tcn
- Model
- Chart
Perform TCN forecasting
Source Code: [link]
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]
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