OpenBB/website/content/terminal/reference/forecast/brnn.md

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title	description	keywords
brnn	This documentation page provides in-depth details on how to effectively perform BRNN forecasting using machine learning techniques such as LSTM and GRU with Datusaurus models. It contains all the necessary parameters with their description, default values, and optionality. The page also offers a clear working example demonstrating the usage of BRNN for forecasting financial time series data.	BRNN forecast Vanilla RNN LSTM GRU machine learning data analysis datusaurus models forecasts parameters covariates train split epoch training RNN module type dropout batch size learning rate residuals

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Perform BRNN forecast (Vanilla RNN, LSTM, GRU): https://unit8co.github.io/darts/generated_api/darts.models.forecasting.block_rnn_model.html

Usage

brnn [--n-rnn-layers N_RNN_LAYERS] [--past-covariates PAST_COVARIATES] [--all-past-covariates] [--naive] [-d {}] [-c TARGET_COLUMN] [-n N_DAYS] [-t TRAIN_SPLIT] [-i INPUT_CHUNK_LENGTH] [-o OUTPUT_CHUNK_LENGTH] [--force-reset FORCE_RESET] [--save-checkpoints SAVE_CHECKPOINTS] [--model-save-name MODEL_SAVE_NAME] [--n-epochs N_EPOCHS] [--model-type MODEL_TYPE] [--dropout DROPOUT] [--batch-size BATCH_SIZE] [--end S_END_DATE] [--start S_START_DATE] [--learning-rate LEARNING_RATE] [--residuals] [--forecast-only] [--export-pred-raw]

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Parameters

Name	Description	Default	Optional	Choices
n_rnn_layers	Number of layers in the RNN module.	1	True	None
past_covariates	Past covariates(columns/features) in same dataset. Comma separated.	None	True	None
all_past_covariates	Adds all rows as past covariates except for date and the target column.	False	True	None
naive	Show the naive baseline for a model.	False	True	None
target_dataset	The name of the dataset you want to select	None	True	None
target_column	The name of the specific column you want to use	close	True	None
n_days	prediction days.	5	True	None
train_split	Start point for rolling training and forecast window. 0.0-1.0	0.85	True	None
input_chunk_length	Number of past time steps for forecasting module at prediction time.	14	True	None
output_chunk_length	The length of the forecast of the model.	5	True	None
force_reset	If set to True, any previously-existing model with the same name will be reset (all checkpoints will be discarded).	True	True	None
save_checkpoints	Whether to automatically save the untrained model and checkpoints.	True	True	None
model_save_name	Name of the model to save.	brnn_model	True	None
n_epochs	Number of epochs over which to train the model.	300	True	None
model_type	Either a string specifying the RNN module type ("RNN", "LSTM" or "GRU")	LSTM	True	None
dropout	Fraction of neurons affected by Dropout, from 0 to 1.	0	True	None
batch_size	Number of time series (input and output) used in each training pass	32	True	None
s_end_date	The end date (format YYYY-MM-DD) to select for testing	None	True	None
s_start_date	The start date (format YYYY-MM-DD) to select for testing	None	True	None
learning_rate	Learning rate during training.	0.001	True	None
residuals	Show the residuals for the model.	False	True	None
forecast_only	Do not plot the historical data without forecasts.	False	True	None
export_pred_raw	Export predictions to a csv file.	False	True	None

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Examples

2022 Jul 23, 10:36 (🦋) /forecast/ $ load GME_20220719_123734.csv -a GME

2022 Jul 23, 11:03 (🦋) /forecast/ $ brnn GME
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:0700:00, 15.10it/s]
Block RNN model obtains MAPE: 17.09%



       Actual price: $ 146.64
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┃ Datetime            ┃ Prediction ┃
┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ 2022-07-19 00:00:00 │ $ 147.89   │
├─────────────────────┼────────────┤
│ 2022-07-20 00:00:00 │ $ 149.11   │
├─────────────────────┼────────────┤
│ 2022-07-21 00:00:00 │ $ 149.59   │
├─────────────────────┼────────────┤
│ 2022-07-22 00:00:00 │ $ 141.46   │
├─────────────────────┼────────────┤
│ 2022-07-25 00:00:00 │ $ 144.99   │
└─────────────────────┴────────────┘

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