StationarityADFArch

class StationarityADFArch(lags=None, trend='c', max_lags=None, method='aic', low_memory=None, p_threshold=0.05)[source]

Test for stationarity via the Augmented Dickey-Fuller Unit Root Test (ADF).

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

Uses arch.unitroot.ADF 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{“n”, “c”, “ct”, “ctt”}, optional

The trend component to include in the test

“n” - No trend components
“c” - Include a constant (Default)
“ct” - Include a constant and linear time trend
“ctt” - Include a constant and linear and quadratic time trends

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

low_memorybool

Flag indicating whether to use a low memory implementation of the lag selection algorithm. The low memory algorithm is slower than the standard algorithm but will use 2-4% of the memory required for the standard algorithm. This options allows automatic lag selection to be used in very long time series. If None, use automatic selection of algorithm.

Attributes:

stationary_bool: whether the series in fit is stationary according to the test more precisely, whether the null of the ADF test is rejected at p_threshold
test_statistic_float: The ADF test statistic, of running adfuller on y in fit
pvalue_floatfloat: MacKinnon’s approximate p-value based on MacKinnon (1994, 2010), obtained when running adfuller 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 StationarityADFArch
>>>
>>> X = load_airline()
>>> sty_est = StationarityADFArch()
>>> sty_est.fit(X)
StationarityADFArch(...)
>>> 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.