基于Transformer GAN架构的多变量时间序列异常检测

Transformer-GAN architecture for anomaly detection in multivariate time series

基于过程中实时采集的多变量时序关联数据进行异常检测是预防工业过程事故、保障系统安全的关键环节之一.然而,工业多变量时间序列异常检测仍面临如下两大难题:(1)时序数据变量间复杂的非线性关联特性缺乏有效的表达方法;(2)正常/异常分布极度不均衡的时间序列间复杂的相关性有待深入挖掘.本文提出一种新的基于多变量时间序列的无监督异常检测方法 ——基于Transformer GAN的多变量时间序列异常检测方法 (TGAN-MTSAD). TGAN-MTSAD采用Transformer网络作为生成对抗网络的基本模型,引入了图注意力层以自动学习时序多元变量间的复杂依赖关系,还应用了patch技巧使模型能够有效捕捉时间窗口内的异常细节信息,并提出了基于重构误差与鉴别误差相结合的异常分数计算方法.采用3个真实世界的数据集对所提方法进行了大量的性能验证与对比实验分析.结果表明, TGAN-MTSAD可以有效检测过程中的时序异常,在大多数情况下优于基线方法,并且具有良好的可解释性,可用于复杂工业异常诊断.

Anomaly detection based on multivariate time series correlation data collected in real time during the process is one of the key aspects of preventing industrial process accidents and ensuring system safety.However,industrial multivariate time series anomaly detection still faces two major challenges:(1)the complex nonlinear correlation characteristics among time series data variables lack an effective representation method,and(2)the complex correlation among time series with highly unbalanced normal/abnormal distribution needs to be deeply explored.In this paper,we propose a Transformer generative adversarial networks(GAN)-based multivariate time series anomaly detection method(TGAN-MTSAD).TGAN-MTSAD employs transformer neural networks as the base model of GAN and introduces a graph attention layer to automatically learn complex dependencies among time series multivariate variables.It applies a patch trick to enable the model to effectively capture anomaly details within a time window.An anomaly score calculation method is proposed based on a combination of reconstruction and discrimination errors.An extensive performance validation and comparative experimental analysis of the proposed method were carried out using three real-world datasets.The results show that TGANMTSAD can effectively detect in-process timing anomalies,outperforming the baseline method in most cases,and has good interpretability for complex industrial anomaly detection.