nbeats
- Model
- Chart
Perform NBEATS Forecasting
Source Code: [link]
openbb.forecast.nbeats(data: Union[pd.Series, pd.DataFrame], target_column: str = "close", n_predict: int = 5, past_covariates: Optional[str] = None, train_split: float = 0.85, forecast_horizon: int = 5, input_chunk_length: int = 14, output_chunk_length: int = 5, num_stacks: int = 10, num_blocks: int = 3, num_layers: int = 4, layer_widths: int = 512, batch_size: int = 800, n_epochs: int = 300, learning_rate: float = 0.001, model_save_name: str = "nbeats_model", force_reset: bool = True, save_checkpoints: bool = True, metric: str = "mape")
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 |
| num_stacks | int | The number of stacks that make up the whole model. Defaults to 10. | 10 | True |
| num_blocks | int | The number of blocks making up every stack. Defaults to 3. | 3 | True |
| num_layers | int | The number of fully connected layers preceding the final forking layers in each block of every stack. Defaults to 4. | 4 | True |
| layer_widths | int | Determines the number of neurons that make up each fully connected layer in each block of every stack. Defaults to 512. | 512 | 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. | 300 | True |
| learning_rate | float | Defaults to 1e-3. | 0.001 | True |
| model_save_name | str | Name for model. Defaults to "brnn_model". | nbeats_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 |
| metric | str | Metric to use for model selection. Defaults to "mape". | mape | True |
Returns
| Type | Description |
|---|---|
| Tuple[List[TimeSeries], List[TimeSeries], List[TimeSeries], Optional[float], type[NBEATSModel]] | Adjusted Data series, Historical forecast by best RNN model, list of Predictions, Mean average precision error, Best NBEATS Model. |
Display NBEATS forecast
Source Code: [link]
openbb.forecast.nbeats_chart(data: Union[pd.DataFrame, pd.Series], target_column: str = "close", dataset_name: str = "", n_predict: int = 5, past_covariates: Optional[str] = None, train_split: float = 0.85, forecast_horizon: int = 5, input_chunk_length: int = 14, output_chunk_length: int = 5, num_stacks: int = 10, num_blocks: int = 3, num_layers: int = 4, layer_widths: int = 512, n_epochs: int = 300, learning_rate: float = 0.001, batch_size: int = 800, model_save_name: str = "nbeats_model", force_reset: bool = True, save_checkpoints: bool = True, export: str = "", sheet_name: Optional[str] = None, 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, metric: str = "mape", external_axes: bool = False)
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 |
| num_stacks | int | The number of stacks that make up the whole model. Defaults to 10. | 10 | True |
| num_blocks | int | The number of blocks making up every stack. Defaults to 3. | 3 | True |
| num_layers | int | The number of fully connected layers preceding the final forking layers in each block of every stack. Defaults to 4. | 4 | True |
| layer_widths | int | Determines the number of neurons that make up each fully connected layer in each block of every stack. Defaults to 512. | 512 | 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. | 300 | True |
| learning_rate | float | Defaults to 1e-3. | 0.001 | True |
| model_save_name | str | Name for model. Defaults to "brnn_model". | nbeats_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 |
| sheet_name | str | Optionally specify the name of the sheet the data is exported to. | None | 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 |
| export_pred_raw | bool | Whether to export the raw predicted values. Defaults to False. | False | True |
| metric | str | Metric to use for evaluation. Defaults to "mape". | mape | True |
| external_axes | bool | Whether to return the figure object or not, by default False | False | True |
Returns
This function does not return anything