国防科技大学学报2025，Vol.47Issue(3)：32-40,9.DOI:10.11887/j.cn.202503004
面向缺失多元时间序列的图神经网络异常检测算法
Anomaly detection algorithm based on graph neural network for missing multivariate time series
摘要
Abstract
Addressing the issue of anomaly detection on missing multivariate time series data in real IoT(Internet of things)environments,a novel method on multivariate time series anomaly detection algorithm intergrated with graph embedding of missing information was proposed.Using a joint learning framework of pre-interpolation and anomaly detection task fusion,a GNN(graph neural network)pre-interpolation module based on time series Gaussian kernel function was designed to realize the joint optimization of pre-interpolation and anomaly detection task.A graph structure learning method for embedding missing information in time series data was proposed,using graph attention mechanism to fuse missing information masking matrix and spatiotemporal feature vectors,effectively modeling the potential connections of missing data distribution in multivariate time series.The performance of the algorithm was verified on real IoT sensor datasets.Experimental results prove that the proposed method significantly outperform the mainstream two-stage methods on the task of missing multivariate time series anomaly detection.The comparative experiment of the pre-interpolation module fully prove the effectiveness of the GNN pre-interpolation layer based on the Gaussian kernel function.关键词
多元时间序列/异常检测/图神经网络/预插值Key words
multivariate time series/anomaly detection/graph neural network/pre-interpolation分类
计算机与自动化引用本文复制引用
高杨,王新宇,贺达,宋明黎,周春燕..面向缺失多元时间序列的图神经网络异常检测算法[J].国防科技大学学报,2025,47(3):32-40,9.基金项目
浙江省"领雁"研发攻关计划资助项目(2024C01114) （2024C01114）
国家自然科学基金联合基金重点资助项目(U20B2066) （U20B2066）
