Machine Learning for Complex Systems with Abnormal Pattern by Exception Maximization Outlier Detection Method
This work addresses outlier detection for real-time monitoring in engineering and economic systems, but it is incremental as it builds on existing probabilistic models.
The paper tackles outlier detection in complex systems by proposing the Exception Maximization Outlier Detection Method (EMODM), a fast online approach using a two-state Gaussian mixture model, and demonstrates its effectiveness on synthetic data and real-world applications like detecting short circuits in a three-phase inverter and COVID-19 anomalies in U.S. unemployment data.
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.