derives a boolean statement whether a series is (trend-)stationary.

Also returns test results for the trend-stationarity test as fitted parameters.

Parameters:

p_thresholdfloat, optional, default=0.05

significance threshold to apply in testing for stationarity

regressionstr, one of {“c”,”ct”,”ctt”,”n”}, optional, default=”c”

Constant and trend order to include in regression.

“c” : constant only (default).
“ct” : constant and trend.

nlagsstr or int, optional, default=”auto”. If int, must be positive.

Indicates the number of lags to be used internally in kpss. If “auto”, lags is calculated using the data-dependent method of Hobijn et al (1998). See also Andrews (1991), Newey & West (1994), and Schwert (1989). If “legacy”, uses int(12 * (n / 100)**(1 / 4)) , as outlined in Schwert (1989). If int, uses that exact number.

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: The p-value of the KPSS test, of running kpss on y in fit. The p-value is interpolated from Table 1 in Kwiatkowski et al. (1992), and a boundary point is returned if the test statistic is outside the table of critical values, that is, if the p-value is outside the interval (0.01, 0.1).
lags_int: The truncation lag parameter.

Examples

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

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.