pmdarima.arima
.KPSSTest¶
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class
pmdarima.arima.
KPSSTest
(alpha=0.05, null='level', lshort=True)[source][source]¶ Conduct a KPSS test for stationarity.
In econometrics, Kwiatkowski–Phillips–Schmidt–Shin (KPSS) tests are used for testing a null hypothesis that an observable time series is stationary around a deterministic trend (i.e. trend-stationary) against the alternative of a unit root.
Parameters: alpha : float, optional (default=0.05)
Level of the test
null : str, optional (default=’level’)
Whether to fit the linear model on the one vector, or an arange. If
null
is ‘trend’, a linear model is fit on an arange, if ‘level’, it is fit on the one vector.lshort : bool, optional (default=True)
Whether or not to truncate the
l
value in the C code.Notes
This test is generally used indirectly via the
pmdarima.arima.ndiffs()
function, which computes the differencing term,d
.References
[R61] R’s tseries KPSS test source code: http://bit.ly/2eJP1IU Methods
get_params
([deep])Get parameters for this estimator. is_stationary
(x)Test whether the time series is stationary. set_params
(**params)Set the parameters of this estimator. should_diff
(x)Test whether the time series is stationary or needs differencing. -
__init__
(alpha=0.05, null='level', lshort=True)[source][source]¶ Initialize self. See help(type(self)) for accurate signature.
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get_params
(deep=True)[source]¶ Get parameters for this estimator.
Parameters: deep : bool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping of string to any
Parameter names mapped to their values.
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is_stationary
(x)[source]¶ Test whether the time series is stationary.
Parameters: x : array-like, shape=(n_samples,)
The time series vector.
Returns: pval : float
The computed P-value of the test.
sig : bool
Whether the P-value is significant at the
alpha
level. More directly, whether to difference the time series.
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set_params
(**params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.Parameters: **params : dict
Estimator parameters.
Returns: self : object
Estimator instance.
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should_diff
(x)[source][source]¶ Test whether the time series is stationary or needs differencing.
Parameters: x : array-like, shape=(n_samples,)
The time series vector.
Returns: pval : float
The computed P-value of the test.
sig : bool
Whether the P-value is significant at the
alpha
level. More directly, whether to difference the time series.
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