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Param Estimator

StationarityADFArch

API referenceSource

Test for stationarity via the Augmented Dickey-Fuller Unit Root Test (ADF).

Quickstart

python
from sktime.param_est.stationarity import StationarityADFArch

estimator = StationarityADFArch(lags=None, trend='c', max_lags=None, method='aic', low_memory=None, p_threshold=0.05)

Parameters(5)

lagsint, optional

The number of lags to use in the ADF regression. If omitted or None, method is used to automatically select the lag length with no more than max_lags are included.

trend{“n”, “c”, “ct”, “ctt”}, optional

The trend component to include in the test

  • “n” - No trend components

  • “c” - Include a constant (Default)

  • “ct” - Include a constant and linear time trend

  • “ctt” - Include a constant and linear and quadratic time trends

max_lagsint, optional
The maximum number of lags to use when selecting lag length
method{“AIC”, “BIC”, “t-stat”}, optional

The method to use when selecting the lag length

  • “AIC” - Select the minimum of the Akaike IC

  • “BIC” - Select the minimum of the Schwarz/Bayesian IC

  • “t-stat” - Select the minimum of the Schwarz/Bayesian IC

low_memorybool
Flag indicating whether to use a low memory implementation of the lag selection algorithm. The low memory algorithm is slower than the standard algorithm but will use 2-4% of the memory required for the standard algorithm. This options allows automatic lag selection to be used in very long time series. If None, use automatic selection of algorithm.

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.param_est.stationarity import StationarityADFArch
>>> 
>>> X = load_airline ()
>>> sty_est = StationarityADFArch ()
>>> sty_est. fit (X) StationarityADFArch(
... )
>>> sty_est. get_fitted_params ()["stationary" ] False