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-stationairty, 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 tesing 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.
“ctt” : constant, and linear and quadratic trend.
“n” : no constant, no 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(...)
>>> sp_est.get_fitted_params()["stationary"]  
False

Methods

`check_is_fitted`()	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters.
`clone_tags`(estimator[, tag_names])	clone/mirror tags from another estimator as dynamic override.
`create_test_instance`([parameter_set])	Construct Estimator instance if possible.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(X)	Fit estimator and estimate parameters.
`get_class_tag`(tag_name[, tag_value_default])	Get tag value from estimator class (only class tags).
`get_class_tags`()	Get class tags from estimator class and all its parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get parameter defaults for the object.
`get_param_names`()	Get parameter names for the object.
`get_params`([deep])	Get parameters for this estimator.
`get_tag`(tag_name[, tag_value_default, …])	Get tag value from estimator class and dynamic tag overrides.
`get_tags`()	Get tags from estimator class and dynamic tag overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`is_composite`()	Check if the object is composite.
`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])	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_tags`(**tag_dict)	Set dynamic tags to given values.
`update`(X)	Update fitted parameters on more data.

classmethod get_test_params(parameter_set='default')[source]#

Return testing parameter settings for the estimator.

Parameters

parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set. There are currently no reserved values for transformers.

Returns

paramsdict or list of dict, default = {}: Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

check_is_fitted()[source]#

Check if the estimator has been fitted.

Raises

NotFittedError: If the estimator has not been fitted yet.

clone()[source]#

Obtain a clone of the object with same hyper-parameters.

A clone is a different object without shared references, in post-init state. This function is equivalent to returning sklearn.clone of self. Equal in value to type(self)(**self.get_params(deep=False)).

Returns

instance of type(self), clone of self (see above)

clone_tags(estimator, tag_names=None)[source]#

clone/mirror tags from another estimator as dynamic override.

Parameters

estimatorestimator inheriting from :class:BaseEstimator
tag_namesstr or list of str, default = None: Names of tags to clone. If None then all tags in estimator are used as tag_names.

Returns

Self: Reference to self.

Notes

Changes object state by setting tag values in tag_set from estimator as dynamic tags in self.

classmethod create_test_instance(parameter_set='default')[source]#

Construct Estimator instance if possible.

Parameters

parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns

instanceinstance of the class with default parameters

Notes

get_test_params can return dict or list of dict. This function takes first or single dict that get_test_params returns, and constructs the object with that.

classmethod create_test_instances_and_names(parameter_set='default')[source]#

Create list of all test instances and a list of names for them.

Parameters

parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns

objslist of instances of cls: i-th instance is cls(**cls.get_test_params()[i])
nameslist of str, same length as objs: i-th element is name of i-th instance of obj in tests convention is {cls.__name__}-{i} if more than one instance otherwise {cls.__name__}
parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

fit(X)[source]#

Fit estimator and estimate parameters.

State change:: Changes state to “fitted”.
Writes to self:: Sets self._is_fitted flag to True. Writes X to self._X. Sets fitted model attributes ending in “_”.

Parameters

Xtime series in sktime compatible data container format

Time series to which to fit the forecaster in the update.

y can be in one of the following formats, must be same scitype as in fit: Series scitype: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) Panel scitype: pd.DataFrame with 2-level row MultiIndex,

3D np.ndarray, list of Series pd.DataFrame, or nested pd.DataFrame

Hierarchical scitype: pd.DataFrame with 3 or more level row MultiIndex For further details:

on usage, see forecasting tutorial examples/01_forecasting.ipynb on specification of formats, examples/AA_datatypes_and_datasets.ipynb

Returns

selfReference to self.

classmethod get_class_tag(tag_name, tag_value_default=None)[source]#

Get tag value from estimator class (only class tags).

Parameters

tag_namestr: Name of tag value.
tag_value_defaultany type: Default/fallback value if tag is not found.

Returns

tag_value: Value of tag_name tag in self. If not found, returns tag_value_default.

classmethod get_class_tags()[source]#

Get class tags from estimator class and all its parent classes.

Returns

collected_tagsdict: Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance. NOT overridden by dynamic tags set by set_tags or mirror_tags.

get_config()[source]#

Get config flags for self.

Returns

config_dictdict: Dictionary of config name : config value pairs. Collected from _config class attribute via nested inheritance and then any overrides and new tags from _onfig_dynamic object attribute.

get_fitted_params(deep=True)[source]#

Get fitted parameters.

State required:: Requires state to be “fitted”.

Parameters

deepbool, default=True

Whether to return fitted parameters of components.

If True, will return a dict of parameter name : value for this object, including fitted parameters of fittable components (= BaseEstimator-valued parameters).
If False, will return a dict of parameter name : value for this object, but not include fitted parameters of components.

