--- title: rnn description: 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. keywords: - RNN - forecasting - training model - Parameters - Returns - chart - machine learning - predict - AI - Neural networks --- import HeadTitle from '@site/src/components/General/HeadTitle.tsx'; import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; Perform RNN forecasting Source Code: [[link](https://github.com/OpenBB-finance/OpenBBTerminal/tree/main/openbb_terminal/forecast/rnn_model.py#L21)] ```python 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) ``` --- ## 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 | --- ## 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 | --- Display RNN forecast Source Code: [[link](https://github.com/OpenBB-finance/OpenBBTerminal/tree/main/openbb_terminal/forecast/rnn_view.py#L20)] ```python 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) ``` --- ## 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 | --- ## Returns This function does not return anything ---