rnn
- Model
- Chart
Perform RNN forecasting
Source Code: [link]
openbb.forecast.rnn(data: Union[pd.Series, pd.DataFrame], target_column: str = "close", n_predict: int = 5, train_split: float = 0.85, forecast_horizon: int = 5, model_type: str = "LSTM", hidden_dim: int = 20, dropout: float = 0.0, batch_size: int = 32, n_epochs: int = 100, learning_rate: float = 0.001, model_save_name: str = "rnn_model", training_length: int = 20, input_chunk_size: int = 14, 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 |
| n_predict | int | Days to predict. Defaults to 5. | 5 | True |
| target_column | str | Target column to forecast. Defaults to "close". | close | True |
| train_split | float | Train/val split. Defaults to 0.85. | 0.85 | True |
| forecast_horizon | int | Forecast horizon when performing historical forecasting. Defaults to 5. | 5 | True |
| model_type | str | Either a string specifying the RNN module type ("RNN", "LSTM" or "GRU"). Defaults to "LSTM". | LSTM | True |
| hidden_dim | int | Size for feature maps for each hidden RNN layer.. Defaults to 20. | 20 | True |
| dropout | float | Fraction of neurons affected by Dropout. Defaults to 0.0. | 0.0 | True |
| batch_size | int | Number of time series (input and output sequences) used in each training pass. Defaults to 32. | 32 | 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". | rnn_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[RNNModel]] | Adjusted Data series, Historical forecast by best RNN model, list of Predictions, Mean average precision error, Best RNN Model |
Display RNN forecast
Source Code: [link]
openbb.forecast.rnn_chart(data: Union[pd.DataFrame, pd.Series], target_column: str = "close", dataset_name: str = "", n_predict: int = 5, train_split: float = 0.85, forecast_horizon: int = 5, model_type: str = "LSTM", hidden_dim: int = 20, dropout: float = 0.0, batch_size: int = 32, n_epochs: int = 100, learning_rate: float = 0.001, model_save_name: str = "rnn_model", training_length: int = 20, input_chunk_size: int = 14, 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 |
| dataset_name | str | The name of the ticker to be predicted | True | |
| n_predict | int | Days to predict. Defaults to 5. | 5 | True |
| target_column | str | Target column to forecast. Defaults to "close". | close | True |
| train_split | float | Train/val split. Defaults to 0.85. | 0.85 | True |
| forecast_horizon | int | Forecast horizon when performing historical forecasting. Defaults to 5. | 5 | True |
| model_type | str | Either a string specifying the RNN module type ("RNN", "LSTM" or "GRU"). Defaults to "LSTM". | LSTM | True |
| hidden_dim | int | Size for feature maps for each hidden RNN layer.. Defaults to 20. | 20 | True |
| dropout | float | Fraction of neurons affected by Dropout. Defaults to 0.0. | 0.0 | True |
| batch_size | int | Number of time series (input and output sequences) used in each training pass. Defaults to 32. | 32 | 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". | rnn_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 |
| forecast_only | bool | Whether to only show dates in the forecasting range. Defaults to False. | False | 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 |
| 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 predictions. Defaults to False. | False | True |
| metric | str | The metric to use for the forecast. 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