摘要
多变量时间序列的异常检测是一个具有挑战性的问题,要求模型从复杂的时间动态中学习信息表示,并推导出一个可区分的标准,该标准能从大量正常时间点识别出少量的异常点.但在时间序列分析中仍存在多变量时间序列复杂的时间相关性和高维度使得异常检测性能较差的问题,针对上述问题,本文提出了一种基于MLP(multi-layer perceptron)架构的模型(UMTS-Mixer),由于MLP的线性结构对顺序敏感,将其用来捕获时间相关性和跨通道相关性.大量实验表明UMTS-Mixer能够有效地检测时间序列异常,并在4个基准数据集上的表现更好,同时,在MSL和PSM两个数据集上取得了最高的F1,分别为91.35％,92.93％.
Abstract
Anomaly detection in multivariate time series is a challenging problem that requires models to learn information representations from complex temporal dynamics and derive a distinguishable criterion that can identify a small number of outliers from a large number of normal time points.However,in time series analysis,the complex temporal correlation and high dimensionality of multivariate time series will result in poor anomaly detection performance.To this end,this study proposes a model based on MLP(multi-layer perceptron)architecture(UMTS-Mixer).Since the linear structure of MLP is sensitive to order,it is employed to capture temporal correlation and cross-channel correlation.A large number of experiments show that UMTS-Mixer can detect time series anomalies and perform better on the four benchmark datasets.Meanwhile,the highest F1 is 91.35％and 92.93％on the MSL and PSM datasets,respectively.
基金项目
国家自然科学基金(62277010)
福建省自然科学基金(2020J01132452)
福厦泉国家自主创新示范区协同创新平台项目(2022FX6)
NETL
NSTL
维普
万方数据