多元统计
异常检测
自编码
小波
模式识别(心理学)
人工智能
计算机科学
图形
系列(地层学)
小波变换
异常(物理)
时间序列
离散小波变换
数据挖掘
机器学习
深度学习
理论计算机科学
地质学
物理
古生物学
凝聚态物理
作者
Jing Wang,Shikuan Shao,Yunfei Bai,Jiaoxue Deng,Youfang Lin
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:72: 1-11
被引量:3
标识
DOI:10.1109/tim.2022.3223142
摘要
In industrial facilities or IT systems, there are lots of multivariate time series generated from various metrics. Anomaly detection in multivariate time series is of great importance in applications such as fault diagnosis and root cause discovery. Recently, some unsupervised methods have made great progress in this task, especially the reconstruction architecture of autoencoders (AEs), learning normal distribution, and producing a significant error for anomalies. Although AEs can reconstruct subtle abnormal patterns well with the powerful generalization ability, it also leads to a high false negative. Moreover, these AE-based models ignore the dependence among variables at different time scales. In this article, we propose a novel anomaly detection framework named Multiscale wavElet Graph AE (MEGA). The main idea is to integrate discrete wavelet transform (DWT) into AE to decompose multivariate time series into multifrequency components and then reconstruct them, thereby highlighting various anomalies in specific frequency bands. Meanwhile, for anomalies caused by the changes of intervariable dependence, we introduce a dynamic graph module to capture such dependence on the decomposed multiscale frequency components. Experiments have been carried out on three public multivariate time-series anomaly detection datasets. The results demonstrate that the MEGA outperforms the state-of-the-art baselines.
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