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arXiv · 2310.02280

Expert enhanced dynamic time warping based anomaly detection

Matej Kloska·Gabriela Grmanova·Viera Rozinajova
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Abstract

Dynamic time warping (DTW) is a well-known algorithm for time series elastic dissimilarity measure. Its ability to deal with non-linear time distortions makes it helpful in variety of data mining tasks. Such a task is also anomaly detection which attempts to reveal unexpected behaviour without false detection alarms. In this paper, we propose a novel anomaly detection method named Expert enhanced dynamic time warping anomaly detection (E-DTWA). It is based on DTW with additional enhancements involving human-in-the-loop concept. The main benefits of our approach comprise efficient detection, flexible retraining based on strong consideration of the expert's detection feedback while retaining low computational and space complexity.

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Matej Kloska, Gabriela Grmanova, Viera Rozinajova. 2023-10-02. Expert enhanced dynamic time warping based anomaly detection. https://doi.org/10.1016/j.eswa.2023.120030

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