StationarityZivotAndrews

class StationarityZivotAndrews(lags=None, trend='c', trim=0.15, max_lags=None, method='aic', p_threshold=0.05)[source]

Test for stationarity via the Zivot-Andrews Unit Root Test.

Direct interface to ZivotAndrews test from the arch package.

Uses arch.unitroot.ZivotAndrews as a test for unit roots, and derives a boolean statement whether a series is stationary.

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

Parameters:

lagsint, optional

The number of lags to use in the ADF regression. If omitted or None, method is used to automatically select the lag length with no more than max_lags are included.

trend{“c”, “t”, “ct”}, optional

The trend component to include in the test

“c” - Include a constant (Default)
“t” - Include a linear time trend
“ct” - Include a constant and linear time trend

trimfloat

percentage of series at begin/end to exclude from break-period calculation in range [0, 0.333] (default=0.15)

max_lagsint, optional

The maximum number of lags to use when selecting lag length

method{“AIC”, “BIC”, “t-stat”}, optional

The method to use when selecting the lag length

“AIC” - Select the minimum of the Akaike IC
“BIC” - Select the minimum of the Schwarz/Bayesian IC
“t-stat” - Select the minimum of the Schwarz/Bayesian IC

Attributes:

stationary_bool, whether the series in fit has a unit root: (with structural break) more precisely, whether the null of the Zivot-Andrews test is rejected at p_threshold
test_statistic_float: The ZA test statistic, of running ZivotAndrews on y in fit
pvalue_floatfloat: p-value obtained when running ZivotAndrews 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 StationarityZivotAndrews
>>>
>>> X = load_airline()
>>> sty_est = StationarityZivotAndrews()
>>> sty_est.fit(X)
StationarityZivotAndrews(...)
>>> 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.