Machine Learning for Complex Systems with Abnormal Pattern by Exception Maximization Outlier Detection Method

TL;DR

This paper introduces EMODM, a fast online outlier detection method using probabilistic models, effective in real-time complex system anomaly detection across various real-world datasets.

Contribution

The paper presents a novel two-state Gaussian mixture model-based outlier detection method that operates efficiently in real-time without prior distribution assumptions.

Findings

Successfully detected short circuit patterns in circuit systems.

Identified abnormal unemployment periods during COVID-19.

Demonstrated high accuracy and effectiveness on real-world data.

Abstract

This paper proposes a novel fast online methodology for outlier detection called the exception maximization outlier detection method(EMODM), which employs probabilistic models and statistical algorithms to detect abnormal patterns from the outputs of complex systems. The EMODM is based on a two-state Gaussian mixture model and demonstrates strong performance in probability anomaly detection working on real-time raw data rather than using special prior distribution information. We confirm this using the synthetic data from two numerical cases. For the real-world data, we have detected the short circuit pattern of the circuit system using EMODM by the current and voltage output of a three-phase inverter. The EMODM also found an abnormal period due to COVID-19 in the insured unemployment data of 53 regions in the United States from 2000 to 2024. The application of EMODM to these two real-life datasets demonstrated the effectiveness and accuracy of our algorithm.