Deep probabilistic graphical modeling for robust multivariate time series anomaly detection with missing data

Yang J.; Yue Z.; Yuan Y.

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Deep probabilistic graphical modeling for robust multivariate time series anomaly detection with missing data

机译：Deep probabilistic graphical modeling for robust multivariate time series anomaly detection with missing data

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? 2023 Elsevier LtdMultivariate time series anomaly detection with missing data is one of the most pending issues for industrial monitoring. Due to scarcity of labeled anomalies, most advanced data-driven anomaly detection approaches fall in the unsupervised learning paradigm. As a premise in the presence of missing data, one needs to improve the data quality through data imputation with a separate model. Our concern lies in the consistency between data imputation and unsupervised learning for robust anomaly detection, regarding accurately discovering the spatiotemporal dependence among multiple variables over time. However, the existing practice tends to overlook this consistency and decouple the training process for these two closely linked tasks. This article novelly proposes a probabilistic multivariate time series anomaly detection framework that unifies data imputation and unsupervised learning. A deep probabilistic graphical model abbreviated SCNF is first devised for unsupervised density estimation. A tailored expectation maximization-based optimization scheme is then developed to achieve the joint training of data imputation and unsupervised learning with missing data. The efficacy is experimentally corroborated in several industrial applications, including chemical process, water treatment and network traffic. Briefly, the joint training framework enhances the AUROC of SCNF by averagely 6.34 for three applications under 50 data missing rate.

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来源
《Reliability engineering & system safety》 |2023年第10期|1.1-1.13|共13页
作者
Yang J.; Yue Z.; Yuan Y.;
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作者单位

Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
中图分类安全科学;
关键词
Anomaly detection; Expectation maximization; Missing data; Multivariate time series; Probabilistic graphical model;

Deep probabilistic graphical modeling for robust multivariate time series anomaly detection with missing data

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