  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.
