The goal of anomaly detection is to identify observations that are generated by a distribution that differs from the reference distribution that qualifies normal behavior. When examining a time series, the reference distribution may evolve over time. The anomaly detector must therefore be able to adapt to such changes. In the online context, it is particularly difficult to adapt to abrupt and unpredictable changes. Our solution to this problem is based on the detection of breakpoints in order to adapt in real time to the new reference behavior of the series and to increase the accuracy of the anomaly detection. This solution also provides a control of the False Discovery Rate by extending methods developed for stationary series.

翻译：异常检测的目标是识别由与表征正常行为的参考分布不同的分布所产生的观测值。在分析时间序列时，参考分布可能随时间演变。因此，异常检测器必须能够适应此类变化。在线检测场景中，尤其难以应对突发且不可预测的变化。我们针对该问题的解决方案基于断点检测，旨在实时适应序列的新参考行为，并提升异常检测的准确性。该方案还通过扩展针对平稳序列开发的方法，实现了对错误发现率的控制。