---
title: expo
description: The page explores the functionalities and parameters of the 'expo' model
  and 'chart' functions used in Probabilistic Exponential Smoothing forecasting. These
  functions take data as input and perform operations like backtesting, retraining
  historical and prediction. Source code links are also shared.
keywords:
- Exponential Smoothing forecasting
- expo model function
- expo chart function
- Probabilistic forecasting
- data series operations
- parameters
---

import HeadTitle from '@site/src/components/General/HeadTitle.tsx';

<HeadTitle title="forecast.expo - Reference | OpenBB SDK Docs" />

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<TabItem value="model" label="Model" default>

Performs Probabilistic Exponential Smoothing forecasting

Source Code: [[link](https://github.com/OpenBB-finance/OpenBBTerminal/tree/main/openbb_terminal/forecast/expo_model.py#L34)]

```python
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)
```

---

## Parameters

| Name | Type | Description | Default | Optional |
| ---- | ---- | ----------- | ------- | -------- |
| data | Union[pd.Series, np.ndarray] | Input data. | None | False |
| target_column | Optional[str]: | Target column to forecast. Defaults to "close". | close | True |
| trend | str | Trend component.  One of [N, A, M]<br/>Defaults to ADDITIVE. | A | True |
| seasonal | str | Seasonal component.  One of [N, A, M]<br/>Defaults to ADDITIVE. | A | True |
| 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 |
| dampen | str | Dampen the function | F | True |
| n_predict | int | Number of days to forecast | 5 | True |
| start_window | float | Size of sliding window from start of timeseries and onwards | 0.85 | True |
| forecast_horizon | int | Number of days to forecast when backtesting and retraining historical | 5 | True |


---

## Returns

| Type | Description |
| ---- | ----------- |
| 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. |
---

</TabItem>
<TabItem value="view" label="Chart">

Display Probabilistic Exponential Smoothing forecast

Source Code: [[link](https://github.com/OpenBB-finance/OpenBBTerminal/tree/main/openbb_terminal/forecast/expo_view.py#L20)]

```python
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)
```

---

## Parameters

| Name | Type | Description | Default | Optional |
| ---- | ---- | ----------- | ------- | -------- |
| data | Union[pd.Series, np.array] | Data to forecast | None | False |
| dataset_name str | None | The name of the ticker to be predicted | None | True |
| target_column | Optional[str]: | Target column to forecast. Defaults to "close". | close | True |
| trend | str | Trend component.  One of [N, A, M]<br/>Defaults to ADDITIVE. | A | True |
| seasonal | str | Seasonal component.  One of [N, A, M]<br/>Defaults to ADDITIVE. | A | True |
| seasonal_periods | int | Number of seasonal periods in a year<br/>If not set, inferred from frequency of the series. | 7 | True |
| dampen | str | Dampen the function | F | True |
| n_predict | int | Number of days to forecast | 5 | True |
| start_window | float | Size of sliding window from start of timeseries and onwards | 0.85 | True |
| forecast_horizon | int | Number of days to forecast when backtesting and retraining historical | 5 | 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<br/>as the previous day's closing price. Defaults to False. | False | True |
| external_axes | Optional[List[plt.axes]] | External axes to plot on | None | True |


---

## Returns

This function does not return anything

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