OpenBB/website/content/sdk/reference/forecast/linregr.md

title, description, keywords

title	description	keywords
linregr	Perform or display linear regression forecasting with OpenBB. The page provides the function parameters and their explanations, as well as the return values.	linear regression forecasting data analysis parameters returns prediction

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Perform Linear Regression Forecasting

Source Code: [link]

openbb.forecast.linregr(data: Union[pd.Series, pd.DataFrame], target_column: str = "close", n_predict: int = 5, past_covariates: str = None, train_split: float = 0.85, forecast_horizon: int = 5, output_chunk_length: int = 5, lags: Union[int, List[int]] = 14, random_state: Optional[int] = None)

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Parameters

Name	Type	Description	Default	Optional
data	Union[pd.Series, pd.DataFrame]	Input Data	None	False
target_column	str	Target column to forecast. Defaults to "close".	close	True
n_predict	int	Days to predict. Defaults to 5.	5	True
train_split	float	Train/val split. Defaults to 0.85.	0.85	True
past_covariates	str	Multiple secondary columns to factor in when forecasting. Defaults to None.	None	True
forecast_horizon	int	Forecast horizon when performing historical forecasting. Defaults to 5.	5	True
output_chunk_length	int	The length of the forecast of the model. Defaults to 1.	5	True
lags	Union[int, List[int]]	lagged target values to predict the next time step	14	True
random_state	Optional[int]	The state for the model	None	True

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Returns

Type	Description
Tuple[List[TimeSeries], List[TimeSeries], List[TimeSeries], float, LinearRegressionModel]	Adjusted Data series, Historical forecast by best RNN model, list of Predictions, Mean average precision error, Best Linear Regression Model.

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Display Linear Regression Forecasting

Source Code: [link]

openbb.forecast.linregr_chart(data: Union[pd.Series, pd.DataFrame], target_column: str = "close", dataset_name: str = "", n_predict: int = 5, past_covariates: str = None, train_split: float = 0.85, forecast_horizon: int = 5, output_chunk_length: int = 1, lags: Union[int, List[int]] = 72, export: str = "", residuals: bool = False, forecast_only: bool = False, start_date: Optional[datetime.datetime] = None, end_date: Optional[datetime.datetime] = None, naive: bool = False, explainability_raw: bool = False, export_pred_raw: bool = False, external_axes: Optional[List[axes]] = None)

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Parameters

Name	Type	Description	Default	Optional
data	Union[pd.Series, pd.DataFrame]	Input Data	None	False
dataset_name	str	The name of the ticker to be predicted		True
n_predict	int	Days to predict. Defaults to 5.	5	True
target_col	str	Target column to forecast. Defaults to "close".	None	True
train_split (float, optional)		Train/val split. Defaults to 0.85.	None	True
past_covariates	str	Multiple secondary columns to factor in when forecasting. Defaults to None.	None	True
forecast_horizon	int	Forecast horizon when performing historical forecasting. Defaults to 5.	5	True
output_chunk_length	int	The length of the forecast of the model. Defaults to 1.	1	True
lags	Union[int, List[int]]	lagged target values to predict the next time step	72	True
export	str	Format to export data		True
residuals	bool	Whether to show residuals for the model. Defaults to False.	False	True
forecast_only	bool	Whether to only show dates in the forecasting range. Defaults to False.	False	True
start_date	Optional[datetime]	The starting date to perform analysis, data before this is trimmed. Defaults to None.	None	True
end_date	Optional[datetime]	The ending date to perform analysis, data after this is trimmed. Defaults to None.	None	True
naive	bool	Whether to show the naive baseline. This just assumes the closing price will be the same as the previous day's closing price. Defaults to False.	False	True
external_axes	Optional[List[plt.axes]]	External axes to plot on	None	True

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Returns

This function does not return anything

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