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Version: v4

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: ForwardRef('Data') | ForwardRef('DataFrame') | ForwardRef('Series') | ForwardRef('ndarray') | dict | list
Input dataset.

y_column: str
Target column.

x_columns: list[str]
list of columns to use as exogenous variables.

Returns​

results: Data

Serializable results.

provider: str

Provider name.

warnings: Optional[list[Warning_]]

list of warnings.

chart: Optional[Chart]

Chart object.

extra: dict[str, Any]

Extra info.

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