Expert enhanced dynamic time warping based anomaly detection

TL;DR

This paper introduces E-DTWA, a novel anomaly detection method that enhances dynamic time warping with expert feedback, improving detection efficiency and adaptability while maintaining low computational costs.

Contribution

The paper proposes a new anomaly detection approach combining DTW with human-in-the-loop feedback, offering improved flexibility and efficiency over existing methods.

Findings

Efficient anomaly detection with low computational complexity

Flexible retraining based on expert feedback

Effective handling of non-linear time distortions

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.