unitroot_test
Get Unit Root Test.
This function applies two renowned tests to assess whether your data series is stationary or if it contains a unit root, indicating it may be influenced by time-based trends or seasonality. The Augmented Dickey-Fuller (ADF) test helps identify the presence of a unit root, suggesting that the series could be non-stationary and potentially unpredictable over time. On the other hand, the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test checks for the stationarity of the series, where failing to reject the null hypothesis indicates a stable, stationary series. Together, these tests provide a comprehensive view of your data's time series properties, essential for accurate modeling and forecasting.
obb.quantitative.unitroot_test(data: Union[list, dict, pd.DataFrame, List[pd.DataFrame], pd.Series, List[pd.Series], numpy.ndarray, Data, List[Data]], target: str, fuller_reg: Literal[str] = c, kpss_reg: Literal[str] = c)
Parameters
- Standard
| Name | Type | Description | Default | Optional |
|---|---|---|---|---|
| data | List[Data] | Time series data. | None | False |
| target | str | Target column name. | None | False |
| fuller_reg | Literal["c", "ct", "ctt", "nc", "c"] | Regression type for ADF test. | c | True |
| kpss_reg | Literal["c", "ct"] | Regression type for KPSS test. | c | True |
Returns
Unit root tests summary.