> For the complete documentation index, see [llms.txt](https://docs.apica.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.apica.io/data-management/overview-1/time-series-ai-ml/averaging.md).
# Averaging
The averaging feature adds a moving average of the target value column to the dataset. Two new columns are added to the dataset whose names start with MA (Moving Average). MA12 is added by default, and the second column is configurable. The configuration sets up the moving average window size. This feature is a simple mathematical implementation.
**Steps to configure averaging for a query:**
1. Navigate to Algorithms section inside the querybuilder .
2. Select a meaningful time-series column and a value column.
3. Select the Averaging option from the operations selection box.
4. An averaging configuration box appears. This box contains a dropdown called ma window using which the window size of the moving average can be set. The bigger the window size the more curvilinear the graph becomes
5. Click on Apply.
6. The resulting dataset contains two columns MA12 whose window size is 12 and another column with MA whose window is previously configured.
**Steps to visualize averaging for a query:**
1. Go to plot section .
2. Select the chart type as a line for better visualization.
3. Select the time column as the X column and the value column as the Y column. Add the moving average columns i.e. columns with the prefix MA, into the Y column multi-selection box.
4. Click on Apply button, A graph similar to the following picture must be rendered.
---
# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:
```
GET https://docs.apica.io/data-management/overview-1/time-series-ai-ml/averaging.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.