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

title, description, keywords

title	description	keywords
rnn	Learn about our Recurrent Neural Networks (RNN) forecasting models in OpenBB Terminal's documentation. This includes instructions on how to perform RNN forecasting, parameters used, returns and how to display an RNN forecast chart.	RNN forecasting training model Parameters Returns chart machine learning predict AI Neural networks

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Perform RNN forecasting

Source Code: [link]

openbb.forecast.rnn(data: Union[pd.Series, pd.DataFrame], target_column: str = "close", n_predict: int = 5, train_split: float = 0.85, forecast_horizon: int = 5, model_type: str = "LSTM", hidden_dim: int = 20, dropout: float = 0.0, batch_size: int = 16, n_epochs: int = 100, learning_rate: float = 0.001, model_save_name: str = "rnn_model", training_length: int = 20, input_chunk_size: int = 14, force_reset: bool = True, save_checkpoints: bool = True)

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Parameters

Name	Type	Description	Default	Optional
data	Union[pd.Series, pd.DataFrame]	Input Data	None	False
n_predict	int	Days to predict. Defaults to 5.	5	True
target_column	str	Target column to forecast. Defaults to "close".	close	True
train_split	float	Train/val split. Defaults to 0.85.	0.85	True
forecast_horizon	int	Forecast horizon when performing historical forecasting. Defaults to 5.	5	True
model_type	str	Either a string specifying the RNN module type ("RNN", "LSTM" or "GRU"). Defaults to "LSTM".	LSTM	True
hidden_dim	int	Size for feature maps for each hidden RNN layer.. Defaults to 20.	20	True
dropout	float	Fraction of neurons affected by Dropout. Defaults to 0.0.	0.0	True
batch_size	int	Number of time series (input and output sequences) used in each training pass. Defaults to 32.	16	True
n_epochs	int	Number of epochs over which to train the model. Defaults to 100.	100	True
learning_rate	float	Defaults to 1e-3.	0.001	True
model_save_name	str	Name for model. Defaults to "brnn_model".	rnn_model	True
force_reset	bool	If set to True, any previously-existing model with the same name will be reset (all checkpoints will be discarded). Defaults to True.	True	True
save_checkpoints	bool	Whether or not to automatically save the untrained model and checkpoints from training. Defaults to True.	True	True

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Returns

Type	Description
Tuple[List[TimeSeries], List[TimeSeries], List[TimeSeries], Optional[float], type[RNNModel]]	Adjusted Data series, Historical forecast by best RNN model, list of Predictions, Mean average precision error, Best RNN Model

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Display RNN forecast

Source Code: [link]

openbb.forecast.rnn_chart(data: Union[pd.DataFrame, pd.Series], target_column: str = "close", dataset_name: str = "", n_predict: int = 5, train_split: float = 0.85, forecast_horizon: int = 5, model_type: str = "LSTM", hidden_dim: int = 20, dropout: float = 0.0, batch_size: int = 16, n_epochs: int = 100, learning_rate: float = 0.001, model_save_name: str = "rnn_model", training_length: int = 20, input_chunk_size: int = 14, force_reset: bool = True, save_checkpoints: bool = True, 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, 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_column	str	Target column to forecast. Defaults to "close".	close	True
train_split	float	Train/val split. Defaults to 0.85.	0.85	True
forecast_horizon	int	Forecast horizon when performing historical forecasting. Defaults to 5.	5	True
model_type	str	Either a string specifying the RNN module type ("RNN", "LSTM" or "GRU"). Defaults to "LSTM".	LSTM	True
hidden_dim	int	Size for feature maps for each hidden RNN layer.. Defaults to 20.	20	True
dropout	float	Fraction of neurons affected by Dropout. Defaults to 0.0.	0.0	True
batch_size	int	Number of time series (input and output sequences) used in each training pass. Defaults to 32.	16	True
n_epochs	int	Number of epochs over which to train the model. Defaults to 100.	100	True
learning_rate	float	Defaults to 1e-3.	0.001	True
model_save_name	str	Name for model. Defaults to "brnn_model".	rnn_model	True
force_reset	bool	If set to True, any previously-existing model with the same name will be reset (all checkpoints will be discarded). Defaults to True.	True	True
save_checkpoints	bool	Whether or not to automatically save the untrained model and checkpoints from training. Defaults to True.	True	True
forecast_only	bool	Whether to only show dates in the forecasting range. Defaults to False.	False	True
export	str	Format to export data		True
residuals	bool	Whether to show residuals for the model. 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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