OneShot：用一条确认异常数据为时序异常检测器加权
OneShot: Label-Efficient Detector Weighting for Time Series Anomaly Detection
时序异常检测的新思路：运维确认一条异常告警，就能自动调好检测器组合，152 条序列上验证有效，做监控系统的可以看看。
时序异常检测没有通用检测器，现有做法是为每条序列挑选单个检测器，或对整个检测器池取平均，且都假设没有任何标签可用。论文提出 OneShot 方法，利用运维人员确认的一条异常告警，对池中每个检测器打分并生成软混合权重。在 152 条单变量和 73 条多变量序列上，OneShot 超过了最强单个检测器、整池平均和直接挑选最优检测器三种基线。代码已在 GitHub 开源。
OneShot: Label-Efficient Detector Weighting for Time Series Anomaly Detection
Time series (TS) anomaly detection (AD) lacks a universal detector, with the best choice determined by the series rather than by the algorithm. Existing methods therefore either select one detector for each series or average a pool of them. However, both assume that no label ever arrives, while a deployed monitor does receive them, since an operator confirms the alarms it raises. In this paper, we propose ONESHOT, which uses a single confirmed anomaly to weight the pool rather than to choose one detector from it. Specifically, we score each detector on the confirmed anomaly, and those scores become the weights of a soft mixture over the whole pool. We show that a single confirmed anomaly is enough to come close to the best detector for the series. Across 152 univariate and 73 multivariate series, ONESHOT outperforms the strongest single detector, averaging the whole pool, and even picking the best detector on the confirmed event. Code is available at: https://github.com/seunghan96/one_shot.
