EADS: An Early Anomaly Detection System for Sensor-Based Multivariate Time Series

Yihao Ang, Qiang Huang, Anthony K. H. Tung, Zhiyong Huang

Abstract: Early Anomaly Detection (AD) in sensor-based Multivariate Time Series (MTS) is crucial for addressing signs of operational failures. However, existing AD methods either struggle to identify anomalies at an early stage or lean heavily on intricate neural networks and extensive data for model training, compromising clarity and interpretability. To bridge this gap, we pioneered CAD, a novel AD framework based on correlation analysis. It harnesses Time-Series Graphs (TSGs) to monitor sensor correlation changes. By meticulously analyzing these changes, CAD excels in ascertaining the precise time of anomalies and identifying the implicated sensors. In this demonstration, we introduce EADS, an Early Anomaly Detection System built upon CAD for sensor-based MTS. We navigate multiple scenarios to illustrate the prowess of EADS in serving as an early AD benchmark platform, offering insightful abnormal time interpretability, and facilitating timely predictive maintenance. The source code is available at https://github.com/YihaoAng/EADS/.