---
title: autoarima
description: This documentation page discusses the use of the automatic ARIMA (AutoARIMA)
  model for forecasting. It provides a detailed insight into the parameters involved,
  the return types, model structures, and links to the source code. This page is specifically
  valuable for individuals seeking understanding of OpenBB's financial forecasting
  abilities based on time series data using Python.
keywords:
- ARIMA
- Forecasting
- AutoARIMA
- TimeSeries
- Source Code
- Parameters
- Returns
- Model
- Chart
---

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

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

Performs Automatic ARIMA forecasting

Source Code: [[link](https://github.com/OpenBB-finance/OpenBBTerminal/tree/main/openbb_terminal/forecast/autoarima_model.py#L27)]

```python
openbb.forecast.autoarima(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], float, StatsForecast] | Adjusted Data series,<br/>List of historical fcast values,<br/>List of predicted fcast values,<br/>Optional[float] - precision<br/>Fit AutoaRIMA model object. |
---

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Display Automatic ARIMA model.

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

```python
openbb.forecast.autoarima_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

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