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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.

Examples​

from openbb import obb
# Get Unit Root Test.
stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()
obb.quantitative.unitroot_test(data=stock_data, target='close')
obb.quantitative.unitroot_test(target='close', data='[{'date': '2023-01-02', 'open': 110.0, 'high': 120.0, 'low': 100.0, 'close': 115.0, 'volume': 10000.0}, {'date': '2023-01-03', 'open': 165.0, 'high': 180.0, 'low': 150.0, 'close': 172.5, 'volume': 15000.0}, {'date': '2023-01-04', 'open': 146.67, 'high': 160.0, 'low': 133.33, 'close': 153.33, 'volume': 13333.33}, {'date': '2023-01-05', 'open': 137.5, 'high': 150.0, 'low': 125.0, 'close': 143.75, 'volume': 12500.0}, {'date': '2023-01-06', 'open': 132.0, 'high': 144.0, 'low': 120.0, 'close': 138.0, 'volume': 12000.0}]')

Parameters​

data: list[openbb_core.provider.abstract.data.Data]

Time series data.

• Optional: False

target: str

Target column name.

• Optional: False

fuller_reg: Literal['c', 'ct', 'ctt', 'nc']

Regression type for ADF test.

• Default: c

• Optional: True

kpss_reg: Literal['c', 'ct']

Regression type for KPSS test.

• Default: c

• Optional: True

Returns​

results: list[UnitRootModel]

Serializable results.

provider: None

Provider name.

warnings: Optional[list[Warning_]]

list of warnings.

chart: Optional[Chart]

Chart object.

extra: dict[str, Any]

Extra info.

Data​

adf: ADFTestModel

kpss: KPSSTestModel

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