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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>
135 lines
8.5 KiB
Markdown
Vendored
135 lines
8.5 KiB
Markdown
Vendored
---
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title: trans
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description: This section emphasizes on Transformer forecasting method implementation
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and visualization used in OpenBB Terminal. It explains functions like how to apply
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the approach on data, various parameters to adjust for better forecasting and display
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the results in chart.
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keywords:
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- Transformer Forecasting
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- Time-Series Prediction
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- Forecasting Parameters
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- Data Visualization
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- pandas series
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- pandas dataframe
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- Machine Learning
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- Activation Function
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- Multi-head Attention Mechanism
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- Encoder layers
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- Decoder layers
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- Feedforward Network
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---
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import HeadTitle from '@site/src/components/General/HeadTitle.tsx';
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<HeadTitle title="forecast.trans - 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 Transformer forecasting
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Source Code: [[link](https://github.com/OpenBB-finance/OpenBBTerminal/tree/main/openbb_terminal/forecast/trans_model.py#L20)]
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```python
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openbb.forecast.trans(data: Union[pd.Series, pd.DataFrame], target_column: str = "close", n_predict: int = 5, train_split: float = 0.85, past_covariates: str = None, forecast_horizon: int = 5, input_chunk_length: int = 14, output_chunk_length: int = 5, d_model: int = 64, nhead: int = 4, num_encoder_layers: int = 3, num_decoder_layers: int = 3, dim_feedforward: int = 512, activation: str = "relu", dropout: float = 0.0, batch_size: int = 32, n_epochs: int = 100, learning_rate: float = 0.001, model_save_name: str = "trans_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 | False |
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| n_predict | int | Days to predict. Defaults to 5. | 5 | True |
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| target_column | str | Target column to forecast. Defaults to "close". | close | True |
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| train_split | float | Train/val split. Defaults to 0.85. | 0.85 | True |
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| past_covariates | str | Multiple secondary columns to factor in when forecasting. Defaults to None. | None | True |
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| forecast_horizon | int | Forecast horizon when performing historical forecasting. Defaults to 5. | 5 | True |
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| input_chunk_length | int | Number of past time steps that are fed to the forecasting module at prediction time. Defaults to 14. | 14 | True |
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| output_chunk_length | int | The length of the forecast of the model. Defaults to 5. | 5 | True |
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| d_model | int | The number of expected features in the encoder/decoder inputs. Defaults to 64. | 64 | True |
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| nhead | int | The number of heads in the multi-head attention mechanism. Defaults to 4. | 4 | True |
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| num_encoder_layers | int | The number of encoder layers in the encoder. Defaults to 3. | 3 | True |
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| num_decoder_layers | int | The number of decoder layers in the encoder. Defaults to 3. | 3 | True |
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| dim_feedforward | int | The dimension of the feedforward network model. Defaults to 512. | 512 | True |
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| activation | str | The activation function of encoder/decoder intermediate layer, ‘relu’ or ‘gelu’. Defaults to 'relu'. | relu | True |
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| dropout | float | Fraction of neurons affected by Dropout. Defaults to 0.0. | 0.0 | True |
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| batch_size | int | Number of time series (input and output sequences) used in each training pass. Defaults to 32. | 32 | True |
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| n_epochs | int | Number of epochs over which to train the model. Defaults to 100. | 100 | True |
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| learning_rate | float | Defaults to 1e-3. | 0.001 | True |
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| model_save_name | str | Name for model. Defaults to "brnn_model". | trans_model | True |
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| force_reset | bool | If set to True, any previously-existing model with the same name will be reset (all checkpoints will be<br/>discarded). Defaults to True. | True | True |
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| save_checkpoints | bool | Whether or not to automatically save the untrained model and checkpoints from training. Defaults to True. | True | True |
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---
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## Returns
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| Type | Description |
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| ---- | ----------- |
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| Tuple[List[TimeSeries], List[TimeSeries], List[TimeSeries], Optional[float], type[TransformerModel]] | Adjusted Data series,<br/>Historical forecast by best RNN model,<br/>list of Predictions,<br/>Mean average precision error,<br/>Best transformer Model. |
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---
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</TabItem>
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<TabItem value="view" label="Chart">
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Display Transformer forecast
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Source Code: [[link](https://github.com/OpenBB-finance/OpenBBTerminal/tree/main/openbb_terminal/forecast/trans_view.py#L20)]
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```python
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openbb.forecast.trans_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, d_model: int = 64, nhead: int = 4, num_encoder_layers: int = 3, num_decoder_layers: int = 3, dim_feedforward: int = 512, activation: str = "relu", dropout: float = 0.1, batch_size: int = 16, n_epochs: int = 100, learning_rate: float = 0.001, model_save_name: str = "trans_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 | False |
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| dataset_name | str | The name of the ticker to be predicted | | True |
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| n_predict | int | Days to predict. Defaults to 5. | 5 | True |
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| target_column | str | Target column to forecast. Defaults to "close". | close | True |
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| train_split | float | Train/val split. Defaults to 0.85. | 0.85 | True |
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| past_covariates | str | Multiple secondary columns to factor in when forecasting. Defaults to None. | None | True |
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| forecast_horizon | int | Forecast horizon when performing historical forecasting. Defaults to 5. | 5 | True |
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| input_chunk_length | int | Number of past time steps that are fed to the forecasting module at prediction time. Defaults to 14. | 14 | True |
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| output_chunk_length | int | The length of the forecast of the model. Defaults to 5. | 5 | True |
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| d_model | int | The number of expected features in the encoder/decoder inputs. Defaults to 64. | 64 | True |
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| nhead | int | The number of heads in the multi-head attention mechanism. Defaults to 4. | 4 | True |
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| num_encoder_layers | int | The number of encoder layers in the encoder. Defaults to 3. | 3 | True |
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| num_decoder_layers | int | The number of decoder layers in the encoder. Defaults to 3. | 3 | True |
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| dim_feedforward | int | The dimension of the feedforward network model. Defaults to 512. | 512 | True |
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| activation | str | The activation function of encoder/decoder intermediate layer, ‘relu’ or ‘gelu’. Defaults to 'relu'. | relu | True |
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| dropout | float | Fraction of neurons affected by Dropout. Defaults to 0.1. | 0.1 | True |
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| batch_size_ | int | Number of time series (input and output sequences) used in each training pass. Defaults to 32. | None | True |
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| n_epochs | int | Number of epochs over which to train the model. Defaults to 100. | 100 | True |
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| model_save_name | str | Name for model. Defaults to "brnn_model". | trans_model | True |
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| force_reset | bool | If set to True, any previously-existing model with the same name will be reset<br/>(all checkpoints will be discarded). Defaults to True. | True | True |
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| save_checkpoints | bool | Whether or not to automatically save the untrained model and checkpoints from training.<br/>Defaults to True. | True | True |
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| export | str | Format to export data | | 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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