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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>
106 lines
5.1 KiB
Markdown
Vendored
106 lines
5.1 KiB
Markdown
Vendored
---
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title: expo
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description: The page explores the functionalities and parameters of the 'expo' model
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and 'chart' functions used in Probabilistic Exponential Smoothing forecasting. These
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functions take data as input and perform operations like backtesting, retraining
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historical and prediction. Source code links are also shared.
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keywords:
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- Exponential Smoothing forecasting
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- expo model function
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- expo chart function
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- Probabilistic forecasting
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- data series operations
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- parameters
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---
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import HeadTitle from '@site/src/components/General/HeadTitle.tsx';
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<HeadTitle title="forecast.expo - 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 Probabilistic Exponential Smoothing forecasting
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Source Code: [[link](https://github.com/OpenBB-finance/OpenBBTerminal/tree/main/openbb_terminal/forecast/expo_model.py#L34)]
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```python
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openbb.forecast.expo(data: Union[pd.Series, pd.DataFrame], target_column: str = "close", trend: str = "A", seasonal: str = "A", seasonal_periods: int = 7, dampen: str = "F", 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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| trend | str | Trend component. One of [N, A, M]<br/>Defaults to ADDITIVE. | A | True |
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| seasonal | str | Seasonal component. One of [N, A, M]<br/>Defaults to ADDITIVE. | A | True |
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| seasonal_periods | int | Number of seasonal periods in a year (7 for daily data)<br/>If not set, inferred from frequency of the series. | 7 | True |
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| dampen | str | Dampen the function | F | 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], Optional[Union[float, ndarray]], ExponentialSmoothing] | Adjusted Data series,<br/>List of historical fcast values,<br/>List of predicted fcast values,<br/>Optional[float] - precision,<br/>Fit Prob. Expo model object. |
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---
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</TabItem>
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<TabItem value="view" label="Chart">
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Display Probabilistic Exponential Smoothing forecast
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Source Code: [[link](https://github.com/OpenBB-finance/OpenBBTerminal/tree/main/openbb_terminal/forecast/expo_view.py#L20)]
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```python
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openbb.forecast.expo_chart(data: Union[pd.DataFrame, pd.Series], target_column: str = "close", dataset_name: str = "", trend: str = "A", seasonal: str = "A", seasonal_periods: int = 7, dampen: str = "F", 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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| dataset_name str | None | The name of the ticker to be predicted | None | True |
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| target_column | Optional[str]: | Target column to forecast. Defaults to "close". | close | True |
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| trend | str | Trend component. One of [N, A, M]<br/>Defaults to ADDITIVE. | A | True |
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| seasonal | str | Seasonal component. One of [N, A, M]<br/>Defaults to ADDITIVE. | A | 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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| dampen | str | Dampen the function | F | 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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