pmdarima.arima.PPTest

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

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.

Parameters:

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.

Notes

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.

References

[R64]

R’s tseries PP test source code: http://bit.ly/2wbzx6V

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, lshort=True)

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

get_params(deep=True)

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.

is_stationary(x)

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.

set_params(**params)

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.

should_diff(x)

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.