--- title: brnn description: OpenBB SDK Function --- import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; # brnn Performs Block RNN forecasting Source Code: [[link](https://github.com/OpenBB-finance/OpenBBTerminal/tree/main/openbb_terminal/forecast/brnn_model.py#L22)] ```python openbb.forecast.brnn(data: Union[pd.Series, pd.DataFrame], target_column: str = "close", n_predict: int = 5, train_split: float = 0.85, past_covariates: str = None, forecast_horizon: int = 5, input_chunk_length: int = 14, output_chunk_length: int = 5, model_type: str = "LSTM", n_rnn_layers: int = 1, dropout: float = 0.0, batch_size: int = 32, n_epochs: int = 100, learning_rate: float = 0.001, model_save_name: str = "brnn_model", force_reset: bool = True, save_checkpoints: bool = True) ``` --- ## 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 | | input_chunk_length | int | Number of past time steps that are fed to the forecasting module at prediction time. Defaults to 14. | 14 | True | | output_chunk_length | int | The length of the forecast of the model. 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 | | n_rnn_layers | int | Number of layers in the RNN module. Defaults to 1. | 1 | 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. | 32 | 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". | brnn_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 | | ---- | ----------- | | E501 | Adjusted Data series,
Historical forecast by best RNN model,
list of Predictions,
Mean average precision error,
Best BRNN Model. | --- Display BRNN forecast Source Code: [[link](https://github.com/OpenBB-finance/OpenBBTerminal/tree/main/openbb_terminal/forecast/brnn_view.py#L20)] ```python openbb.forecast.brnn_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, input_chunk_length: int = 14, output_chunk_length: int = 5, model_type: str = "LSTM", n_rnn_layers: int = 1, dropout: float = 0.0, batch_size: int = 16, n_epochs: int = 100, learning_rate: float = 0.001, model_save_name: str = "rnn_model", 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 | | target_column | str | Target column to forecast. Defaults to "close". | close | True | | dataset_name | str | The name of the ticker to be predicted | | 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 | | input_chunk_length | int | Number of past time steps that are fed to the forecasting module at prediction time. Defaults to 14. | 14 | True | | output_chunk_length | int | The length of the forecast of the model. 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 | | n_rnn_layers | int | Number of layers in the RNN module. Defaults to 1. | 1 | 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 101. | 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 | | 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 | --- ## Returns This function does not return anything ---