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

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expo	The page explores the functionalities and parameters of the 'expo' model and 'chart' functions used in Probabilistic Exponential Smoothing forecasting. These functions take data as input and perform operations like backtesting, retraining historical and prediction. Source code links are also shared.	Exponential Smoothing forecasting expo model function expo chart function Probabilistic forecasting data series operations parameters

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Performs Probabilistic Exponential Smoothing forecasting

Source Code: [link]

openbb.forecast.expo(data: Union[pd.Series, pd.DataFrame], target_column: str = "close", trend: str = "A", seasonal: str = "A", seasonal_periods: int = 7, dampen: str = "F", 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
trend	str	Trend component. One of [N, A, M] Defaults to ADDITIVE.	A	True
seasonal	str	Seasonal component. One of [N, A, M] Defaults to ADDITIVE.	A	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
dampen	str	Dampen the function	F	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], Optional[Union[float, ndarray]], ExponentialSmoothing]	Adjusted Data series, List of historical fcast values, List of predicted fcast values, Optional[float] - precision, Fit Prob. Expo model object.

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Display Probabilistic Exponential Smoothing forecast

Source Code: [link]

openbb.forecast.expo_chart(data: Union[pd.DataFrame, pd.Series], target_column: str = "close", dataset_name: str = "", trend: str = "A", seasonal: str = "A", seasonal_periods: int = 7, dampen: str = "F", 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
trend	str	Trend component. One of [N, A, M] Defaults to ADDITIVE.	A	True
seasonal	str	Seasonal component. One of [N, A, M] Defaults to ADDITIVE.	A	True
seasonal_periods	int	Number of seasonal periods in a year If not set, inferred from frequency of the series.	7	True
dampen	str	Dampen the function	F	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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