Anomaly detection on time series is a fundamental task in monitoring the Key Performance Indicators (KPIs) of IT systems. The existing approaches in the literature either require a lot of training resources or are hard to be deployed in real scenarios. In this paper, the online matrix profile, which requires no training, is proposed to address this issue. The anomalies are detected by referring to the past subsequence that is the closest to the current one. The distance significance is introduced based on the online matrix profile, which demonstrates a prominent pattern when an anomaly occurs. Another training-free approach spectral residual is integrated into our approach to further enhance the detection accuracy. Moreover, the proposed approach is sped up by at least four times for long time series by the introduced cache strategy. In comparison to the existing approaches, the online matrix profile makes a good trade-off between accuracy and efficiency. More importantly, it is generic to various types of time series in the sense that it works without the constraint from any trained model.
翻译:在时间序列上异常检测是监测信息技术系统关键业绩指标(KPIs)的一项基本任务。文献中的现有方法要么需要大量的培训资源,要么很难在真实情况下运用。在本文中,提出不需要任何培训的在线矩阵剖面图解决这一问题。通过提及过去最接近当前序列的子序列来检测异常。在在线矩阵剖面图的基础上引入了距离意义,这显示了出现异常时的突出模式。另一种不培训方法光谱残余被纳入了我们进一步提高探测准确性的方法。此外,通过引入的缓存战略,拟议的方法在很长的时间序列中至少加速了4次。与现有方法相比,在线矩阵剖面图在准确性和效率之间做了良好的交换。更重要的是,它对于各种时间序列来说是通用的,因为其运作不受任何经过培训的模式的约束。