Anomaly detection on time series is a fundamental task in monitoring the Key Performance Indicators (KPIs) of IT systems. Many of the existing approaches in the literature show good performance while requiring a lot of training resources. 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:在时间序列上异常探测是监测信息技术系统关键业绩指标(KPIs)的一项基本任务。文献中的许多现有方法显示业绩良好,但需要大量培训资源。在本文中,提议解决这一问题不需要培训的在线矩阵剖面图。通过提及过去最接近当前序列的子序列来检测异常。根据在线矩阵剖面图引入了距离重要性,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,拟议的方法通过引入的缓冲战略以至少四倍的速度递增长期序列。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权衡。更重要的是,它对于各类时间序列是通用的,因为它没有受到任何经过培训的模式的限制。