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* fix(docs): added margin between sections * feat: removed unused files; updated ga * fix: juan stuff doesnt work * fix: random fix * moon at noon * fix: update docusaurus config * Update controller_doc_classes.py * fix: temp fix * fix: temp fix * Update controller_doc_classes.py * fix: remove old content * fix: taggfix * init * fix: styling and moved file * Update generate_terminal_markdown.py * fix: removed unused images * fix: port new files * Update generate_sdk_markdown.py * Update controller_doc_classes.py Co-authored-by: teh_coderer <me@tehcoderer.com>
116 lines
7.8 KiB
Markdown
Vendored
116 lines
7.8 KiB
Markdown
Vendored
---
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title: tft
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description: OpenBB SDK Function
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---
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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# tft
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<Tabs>
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<TabItem value="model" label="Model" default>
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Performs Temporal Fusion Transformer forecasting
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Source Code: [[link](https://github.com/OpenBB-finance/OpenBBTerminal/tree/main/openbb_terminal/forecast/tft_model.py#L26)]
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```python
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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)
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```
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---
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## Parameters
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| Name | Type | Description | Default | Optional |
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| ---- | ---- | ----------- | ------- | -------- |
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| data (Union[pd.Series, pd.DataFrame]) | | Input Data | None | True |
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| target_column | Optional[str]: | Target column to forecast. Defaults to "close". | close | True |
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| n_predict (int, optional) | | Days to predict. Defaults to 5. | None | True |
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| train_split (float, optional) | | Train/val split. Defaults to 0.85. | None | True |
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| past_covariates (str, optional) | | Multiple secondary columns to factor in when forecasting. Defaults to None. | None | True |
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| forecast_horizon (int, optional) | | Forecast horizon when performing historical forecasting. Defaults to 5. | None | True |
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| input_chunk_length (int, optional) | | Number of past time steps that are fed to the forecasting module at prediction time.<br/>Defaults to 14. | None | True |
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| output_chunk_length (int, optional) | | The length of the forecast of the model. Defaults to 5. | None | True |
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| hidden_size (int, optional) | | Hidden state size of the TFT. Defaults to 16. | None | True |
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| lstm_layers (int, optional) | | Number of layers for the Long Short Term Memory Encoder and Decoder. Defaults to 16. | None | True |
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| num_attention_headers (int, optional) | | Number of attention heads. Defaults to 4. | None | True |
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| full_attention (bool, optional) | | Whether to apply a multi-head attention query. Defaults to False> | None | True |
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| dropout (float, optional) | | Fraction of neurons affected by dropout. Defaults to 0.1. | None | True |
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| hidden_continuous_size (int, optional) | | Default hidden size for processing continuous variables. Defaults to 8. | None | True |
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| n_epochs (int, optional) | | Number of epochs to run during training. Defaults to 200. | None | True |
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| batch_size (int, optional) | | Number of samples to pass through network during a single epoch. Defaults to 32. | None | True |
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| model_save_name (str, optional) | | The name for the model. Defaults to tft_model | None | True |
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| force_reset (bool, optional) | | If set to True, any previously-existing model with the same name will be reset<br/>(all checkpoints will be discarded). Defaults to True. | None | True |
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| save_checkpoints (bool, optional) | | Whether or not to automatically save the untrained model and checkpoints from training.<br/>Defaults to True. | None | True |
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---
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## Returns
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| Type | Description |
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| ---- | ----------- |
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| | Adjusted Data series,<br/>List of historical fcast values,<br/>List of predicted fcast values,<br/>Optional[float] - precision,<br/>Fit Prob. TFT model object. |
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---
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</TabItem>
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<TabItem value="view" label="Chart">
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Display Temporal Fusion Transformer forecast
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Source Code: [[link](https://github.com/OpenBB-finance/OpenBBTerminal/tree/main/openbb_terminal/forecast/tft_view.py#L20)]
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```python
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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)
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```
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---
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## Parameters
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| Name | Type | Description | Default | Optional |
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| ---- | ---- | ----------- | ------- | -------- |
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| data (Union[pd.Series, pd.DataFrame]) | | Input Data | None | True |
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| target_column | Optional[str]: | Target column to forecast. Defaults to "close". | close | True |
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| dataset_name str | None | The name of the ticker to be predicted | None | True |
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| n_predict (int, optional) | | Days to predict. Defaults to 5. | None | True |
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| train_split (float, optional) | | Train/val split. Defaults to 0.85. | None | True |
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| past_covariates (str, optional) | | Multiple secondary columns to factor in when forecasting. Defaults to None. | None | True |
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| forecast_horizon (int, optional) | | Forecast horizon when performing historical forecasting. Defaults to 5. | None | True |
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| input_chunk_length (int, optional) | | Number of past time steps that are fed to the forecasting module at prediction time.<br/>Defaults to 14. | None | True |
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| output_chunk_length (int, optional) | | The length of the forecast of the model. Defaults to 5. | None | True |
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| hidden_size (int, optional) | | Hidden state size of the TFT. Defaults to 16. | None | True |
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| lstm_layers (int, optional) | | Number of layers for the Long Short Term Memory Encoder and Decoder. Defaults to 16. | None | True |
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| num_attention_headers (int, optional) | | Number of attention heads. Defaults to 4. | None | True |
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| full_attention (bool, optional) | | Whether to apply a multi-head attention query. Defaults to False> | None | True |
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| dropout (float, optional) | | Fraction of neurons affected by dropout. Defaults to 0.1. | None | True |
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| hidden_continuous_size (int, optional) | | Default hidden size for processing continuous variables. Defaults to 8. | None | True |
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| n_epochs (int, optional) | | Number of epochs to run during training. Defaults to 200. | None | True |
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| batch_size (int, optional) | | Number of samples to pass through network during a single epoch. Defaults to 32. | None | True |
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| model_save_name (str, optional) | | The name for the model. Defaults to tft_model | None | True |
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| force_reset (bool, optional) | | If set to True, any previously-existing model with the same name will be reset<br/>(all checkpoints will be discarded). Defaults to True. | None | True |
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| save_checkpoints (bool, optional) | | Whether or not to automatically save the untrained model and checkpoints from training.<br/>Defaults to True. | None | True |
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| residuals | bool | Whether to show residuals for the model. Defaults to False. | False | True |
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| forecast_only | bool | Whether to only show dates in the forecasting range. Defaults to False. | False | True |
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| start_date | Optional[datetime] | The starting date to perform analysis, data before this is trimmed. Defaults to None. | None | True |
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| end_date | Optional[datetime] | The ending date to perform analysis, data after this is trimmed. Defaults to None. | None | True |
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| naive | bool | Whether to show the naive baseline. This just assumes the closing price will be the same<br/>as the previous day's closing price. Defaults to False. | False | True |
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| external_axes | Optional[List[plt.axes]] | External axes to plot on | None | True |
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---
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## Returns
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This function does not return anything
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---
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</TabItem>
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</Tabs> |