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102 lines
4.5 KiB
Markdown
Vendored
102 lines
4.5 KiB
Markdown
Vendored
---
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title: theta
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description: This page provides detailed information on how to use the 'Theta' model
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and 'Theta' chart for financial forecasting with OpenBB. It explains various parameters
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for accurate prediction and chart display, alongside respective source code examples.
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keywords:
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- Theta model
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- Theta chart
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- Forecasting
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- Financial prediction
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- Source code
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- Model parameters
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- Chart display
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---
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import HeadTitle from '@site/src/components/General/HeadTitle.tsx';
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<HeadTitle title="forecast.theta - 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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Performs Theta forecasting
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Source Code: [[link](https://github.com/OpenBB-finance/OpenBBTerminal/tree/main/openbb_terminal/forecast/theta_model.py#L29)]
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```python
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openbb.forecast.theta(data: Union[pd.Series, pd.DataFrame], target_column: str = "close", seasonal: str = "M", seasonal_periods: int = 7, n_predict: int = 5, start_window: float = 0.85, forecast_horizon: int = 5)
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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, np.ndarray] | Input data. | None | False |
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| target_column | Optional[str]: | Target column to forecast. Defaults to "close". | close | True |
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| seasonal | str | Seasonal component. One of [N, A, M]<br/>Defaults to MULTIPLICATIVE. | M | True |
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| seasonal_periods | int | Number of seasonal periods in a year<br/>If not set, inferred from frequency of the series. | 7 | True |
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| n_predict | int | Number of days to forecast | 5 | True |
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| start_window | float | Size of sliding window from start of timeseries and onwards | 0.85 | True |
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| forecast_horizon | int | Number of days to forecast when backtesting and retraining historical | 5 | 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], float, float, type[Theta]] | Adjusted Data series,<br/>Historical forecast by best theta,<br/>list of Predictions,<br/>Mean average precision error,<br/>Best Theta,<br/>Theta Model. |
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---
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</TabItem>
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<TabItem value="view" label="Chart">
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Display Theta forecast
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Source Code: [[link](https://github.com/OpenBB-finance/OpenBBTerminal/tree/main/openbb_terminal/forecast/theta_view.py#L21)]
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```python
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openbb.forecast.theta_chart(data: Union[pd.DataFrame, pd.Series], target_column: str = "close", dataset_name: str = "", seasonal: str = "M", seasonal_periods: int = 7, n_predict: int = 5, start_window: float = 0.85, forecast_horizon: int = 5, 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, np.array] | Data to forecast | None | False |
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| target_column | Optional[str]: | Target column to forecast. Defaults to "close". | close | True |
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| dataset_name str | None | The name of the ticker to be predicted | None | True |
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| seasonal | str | Seasonal component. One of [N, A, M]<br/>Defaults to MULTIPLICATIVE. | M | True |
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| seasonal_periods | int | Number of seasonal periods in a year<br/>If not set, inferred from frequency of the series. | 7 | True |
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| n_predict | int | Number of days to forecast | 5 | True |
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| start_window | float | Size of sliding window from start of timeseries and onwards | 0.85 | True |
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| forecast_horizon | int | Number of days to forecast when backtesting and retraining historical | 5 | 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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