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* add first iteration of content for Terminal Pro * Update index.md * Update dashboards.md * Update folders.md * Update grouping.md * Update report.md * Update templates.md * Update quick-start.md * Update index.md * Update index.md * Update home.md * Update news.md * Update index.md * Update index.md * Update index.md * Update data-provider.md * Update chat-with-widget.md * Update data-manipulation.md * Update forecasting.md * fix: images self closing tags * fix: only pro visitors can see pro * inter font * improve SEO content and restructuring * add new package for toggle on tutorials * move folder around * improve main page of each prod * small updates and improvements * fix logo above * small improvement in headtitle for portfolio funcs * fix typos from OpenAI's GPT-4 * fix _category_ json * remove generic SEO words * add script that generates SEO for documentation * add missing file * remove sdk warning message * fix links for andrew --------- Co-authored-by: jose-donato <zmcdonato@gmail.com> Co-authored-by: jose-donato <43375532+jose-donato@users.noreply.github.com> Co-authored-by: andrewkenreich <andrew.kenreich@gmail.com>
124 lines
7.7 KiB
Markdown
Vendored
124 lines
7.7 KiB
Markdown
Vendored
---
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title: nbeats
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description: Learn how to perform NBEATS Forecasting with OpenBB Finance. Explore
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the parameters, their descriptions, and learn how to properly tune them for optimal
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forecasting results.
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keywords:
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- NBEATS Forecasting
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- OpenBB Finance
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- forecast parameters
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- training models
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- machine learning
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- data science
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- time series prediction
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---
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import HeadTitle from '@site/src/components/General/HeadTitle.tsx';
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<HeadTitle title="forecast.nbeats - 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 NBEATS Forecasting
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Source Code: [[link](https://github.com/OpenBB-finance/OpenBBTerminal/tree/main/openbb_terminal/forecast/nbeats_model.py#L21)]
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```python
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openbb.forecast.nbeats(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, num_stacks: int = 10, num_blocks: int = 3, num_layers: int = 4, layer_widths: int = 512, batch_size: int = 800, n_epochs: int = 100, learning_rate: float = 0.001, model_save_name: str = "nbeats_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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| num_stacks | int | The number of stacks that make up the whole model. Defaults to 10. | 10 | True |
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| num_blocks | int | The number of blocks making up every stack. Defaults to 3. | 3 | True |
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| num_layers | int | The number of fully connected layers preceding the final forking layers in each block<br/>of every stack. Defaults to 4. | 4 | True |
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| layer_widths | int | Determines the number of neurons that make up each fully connected layer in each block<br/>of every stack. Defaults to 512. | 512 | 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". | nbeats_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[NBEATSModel]] | Adjusted Data series,<br/>Historical forecast by best RNN model,<br/>list of Predictions,<br/>Mean average precision error,<br/>Best NBEATS Model. |
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---
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</TabItem>
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<TabItem value="view" label="Chart">
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Display NBEATS forecast
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Source Code: [[link](https://github.com/OpenBB-finance/OpenBBTerminal/tree/main/openbb_terminal/forecast/nbeats_view.py#L20)]
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```python
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openbb.forecast.nbeats_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, num_stacks: int = 10, num_blocks: int = 3, num_layers: int = 4, layer_widths: int = 512, n_epochs: int = 100, 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 = "", 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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| num_stacks | int | The number of stacks that make up the whole model. Defaults to 10. | 10 | True |
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| num_blocks | int | The number of blocks making up every stack. Defaults to 3. | 3 | True |
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| num_layers | int | The number of fully connected layers preceding the final forking layers in each block<br/>of every stack. Defaults to 4. | 4 | True |
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| layer_widths | int | Determines the number of neurons that make up each fully connected layer in each block<br/>of every stack. Defaults to 512. | 512 | True |
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| batch_size | int | Number of time series (input and output sequences) used in each training pass. Defaults<br/>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". | nbeats_model | True |
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| force_reset | bool | If set to True, any previously-existing model with the same name will be reset (all<br/>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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| 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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