Returns

fitted_paramsdict with str-valued keys

Dictionary of fitted parameters, paramname : paramvalue keys-value pairs include:

always: all fitted parameters of this object, as via get_param_names values are fitted parameter value for that key, of this object
if deep=True, also contains keys/value pairs of component parameters parameters of components are indexed as [componentname]__[paramname] all parameters of componentname appear as paramname with its value
if deep=True, also contains arbitrary levels of component recursion, e.g., [componentname]__[componentcomponentname]__[paramname], etc

classmethod get_param_defaults()[source]#

Get parameter defaults for the object.

Returns

default_dict: dict with str keys: keys are all parameters of cls that have a default defined in __init__ values are the defaults, as defined in __init__

classmethod get_param_names()[source]#

Get parameter names for the object.

Returns

param_names: list of str, alphabetically sorted list of parameter names of cls

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsdict: Parameter names mapped to their values.

get_tag(tag_name, tag_value_default=None, raise_error=True)[source]#

Get tag value from estimator class and dynamic tag overrides.

Parameters

tag_namestr: Name of tag to be retrieved
tag_value_defaultany type, optional; default=None: Default/fallback value if tag is not found
raise_errorbool: whether a ValueError is raised when the tag is not found

Returns

tag_value: Value of the tag_name tag in self. If not found, returns an error if raise_error is True, otherwise it returns tag_value_default.

Raises

ValueError if raise_error is True i.e. if tag_name is not in self.get_tags(
).keys()

get_tags()[source]#

Get tags from estimator class and dynamic tag overrides.

Returns

collected_tagsdict: Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance and then any overrides and new tags from _tags_dynamic object attribute.

is_composite()[source]#

Check if the object is composite.

A composite object is an object which contains objects, as parameters. Called on an instance, since this may differ by instance.

Returns

composite: bool, whether self contains a parameter which is BaseObject

property is_fitted[source]#: Whether fit has been called.

classmethod load_from_path(serial)[source]#

Load object from file location.

Parameters

serialresult of ZipFile(path).open(“object)

Returns

deserialized self resulting in output at path, of cls.save(path)

classmethod load_from_serial(serial)[source]#

Load object from serialized memory container.

Parameters

serial1st element of output of cls.save(None)

Returns

deserialized self resulting in output serial, of cls.save(None)

reset()[source]#

Reset the object to a clean post-init state.

Equivalent to sklearn.clone but overwrites self. After self.reset() call, self is equal in value to type(self)(**self.get_params(deep=False))

Detail behaviour: removes any object attributes, except:

hyper-parameters = arguments of __init__ object attributes containing double-underscores, i.e., the string “__”

runs __init__ with current values of hyper-parameters (result of get_params)

Not affected by the reset are: object attributes containing double-underscores class and object methods, class attributes

save(path=None)[source]#

Save serialized self to bytes-like object or to (.zip) file.

Behaviour: if path is None, returns an in-memory serialized self if path is a file location, stores self at that location as a zip file

saved files are zip files with following contents: _metadata - contains class of self, i.e., type(self) _obj - serialized self. This class uses the default serialization (pickle).

Parameters

pathNone or file location (str or Path): if None, self is saved to an in-memory object if file location, self is saved to that file location. If:

path=”estimator” then a zip file estimator.zip will be made at cwd. path=”/home/stored/estimator” then a zip file estimator.zip will be stored in /home/stored/.

Returns

if path is None - in-memory serialized self
if path is file location - ZipFile with reference to the file

set_config(**config_dict)[source]#

Set config flags to given values.

Parameters

config_dictdict: Dictionary of config name : config value pairs.

Returns

selfreference to self.

Notes

Changes object state, copies configs in config_dict to self._config_dynamic.

set_params(**params)[source]#

Set the parameters of this object.

The method works on simple estimators as well as on nested objects. The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**paramsdict: BaseObject parameters

Returns

selfreference to self (after parameters have been set)

set_tags(**tag_dict)[source]#

Set dynamic tags to given values.

Parameters

tag_dictdict: Dictionary of tag name : tag value pairs.

Returns

Self: Reference to self.

Notes

Changes object state by settting tag values in tag_dict as dynamic tags in self.

update(X)[source]#

Update fitted parameters on more data.

If no estimator-specific update method has been implemented, default fall-back is fitting to all observed data so far

State required:: Requires state to be “fitted”.
Accesses in self:: Fitted model attributes ending in “_”. Pointers to seen data, self._X self._is_fitted model attributes ending in “_”.
Writes to self:: Update self._X with X, by appending rows. Updates fitted model attributes ending in “_”.

Parameters

Xtime series in sktime compatible data container format

Time series to which to fit the forecaster in the update.

y can be in one of the following formats, must be same scitype as in fit: Series scitype: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) Panel scitype: pd.DataFrame with 2-level row MultiIndex,

3D np.ndarray, list of Series pd.DataFrame, or nested pd.DataFrame

Hierarchical scitype: pd.DataFrame with 3 or more level row MultiIndex For further details:

on usage, see forecasting tutorial examples/01_forecasting.ipynb on specification of formats, examples/AA_datatypes_and_datasets.ipynb

Returns

selfreference to self