tft
Perform TFT forecast (Temporal Fusion Transformer): https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tft_model.html
Usageโ
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]
Parametersโ
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 |
Examplesโ
2022 Jul 23, 10:36 (๐ฆ) /forecast/ $ load GME_20220719_123734.csv -a GME
2022 Jul 23, 11:03 (๐ฆ) /forecast/ $ tft GME
100%|โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ| 115/115 [00:0700:00, 15.10it/s]
TFT model obtains MAPE: 44.60%
Actual price: $ 146.64
โโโโโโโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโ
โ Datetime โ Prediction โ
โกโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฉ
โ 2022-07-19 00:00:00 โ $ 169.69 โ
โโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโค
โ 2022-07-20 00:00:00 โ $ 168.53 โ
โโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโค
โ 2022-07-21 00:00:00 โ $ 167.33 โ
โโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโค
โ 2022-07-22 00:00:00 โ $ 167.23 โ
โโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโค
โ 2022-07-25 00:00:00 โ $ 165.82 โ
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