Expert enhanced dynamic time warping based anomaly detection

• URL: http://arxiv.org/abs/2310.02280v1
• Date: Mon, 2 Oct 2023 04:54:04 GMT
• Title: Expert enhanced dynamic time warping based anomaly detection
• Authors: Matej Kloska, Gabriela Grmanova, Viera Rozinajova
• Abstract summary: 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.
• Score: 1.3812010983144802
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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