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

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nbeats	Explore how to use NBEATS, Neural Bayesian Estimation of Time Series, a powerful tool for forecasting. It includes details of parameters, usage, and examples.	nbeats Neural Bayesian Estimation Time Series forecasting darts.models.forecasting python library data science machine learning API examples

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Perform NBEATS forecast (Neural Bayesian Estimation of Time Series): https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nbeats.html

Usage

nbeats [--num_stacks NUM_STACKS] [--num_blocks NUM_BLOCKS] [--num_layers NUM_LAYERS] [--layer_widths LAYER_WIDTHS] [--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] [--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
num_stacks	The number of stacks that make up the whole model.	10	True	None
num_blocks	The number of blocks making up every stack.	3	True	None
num_layers	The number of fully connected layers preceding the final forking layers in each block of every stack.	4	True	None
layer_widths	Determines the number of neurons that make up each fully connected layer in each block of every stack	512	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.	nbeats_model	True	None
n_epochs	Number of epochs over which to train the model.	300	True	None
batch_size	Number of time series (input and output) used in each training pass	800	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/ $ nbeats GME
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:0700:00, 15.10it/s]
NBEATS model obtains MAPE: 23.53%



       Actual price: $ 146.64
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ Datetime            ┃ Prediction ┃
┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ 2022-07-19 00:00:00 │ $ 158.52   │
├─────────────────────┼────────────┤
│ 2022-07-20 00:00:00 │ $ 172.21   │
├─────────────────────┼────────────┤
│ 2022-07-21 00:00:00 │ $ 67.46    │
├─────────────────────┼────────────┤
│ 2022-07-22 00:00:00 │ $ 97.63    │
├─────────────────────┼────────────┤
│ 2022-07-25 00:00:00 │ $ 154.24   │
└─────────────────────┴────────────┘

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