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* add first iteration of content for Terminal Pro * Update index.md * Update dashboards.md * Update folders.md * Update grouping.md * Update report.md * Update templates.md * Update quick-start.md * Update index.md * Update index.md * Update home.md * Update news.md * Update index.md * Update index.md * Update index.md * Update data-provider.md * Update chat-with-widget.md * Update data-manipulation.md * Update forecasting.md * fix: images self closing tags * fix: only pro visitors can see pro * inter font * improve SEO content and restructuring * add new package for toggle on tutorials * move folder around * improve main page of each prod * small updates and improvements * fix logo above * small improvement in headtitle for portfolio funcs * fix typos from OpenAI's GPT-4 * fix _category_ json * remove generic SEO words * add script that generates SEO for documentation * add missing file * remove sdk warning message * fix links for andrew --------- Co-authored-by: jose-donato <zmcdonato@gmail.com> Co-authored-by: jose-donato <43375532+jose-donato@users.noreply.github.com> Co-authored-by: andrewkenreich <andrew.kenreich@gmail.com>
129 lines
8.5 KiB
Markdown
Vendored
129 lines
8.5 KiB
Markdown
Vendored
---
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title: tft
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description: This Docusaurus page provides in-depth information on the use of Temporal
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Fusion Transformer for forecasting purposes, as implemented through OpenBB finance.
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It includes details on the model's parameters, usage, and output, along with the
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source code and chart visualization capabilities. The page is of high relevance
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for developers, data analysts, and professionals dealing with predictive modeling
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and data analysis tasks.
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keywords:
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- Temporal Fusion Transformer forecasting
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- OpenBB finance
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- Temporal Fusion Transformer chart
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- Machine Learning
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- Data analysis
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- Predictive modeling
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- Predictive analytics
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- Data modeling
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---
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import HeadTitle from '@site/src/components/General/HeadTitle.tsx';
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<HeadTitle title="forecast.tft - Reference | OpenBB SDK Docs" />
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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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>
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