Multivariate time series anomaly detection with variational autoencoder and spatial–temporal graph network

Effective anomaly detection in multivariate time series (MTS) is very essential for modern complex physical equipment. A single anomaly in physical equipment can cause a series of failures due to fault propagation. Therefore, the equipment needs to be comprehensively monitored by an anomaly detectio...

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Bibliographic Details
Published in:Computers & security Vol. 142; p. 103877
Main Authors: Guan, Siwei, He, Zhiwei, Ma, Shenhui, Gao, Mingyu
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.07.2024
Subjects:
Anomaly detection
Multivariate time series
Physical equipment
Spatial–temporal graph networks
Variational autoencoder
Variational autoencoder
Multivariate time series
Physical equipment
Anomaly detection
Spatial–temporal graph networks
ISSN:0167-4048, 1872-6208
Online Access:Get full text
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Summary:Effective anomaly detection in multivariate time series (MTS) is very essential for modern complex physical equipment. A single anomaly in physical equipment can cause a series of failures due to fault propagation. Therefore, the equipment needs to be comprehensively monitored by an anomaly detection system to ensure its health. However, given the rise in the size and complexity of the physical equipment, the number of sensors required to infer its health status has increased dramatically, and the complex temporal and spatial dependencies in sensors are difficult to capture. Aiming at the problem, this paper proposes a MTS anomaly detection algorithm called VSAD, which is based on spatial–temporal graph networks and variational autoencoder (VAE). It employs spatial–temporal graph networks to learn complex temporal and spatial dependencies in MTS. Moreover, its VAE architecture enables it to learn data distribution patterns in an unsupervised manner and its threshold is determined by an automatic threshold selection strategy. Finally, experimental results conducted on six publicly available benchmark datasets show that VSAD outperforms state-of-the-art methods for anomaly detection, with an average F1 scores improvement of about 7%.
ISSN:0167-4048
1872-6208
DOI:10.1016/j.cose.2024.103877