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autocorrelation

Perform Durbin-Watson test for autocorrelation.

The Durbin-Watson test is a widely used method for detecting the presence of autocorrelation in the residuals from a statistical or econometric model. Autocorrelation occurs when past values in the data series influence future values, which can be a critical issue in time-series analysis, affecting the reliability of model predictions. The test provides a statistic that ranges from 0 to 4, where a value around 2 suggests no autocorrelation, values towards 0 indicate positive autocorrelation, and values towards 4 suggest negative autocorrelation. Understanding the degree of autocorrelation helps in refining models to better capture the underlying dynamics of the data, ensuring more accurate and trustworthy results.

Examples

from openbb import obb
# Perform Durbin-Watson test for autocorrelation.
stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()
obb.econometrics.autocorrelation(data=stock_data, y_column="close", x_columns=["open", "high", "low"])
obb.econometrics.autocorrelation(y_column='close', x_columns='['open', 'high', 'low']', 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]

Input dataset.

Optional: False


y_column: str

Target column.

Optional: False


x_columns: list[str]

list of columns to use as exogenous variables.

Optional: False


Returns

results: list[dict]

Serializable results.


provider: None

Provider name.


warnings: Optional[list[Warning_]]

list of warnings.


chart: Optional[Chart]

Chart object.


extra: dict[str, Any]

Extra info.