A Stitch in Time Saves Nine: Enabling Early Anomaly Detection with Correlation Analysis

Abstract

Early detection of anomalies from sensor-based Multivariate Time Series (MTS) is vital for timely response to the signs of operation failures and errors. While many interesting works have been done toward solving this problem, existing methods typically detect such anomalies as outliers by making certain assumptions that allow efficient and easily understandable solutions to be used but might not be applicable to time series. Meanwhile, unsupervised deep learning-based methods might be highly accurate but often lead to challenges for real-time industrial scenarios, e.g., requiring a large amount of training data and producing unstable output. In this paper, we propose a new approach, CAD, to detect anomalies from sensor-based MTS. We aim to leverage the latent correlations between sensors by first converting the MTS into a sequence of Time-Series Graphs (TSGs) that connect sensors to their highly correlated neighbors within a certain time period. Then, we track the unusual correlation variations between sensors on the sequence of TSGs. By analyzing the correlation variations with a theoretical guarantee, CAD can detect the time of occurrence for the anomalies simultaneously with the sensors that are affected as early as possible. Extensive experiments over eight real-world datasets show that CAD is effective, scalable, yet stable compared to nine state-of-the-art methods while keeping comparable efficiency. Moreover, it maintains above 85% accuracy on large-scale datasets with over 1,000 sensors. Notably, CAD can determine relevant sensors in a very early stage of the anomaly so that timely predictive maintenance can be done. The code is available at https://github.com/YihaoAng/CAD.