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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>
1.4 KiB
Vendored
1.4 KiB
Vendored
title, description, keywords
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| decompose | This documentation page provides details on the 'decompose' function in OpenBB's quantitative analysis module. The function executes seasonal decomposition on a specified DataFrame, returning decomposed results and filtered dataframes for cycle and trend. |
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import HeadTitle from '@site/src/components/General/HeadTitle.tsx';
Perform seasonal decomposition
Source Code: [link]
openbb.qa.decompose(data: pd.DataFrame, multiplicative: bool = False)
Parameters
| Name | Type | Description | Default | Optional |
|---|---|---|---|---|
| data | pd.DataFrame | Dataframe of targeted data | None | False |
| multiplicative | bool | Boolean to indicate multiplication instead of addition | False | True |
Returns
| Type | Description |
|---|---|
| Tuple[DecomposeResult, pd.DataFrame, pd.DataFrame] | DecomposeResult class from statsmodels (observed, seasonal, trend, residual, and weights), Filtered cycle DataFrame, Filtered trend DataFrame |