  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.
