mirror of
https://github.com/OpenBB-finance/OpenBB.git
synced 2026-11-03 07:51:49 +00:00
4ad995fdd0
* 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>
104 lines
4.5 KiB
Markdown
Vendored
104 lines
4.5 KiB
Markdown
Vendored
---
|
|
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
|
|
---
|
|
|
|
import HeadTitle from '@site/src/components/General/HeadTitle.tsx';
|
|
|
|
<HeadTitle title="forecast.autoarima - Reference | OpenBB SDK Docs" />
|
|
|
|
import Tabs from '@theme/Tabs';
|
|
import TabItem from '@theme/TabItem';
|
|
|
|
<Tabs>
|
|
<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. |
|
|
---
|
|
|
|
</TabItem>
|
|
<TabItem value="view" label="Chart">
|
|
|
|
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
|
|
|
|
---
|
|
|
|
</TabItem>
|
|
</Tabs>
|