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103 lines
5.7 KiB
Markdown
Vendored
103 lines
5.7 KiB
Markdown
Vendored
---
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title: brnn
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description: This documentation page provides in-depth details on how to effectively
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perform BRNN forecasting using machine learning techniques such as LSTM and GRU
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with Datusaurus models. It contains all the necessary parameters with their description,
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default values, and optionality. The page also offers a clear working example demonstrating
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the usage of BRNN for forecasting financial time series data.
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keywords:
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- BRNN forecast
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- Vanilla RNN
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- LSTM
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- GRU
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- machine learning
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- data analysis
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- datusaurus models
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- forecasts
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- parameters
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- covariates
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- train split
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- epoch
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- training
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- RNN module type
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- dropout
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- batch size
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- learning rate
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- residuals
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---
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import HeadTitle from '@site/src/components/General/HeadTitle.tsx';
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<HeadTitle title="forecast /brnn - Reference | OpenBB Terminal Docs" />
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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
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### Usage
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```python
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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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```
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---
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## Parameters
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| Name | Description | Default | Optional | Choices |
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| ---- | ----------- | ------- | -------- | ------- |
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| n_rnn_layers | Number of layers in the RNN module. | 1 | True | None |
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| past_covariates | Past covariates(columns/features) in same dataset. Comma separated. | None | True | None |
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| all_past_covariates | Adds all rows as past covariates except for date and the target column. | False | True | None |
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| naive | Show the naive baseline for a model. | False | True | None |
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| target_dataset | The name of the dataset you want to select | None | True | None |
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| target_column | The name of the specific column you want to use | close | True | None |
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| n_days | prediction days. | 5 | True | None |
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| train_split | Start point for rolling training and forecast window. 0.0-1.0 | 0.85 | True | None |
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| input_chunk_length | Number of past time steps for forecasting module at prediction time. | 14 | True | None |
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| output_chunk_length | The length of the forecast of the model. | 5 | True | None |
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| 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 |
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| save_checkpoints | Whether to automatically save the untrained model and checkpoints. | True | True | None |
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| model_save_name | Name of the model to save. | brnn_model | True | None |
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| n_epochs | Number of epochs over which to train the model. | 300 | True | None |
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| model_type | Either a string specifying the RNN module type ("RNN", "LSTM" or "GRU") | LSTM | True | None |
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| dropout | Fraction of neurons affected by Dropout, from 0 to 1. | 0 | True | None |
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| batch_size | Number of time series (input and output) used in each training pass | 32 | True | None |
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| s_end_date | The end date (format YYYY-MM-DD) to select for testing | None | True | None |
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| s_start_date | The start date (format YYYY-MM-DD) to select for testing | None | True | None |
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| learning_rate | Learning rate during training. | 0.001 | True | None |
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| residuals | Show the residuals for the model. | False | True | None |
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| forecast_only | Do not plot the historical data without forecasts. | False | True | None |
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| export_pred_raw | Export predictions to a csv file. | False | True | None |
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---
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## Examples
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```python
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2022 Jul 23, 10:36 (🦋) /forecast/ $ load GME_20220719_123734.csv -a GME
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2022 Jul 23, 11:03 (🦋) /forecast/ $ brnn GME
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100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:0700:00, 15.10it/s]
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Block RNN model obtains MAPE: 17.09%
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Actual price: $ 146.64
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┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓
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┃ Datetime ┃ Prediction ┃
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┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩
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│ 2022-07-19 00:00:00 │ $ 147.89 │
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├─────────────────────┼────────────┤
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│ 2022-07-20 00:00:00 │ $ 149.11 │
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├─────────────────────┼────────────┤
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│ 2022-07-21 00:00:00 │ $ 149.59 │
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├─────────────────────┼────────────┤
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│ 2022-07-22 00:00:00 │ $ 141.46 │
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├─────────────────────┼────────────┤
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│ 2022-07-25 00:00:00 │ $ 144.99 │
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└─────────────────────┴────────────┘
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```
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---
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