A General Framework for the Assessment of Detectors of Anomalies in Time Series

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Abstract
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.
Funding Information
  • FAIR—Future Artificial Intelligence Research
  • European Union Next-GenerationEU (D.D. 1555 11/10/2022, PE00000013)

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