class pmdarima.arima.PPTest(alpha=0.05, lshort=True)[source][source]

Conduct a PP test for stationarity.

In statistics, the Phillips–Perron test (named after Peter C. B. Phillips and Pierre Perron) is a unit root test. It is used in time series analysis to test the null hypothesis that a time series is integrated of order 1. It builds on the Dickey–Fuller test of the null hypothesis p = 0.


alpha : float, optional (default=0.05)

Level of the test

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.

The R code allows for two types of tests: ‘Z(alpha)’ and ‘Z(t_alpha)’. Since sklearn does not allow extraction of std errors from the linear model fit, t_alpha is much more difficult to achieve, so we do not allow that variant.


[R34]R’s tseries PP 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, 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.