--- title: brnn description: This page provides comprehensive information about the BRNN function in the OpenBB finance library. It details how to perform Block RNN forecasting and display the forecast with parameters and examples. The page is highly specific to Python developers working in finance and forecasting. keywords: - BRNN Forecast - OpenBB Forecast - Python forecasting - Data series forecasting - RNN - LSTM - GRU - Chart Display - Forecasting parameters --- import HeadTitle from '@site/src/components/General/HeadTitle.tsx'; import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; 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 ---