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

title, description, keywords

title	description	keywords
rnn	Explore the use of Recurrent Neural Networks (RNNs) for time series forecasting. It explains how to perform RNN forecasts using different architectures like Vanilla RNN, LSTM, and GRU, using the Darts models forecasting library. The document covers usage examples, parameters description and features of the model.	RNN forecast Vanilla RNN LSTM GRU darts.models.forecasting.rnn_model parameters examples machine learning model training model prediction time series analysis

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

Usage

rnn [--hidden-dim HIDDEN_DIM] [--training_length TRAINING_LENGTH] [--naive] [-d {}] [-c TARGET_COLUMN] [-n N_DAYS] [-t TRAIN_SPLIT] [-i INPUT_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
hidden_dim	Size for feature maps for each hidden RNN layer (h_n)	20	True	None
training_length	The length of both input (target and covariates) and output (target) time series used during training. Generally speaking, training_length should have a higher value than input_chunk_length because otherwise during training the RNN is never run for as many iterations as it will during training.	20	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
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.	rnn_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/ $ rnn GME
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:0700:00, 15.10it/s]
RNN model obtains MAPE: 14.67%



       Actual price: $ 146.64
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┃ Datetime            ┃ Prediction ┃
┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ 2022-07-19 00:00:00 │ $ 146.89   │
├─────────────────────┼────────────┤
│ 2022-07-20 00:00:00 │ $ 148.58   │
├─────────────────────┼────────────┤
│ 2022-07-21 00:00:00 │ $ 150.00   │
├─────────────────────┼────────────┤
│ 2022-07-22 00:00:00 │ $ 151.23   │
├─────────────────────┼────────────┤
│ 2022-07-25 00:00:00 │ $ 152.29   │
└─────────────────────┴────────────┘

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