StationarityADF

class StationarityADF(p_threshold=0.05, maxlag=None, regression='c', autolag='AIC')[source]

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

Uses statsmodels.tsa.stattools.adfuller 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:

p_thresholdfloat, optional, default=0.05

significance threshold to apply in testing for stationarity

maxlagint or None, optional, default=None

Maximum lag which is included in test, default value of 12*(nobs/100)^{1/4} is used when None.

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.
“ctt” : constant, and linear and quadratic trend.
“n” : no constant, no trend.

autolagone of {“AIC”, “BIC”, “t-stat”, None}, optional, default=”AIC”

Method to use when automatically determining the lag length among the values 0, 1, …, maxlag.

If “AIC” (default) or “BIC”, then the number of lags is chosen to minimize the corresponding information criterion.
“t-stat” based choice of maxlag. Starts with maxlag and drops a lag until the t-statistic on the last lag length is significant using a 5%-sized test.
If None, then the number of included lags is set to maxlag.

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 StationarityADF
>>>
>>> X = load_airline()
>>> sty_est = StationarityADF()
>>> sty_est.fit(X)
StationarityADF(...)
>>> 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.