Anomalies are rare events, and this affects the design flow of detectors that monitor systems that behave normally most of the time but whose failure may have serious consequences. This limitation is particularly evident in the detector performance evaluation: it requires an abundance of normal and anomalous data but realistically faces a scarcity of the latter. To address this, in this article, we develop a framework comprising a set of abstract anomalies modeling the effects real-world failures and disturbances have on sensor readings. In addition, we devise synthetic generation procedures for these anomalies. Given a dataset of normal tracks from the actual application, one may apply such procedures to produce anomalous-like time series for a comprehensive detector assessment. We show that this framework can anticipate the detector performing best with real-world anomalies in the context of human and structural health monitoring, also highlighting that, in these cases, the best detector is not the most complex.

中文翻译：

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时间序列异常检测器评估的通用框架


异常是罕见的事件，这会影响检测器的设计流程，这些检测器监视大多数时间表现正常但发生故障可能会产生严重后果的系统。这种限制在探测器性能评估中尤为明显：它需要大量的正常和异常数据，但实际上面临着后者的稀缺。为了解决这个问题，在本文中，我们开发了一个框架，其中包含一组抽象异常，模拟现实世界故障和干扰对传感器读数的影响。此外，我们还为这些异常设计了合成生成程序。给定实际应用中的正常轨迹数据集，人们可以应用此类程序来生成类似异常的时间序列，以进行全面的探测器评估。我们表明，该框架可以预测探测器在人体和结构健康监测的背景下对现实世界异常表现最佳，同时还强调，在这些情况下，最好的探测器并不是最复杂的。