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.


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.


This test is generally used indirectly via the pmdarima.arima.ndiffs() function, which computes the differencing term, d.


[R28]R’s tseries KPSS test source code:


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.
__init__(alpha=0.05, null='level', lshort=True)[source][source]

Initialize self. See help(type(self)) for accurate signature.


Get parameters for this estimator.


deep : boolean, optional

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


params : mapping of string to any

Parameter names mapped to their values.


Test whether the time series is stationary.


x : array-like, shape=(n_samples,)

The time series vector.


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.


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.