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126 lines
7.6 KiB
Markdown
Vendored
126 lines
7.6 KiB
Markdown
Vendored
---
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title: tcn
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description: This documentation page provides information on how to use the 'tcn'
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functionality of OpenBBTerminal. This enables you to perform Temporal Convolutional
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Network (TCN) forecasting and display the forecast using OpenBB Platform's resources.
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keywords:
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- Temporal Convolutional Network
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- TCN forecasting
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- OpenBB platform
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- tcn_model.py
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- tcn_view.py
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- time series prediction
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- Forecast visualization
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- Source code
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- Predictive analytics
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---
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import HeadTitle from '@site/src/components/General/HeadTitle.tsx';
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<HeadTitle title="forecast.tcn - 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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Perform TCN forecasting
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Source Code: [[link](https://github.com/OpenBB-finance/OpenBBTerminal/tree/main/openbb_terminal/forecast/tcn_model.py#L20)]
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```python
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openbb.forecast.tcn(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, dropout: float = 0.1, num_filters: int = 6, weight_norm: bool = True, dilation_base: int = 2, n_epochs: int = 100, learning_rate: float = 0.001, batch_size: int = 800, model_save_name: str = "tcn_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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| target_column | str | Target column to forecast. Defaults to "close". | close | True |
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| n_predict | int | Days to predict. Defaults to 5. | 5 | 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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| dropout | float | Fraction of neurons affected by Dropout. Defaults to 0.1. | 0.1 | True |
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| num_filters | int | The number of filters in a convolutional layer of the TCN. Defaults to 6. | 6 | True |
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| weight_norm | bool | Boolean value indicating whether to use weight normalization. Defaults to True. | True | True |
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| dilation_base | int | The base of the exponent that will determine the dilation on every level. Defaults to 2. | 2 | True |
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| batch_size | int | Number of time series (input and output sequences) used in each training pass. Defaults to 32. | 800 | 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". | tcn_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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---
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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[TCNModel]] | Adjusted Data series,<br/>Historical forecast by best RNN model,<br/>list of Predictions,<br/>Mean average precision error,<br/>Best TCN Model. |
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---
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</TabItem>
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<TabItem value="view" label="Chart">
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Display TCN forecast
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Source Code: [[link](https://github.com/OpenBB-finance/OpenBBTerminal/tree/main/openbb_terminal/forecast/tcn_view.py#L20)]
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```python
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openbb.forecast.tcn_chart(data: Union[pd.DataFrame, pd.Series], 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, dropout: float = 0.1, num_filters: int = 6, weight_norm: bool = True, dilation_base: int = 2, n_epochs: int = 100, learning_rate: float = 0.001, batch_size: int = 800, model_save_name: str = "tcn_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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| target_column | str | Target column to forecast. Defaults to "close". | close | True |
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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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| 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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| dropout | float | Fraction of neurons affected by Dropout. Defaults to 0.1. | 0.1 | True |
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| num_filters | int | The number of filters in a convolutional layer of the TCN. Defaults to 6. | 6 | True |
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| weight_norm | bool | Boolean value indicating whether to use weight normalization. Defaults to True. | True | True |
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| dilation_base | int | The base of the exponent that will determine the dilation on every level. Defaults to 2. | 2 | True |
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| batch_size | int | Number of time series (input and output sequences) used in each training pass. Defaults to 32. | 800 | 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". | tcn_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. 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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