tft
- Model
- Chart
Performs Temporal Fusion Transformer forecasting
Source Code: [link]
openbb.forecast.tft(data: Union[pd.Series, pd.DataFrame], target_column: str = "close", n_predict: int = 5, past_covariates: str = None, train_split: float = 0.85, forecast_horizon: int = 5, input_chunk_length: int = 14, output_chunk_length: int = 5, hidden_size: int = 16, lstm_layers: int = 1, num_attention_heads: int = 4, full_attention: bool = False, dropout: float = 0.1, hidden_continuous_size: int = 8, n_epochs: int = 200, batch_size: int = 32, model_save_name: str = "tft_model", force_reset: bool = True, save_checkpoints: bool = True)
Parameters
| Name | Type | Description | Default | Optional |
|---|---|---|---|---|
| data (Union[pd.Series, pd.DataFrame]) | Input Data | None | True | |
| target_column | Optional[str] | Target column to forecast. Defaults to "close". | close | True |
| n_predict | (int, optional) | Days to predict. Defaults to 5. | 5 | True |
| train_split | (float, optional) | Train/val split. Defaults to 0.85. | 0.85 | True |
| past_covariates | (str, optional) | Multiple secondary columns to factor in when forecasting. Defaults to None. | None | True |
| forecast_horizon | (int, optional) | Forecast horizon when performing historical forecasting. Defaults to 5. | 5 | True |
| input_chunk_length | (int, optional) | Number of past time steps that are fed to the forecasting module at prediction time. Defaults to 14. | 14 | True |
| output_chunk_length | (int, optional) | The length of the forecast of the model. Defaults to 5. | 5 | True |
| hidden_size | (int, optional) | Hidden state size of the TFT. Defaults to 16. | 16 | True |
| lstm_layers | (int, optional) | Number of layers for the Long Short Term Memory Encoder and Decoder. Defaults to 16. | 1 | True |
| num_attention_headers | (int, optional) | Number of attention heads. Defaults to 4. | None | True |
| full_attention | (bool, optional) | Whether to apply a multi-head attention query. Defaults to False> | False | True |
| dropout | (float, optional) | Fraction of neurons affected by dropout. Defaults to 0.1. | 0.1 | True |
| hidden_continuous_size | (int, optional) | Default hidden size for processing continuous variables. Defaults to 8. | 8 | True |
| n_epochs | (int, optional) | Number of epochs to run during training. Defaults to 200. | 200 | True |
| batch_size | (int, optional) | Number of samples to pass through network during a single epoch. Defaults to 32. | 32 | True |
| model_save_name | (str, optional) | The name for the model. Defaults to tft_model | tft_model | True |
| force_reset | (bool, optional) | If set to True, any previously-existing model with the same name will be reset (all checkpoints will be discarded). Defaults to True. | True | True |
| save_checkpoints | (bool, optional) | Whether or not to automatically save the untrained model and checkpoints from training. Defaults to True. | True | True |
Returns
| Type | Description |
|---|---|
| Adjusted Data series, List of historical fcast values, List of predicted fcast values, Optional[float] - precision, Fit Prob. TFT model object. |
Display Temporal Fusion Transformer forecast
Source Code: [link]
openbb.forecast.tft_chart(data: Union[pd.Series, pd.DataFrame], target_column: str = "close", dataset_name: str = "", n_predict: int = 5, past_covariates: str = None, train_split: float = 0.85, forecast_horizon: int = 5, input_chunk_length: int = 14, output_chunk_length: int = 5, hidden_size: int = 16, lstm_layers: int = 1, num_attention_heads: int = 4, full_attention: bool = False, dropout: float = 0.1, hidden_continuous_size: int = 8, n_epochs: int = 200, batch_size: int = 32, model_save_name: str = "tft_model", force_reset: bool = True, save_checkpoints: bool = True, export: str = "", residuals: bool = False, forecast_only: bool = False, start_date: Optional[datetime.datetime] = None, end_date: Optional[datetime.datetime] = None, naive: bool = False, export_pred_raw: bool = False, external_axes: Optional[List[axes]] = None)
Parameters
| Name | Type | Description | Default | Optional |
|---|---|---|---|---|
| data (Union[pd.Series, pd.DataFrame]) | Input Data | None | True | |
| target_column | Optional[str]: | Target column to forecast. Defaults to "close". | close | True |
| dataset_name str | None | The name of the ticker to be predicted | None | True |
| n_predict (int, optional) | Days to predict. Defaults to 5. | None | True | |
| train_split (float, optional) | Train/val split. Defaults to 0.85. | None | True | |
| past_covariates (str, optional) | Multiple secondary columns to factor in when forecasting. Defaults to None. | None | True | |
| forecast_horizon (int, optional) | Forecast horizon when performing historical forecasting. Defaults to 5. | None | True | |
| input_chunk_length (int, optional) | Number of past time steps that are fed to the forecasting module at prediction time. Defaults to 14. | None | True | |
| output_chunk_length (int, optional) | The length of the forecast of the model. Defaults to 5. | None | True | |
| hidden_size (int, optional) | Hidden state size of the TFT. Defaults to 16. | None | True | |
| lstm_layers (int, optional) | Number of layers for the Long Short Term Memory Encoder and Decoder. Defaults to 16. | None | True | |
| num_attention_headers (int, optional) | Number of attention heads. Defaults to 4. | None | True | |
| full_attention (bool, optional) | Whether to apply a multi-head attention query. Defaults to False> | None | True | |
| dropout (float, optional) | Fraction of neurons affected by dropout. Defaults to 0.1. | None | True | |
| hidden_continuous_size (int, optional) | Default hidden size for processing continuous variables. Defaults to 8. | None | True | |
| n_epochs (int, optional) | Number of epochs to run during training. Defaults to 200. | None | True | |
| batch_size (int, optional) | Number of samples to pass through network during a single epoch. Defaults to 32. | None | True | |
| model_save_name (str, optional) | The name for the model. Defaults to tft_model | None | True | |
| force_reset (bool, optional) | If set to True, any previously-existing model with the same name will be reset (all checkpoints will be discarded). Defaults to True. | None | True | |
| save_checkpoints (bool, optional) | Whether or not to automatically save the untrained model and checkpoints from training. Defaults to True. | None | True | |
| residuals | bool | Whether to show residuals for the model. Defaults to False. | False | True |
| forecast_only | bool | Whether to only show dates in the forecasting range. Defaults to False. | False | True |
| start_date | Optional[datetime] | The starting date to perform analysis, data before this is trimmed. Defaults to None. | None | True |
| end_date | Optional[datetime] | The ending date to perform analysis, data after this is trimmed. Defaults to None. | None | True |
| naive | bool | Whether to show the naive baseline. This just assumes the closing price will be the same as the previous day's closing price. Defaults to False. | False | True |
| external_axes | Optional[List[plt.axes]] | External axes to plot on | None | True |
Returns
This function does not return anything