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

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autoarima	This documentation page discusses the use of the automatic ARIMA (AutoARIMA) model for forecasting. It provides a detailed insight into the parameters involved, the return types, model structures, and links to the source code. This page is specifically valuable for individuals seeking understanding of OpenBB's financial forecasting abilities based on time series data using Python.	ARIMA Forecasting AutoARIMA TimeSeries Source Code Parameters Returns Model Chart

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Performs Automatic ARIMA forecasting

Source Code: [link]

openbb.forecast.autoarima(data: Union[pd.Series, pd.DataFrame], target_column: str = "close", seasonal_periods: int = 7, n_predict: int = 5, start_window: float = 0.85, forecast_horizon: int = 5)

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Parameters

Name	Type	Description	Default	Optional
data	Union[pd.Series, np.ndarray]	Input data.	None	False
target_column	Optional[str]	Target column to forecast. Defaults to "close".	close	True
seasonal_periods	int	Number of seasonal periods in a year (7 for daily data) If not set, inferred from frequency of the series.	7	True
n_predict	int	Number of days to forecast	5	True
start_window	float	Size of sliding window from start of timeseries and onwards	0.85	True
forecast_horizon	int	Number of days to forecast when backtesting and retraining historical	5	True

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Returns

Type	Description
Tuple[List[TimeSeries], List[TimeSeries], List[TimeSeries], float, StatsForecast]	Adjusted Data series, List of historical fcast values, List of predicted fcast values, Optional[float] - precision Fit AutoaRIMA model object.

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Display Automatic ARIMA model.

Source Code: [link]

openbb.forecast.autoarima_chart(data: Union[pd.DataFrame, pd.Series], target_column: str = "close", dataset_name: str = "", seasonal_periods: int = 7, n_predict: int = 5, start_window: float = 0.85, forecast_horizon: int = 5, export: str = "", residuals: bool = False, forecast_only: bool = False, start_date: Optional[datetime.datetime] = None, end_date: Optional[datetime.datetime] = None, naive: 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, np.array]	Data to forecast	None	False
dataset_name str	None	The name of the ticker to be predicted	None	True
target_column	Optional[str]:	Target column to forecast. Defaults to "close".	close	True
seasonal_periods	int	Number of seasonal periods in a year If not set, inferred from frequency of the series.	7	True
n_predict	int	Number of days to forecast	5	True
start_window	float	Size of sliding window from start of timeseries and onwards	0.85	True
forecast_horizon	int	Number of days to forecast when backtesting and retraining historical	5	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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