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
- 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.