--- title: tcn description: This documentation page provides information on how to use the 'tcn' functionality of OpenBBTerminal. This enables you to perform Temporal Convolutional Network (TCN) forecasting and display the forecast using OpenBB Platform's resources. keywords: - Temporal Convolutional Network - TCN forecasting - OpenBB platform - tcn_model.py - tcn_view.py - time series prediction - Forecast visualization - Source code - Predictive analytics --- import HeadTitle from '@site/src/components/General/HeadTitle.tsx'; import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; Perform TCN forecasting Source Code: [[link](https://github.com/OpenBB-finance/OpenBBTerminal/tree/main/openbb_terminal/forecast/tcn_model.py#L20)] ```python 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) ``` --- ## Parameters | Name | Type | Description | Default | Optional | | ---- | ---- | ----------- | ------- | -------- | | data | Union[pd.Series, pd.DataFrame] | Input Data | None | False | | target_column | str | Target column to forecast. Defaults to "close". | close | True | | n_predict | int | Days to predict. Defaults to 5. | 5 | True | | train_split | float | Train/val split. Defaults to 0.85. | 0.85 | True | | past_covariates | str | Multiple secondary columns to factor in when forecasting. Defaults to None. | None | True | | forecast_horizon | int | Forecast horizon when performing historical forecasting. Defaults to 5. | 5 | True | | input_chunk_length | int | Number of past time steps that are fed to the forecasting module at prediction time. Defaults to 14. | 14 | True | | output_chunk_length | int | The length of the forecast of the model. Defaults to 5. | 5 | True | | dropout | float | Fraction of neurons affected by Dropout. Defaults to 0.1. | 0.1 | True | | num_filters | int | The number of filters in a convolutional layer of the TCN. Defaults to 6. | 6 | True | | weight_norm | bool | Boolean value indicating whether to use weight normalization. Defaults to True. | True | True | | dilation_base | int | The base of the exponent that will determine the dilation on every level. Defaults to 2. | 2 | True | | batch_size | int | Number of time series (input and output sequences) used in each training pass. Defaults to 32. | 800 | True | | n_epochs | int | Number of epochs over which to train the model. Defaults to 100. | 100 | True | | learning_rate | float | Defaults to 1e-3. | 0.001 | True | | model_save_name | str | Name for model. Defaults to "brnn_model". | tcn_model | True | | force_reset | bool | 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 | Whether or not to automatically save the untrained model and checkpoints from training.
Defaults to True. | True | True | --- ## Returns | Type | Description | | ---- | ----------- | | Tuple[List[TimeSeries], List[TimeSeries], List[TimeSeries], Optional[float], type[TCNModel]] | Adjusted Data series,
Historical forecast by best RNN model,
list of Predictions,
Mean average precision error,
Best TCN Model. | --- Display TCN forecast Source Code: [[link](https://github.com/OpenBB-finance/OpenBBTerminal/tree/main/openbb_terminal/forecast/tcn_view.py#L20)] ```python 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) ``` --- ## Parameters | Name | Type | Description | Default | Optional | | ---- | ---- | ----------- | ------- | -------- | | data | Union[pd.Series, pd.DataFrame] | Input Data | None | False | | target_column | str | Target column to forecast. Defaults to "close". | close | True | | dataset_name | str | The name of the ticker to be predicted | | True | | n_predict | int | Days to predict. Defaults to 5. | 5 | True | | train_split | float | Train/val split. Defaults to 0.85. | 0.85 | True | | past_covariates | str | Multiple secondary columns to factor in when forecasting. Defaults to None. | None | True | | forecast_horizon | int | Forecast horizon when performing historical forecasting. Defaults to 5. | 5 | True | | input_chunk_length | int | Number of past time steps that are fed to the forecasting module at prediction time. Defaults to 14. | 14 | True | | output_chunk_length | int | The length of the forecast of the model. Defaults to 5. | 5 | True | | dropout | float | Fraction of neurons affected by Dropout. Defaults to 0.1. | 0.1 | True | | num_filters | int | The number of filters in a convolutional layer of the TCN. Defaults to 6. | 6 | True | | weight_norm | bool | Boolean value indicating whether to use weight normalization. Defaults to True. | True | True | | dilation_base | int | The base of the exponent that will determine the dilation on every level. Defaults to 2. | 2 | True | | batch_size | int | Number of time series (input and output sequences) used in each training pass. Defaults to 32. | 800 | True | | n_epochs | int | Number of epochs over which to train the model. Defaults to 100. | 100 | True | | learning_rate | float | Defaults to 1e-3. | 0.001 | True | | model_save_name | str | Name for model. Defaults to "brnn_model". | tcn_model | True | | force_reset | bool | 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 | Whether or not to automatically save the untrained model and checkpoints from training. Defaults to True. | True | True | | export | str | Format to export data | | 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 ---