---
title: autoselect
description: This page provides detailed information about autoselect feature in OpenBB
  Terminal with its source code. It outlines parameters and returns of statistical
  forecasting model and autoselect chart module in python.
keywords:
- autoselect
- statistical forecasting
- parameters
- returns
- forecasting model
- chart
---

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

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

import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';

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

Performs Automatic Statistical forecasting

Source Code: [[link](https://github.com/OpenBB-finance/OpenBBTerminal/tree/main/openbb_terminal/forecast/autoselect_model.py#L32)]

```python
openbb.forecast.autoselect(data: Union[pd.Series, pd.DataFrame], target_column: str = "close", seasonal_periods: int = 7, 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 |
| 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 |
| 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[float], StatsForecast, Union[int, str]] | list[np.ndarray] - Adjusted Data series<br/>list[np.ndarray] - List of historical fcast values<br/>list[np.ndarray] - List of predicted fcast values<br/>Optional[float] - precision<br/>StatsForecast - Fit ETS model object.<br/>Union[int, str] - Best model |
---

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

Display Automatic Statistical Forecasting Model

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

```python
openbb.forecast.autoselect_chart(data: Union[pd.DataFrame, pd.Series], target_column: str = "close", dataset_name: str = "", 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)
```

---

## 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 |
| seasonal_periods | int | Number of seasonal periods in a year<br/>If not set, inferred from frequency of the series. | 7 | 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

---

</TabItem>
</Tabs>