Outlier Detection

Purpose

Outlier Detection supports the automation of outlier detection in a data sample or in outputs such as MC simulation results, using a mix of traditional statistical approaches and ML approaches. Its purpose is to help identify unusual values or patterns and support documentation of the work performed.

Primary users

The primary user is not specified. The agent is associated with AQS and owned by Ronan Davit, and it is designed for users who need to review and validate outlier detection results.

Where it fits (process/stage/trigger)

This agent fits into a cross-industry analytical workflow when a data sample or simulation output needs to be checked for outliers. It is triggered when outlier detection must be automated and when the detected results require human-in-the-loop validation.

Key capabilities / workflow

The agent analyzes a data sample or simulation outputs, applies a mix of traditional statistical approaches and ML approaches, identifies outlier detection results, supports human-in-the-loop validation through a UI, and documents the work performed.

Inputs

Typical inputs are data samples or simulation outputs such as MC simulation outputs. Other input details, datasets, credentials, or links are not specified.

Outputs / Deliverables

The outputs include outlier detection results, a human-in-the-loop UI to validate the results, and documentation of the work performed. Other deliverables are not specified.

Value

Outlier Detection helps automate a data quality and analytical review task that can otherwise be manual and time-consuming. It combines statistical and ML-based approaches with human validation to improve review efficiency while keeping validation and documentation in the process.