An Anomaly Detection System for High-Dimensional Industry Time Series Data Based on GNN and Apache Flink

Abstract

Intelligent systems have been widely used in various fields. They generate a large number of high-dimensional time series monitoring data in the process of operation, which often hide various potential abnormal conditions, which bring hidden dangers to the stable operation of the system. Existing anomaly detection methods mainly focus on the sequence characteristics of time series data, but often ignore the correlation between different variables of multivariate data, and the detection efficiency is low when facing high-dimensional time series data. To solve the above problems, we propose a deep anomaly detection method based on graph neural network, and combined with the big data computing framework Apache Flink, we construct a real-time anomaly detection system for large-scale high-dimensional time series data. Experimental results on SWaT and WADI show that our proposed method can accurately detect anomalies in multivariate time series data, and can perform low-latency real-time anomaly detection on high-dimensional industrial streaming data.