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.
Parameters
- standard
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.