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

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title	description	keywords
tft	This page provides detailed instructions on how to perform temporal forecasting using the Temporal Fusion Transformer (TFT). It includes usage examples, parameter explanations, and result predictions. It's an exceptional resource if you're looking to understand or implement the TFT for data analysis and machine learning purposes.	Temporal Fusion Transformer Temporal forecasting Data analysis TFT parameters Model prediction Darts.TFT Darts.models.forecasting Model training Data Science Machine Learning Forecasting Techniques Time Series Forecasting

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Perform TFT forecast (Temporal Fusion Transformer): https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tft_model.html

Usage

tft [--lstm-layers LSTM_LAYERS] [--num-attention-heads NUM_ATTENTION_HEADS] [--full-attention] [--hidden-continuous-size HIDDEN_CONTINUOUS_SIZE] [--hidden-size HIDDEN_SIZE] [--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] [--dropout DROPOUT] [--batch-size BATCH_SIZE] [--end S_END_DATE] [--start S_START_DATE] [--residuals] [--forecast-only] [--export-pred-raw]

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Parameters

Name	Description	Default	Optional	Choices
lstm_layers	Number of LSTM layers.	1	True	None
num_attention_heads	Number of attention heads.	4	True	None
full_attention	Whether to apply a multi-head attention query.	False	True	None
hidden_continuous_size	Default hidden size for processing continuous variables.	8	True	None
hidden_size	Size for feature maps for each hidden RNN layer (h_n)	16	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.	tft_model	True	None
n_epochs	Number of epochs over which to train the model.	300	True	None
dropout	Fraction of neurons affected by Dropout, from 0 to 1.	0.1	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
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/ $ tft GME
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:0700:00, 15.10it/s]
TFT model obtains MAPE: 44.60%



       Actual price: $ 146.64
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ Datetime            ┃ Prediction ┃
┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ 2022-07-19 00:00:00 │ $ 169.69   │
├─────────────────────┼────────────┤
│ 2022-07-20 00:00:00 │ $ 168.53   │
├─────────────────────┼────────────┤
│ 2022-07-21 00:00:00 │ $ 167.33   │
├─────────────────────┼────────────┤
│ 2022-07-22 00:00:00 │ $ 167.23   │
├─────────────────────┼────────────┤
│ 2022-07-25 00:00:00 │ $ 165.82   │
└─────────────────────┴────────────┘

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