Abstract
“Discord” is useful method of detecting anomaly from time series data such as equipment sensor data. The discord method detects anomaly by calculating brute-force nearest neighbor distance between two subsequence of the time series. Hence there is a problem of increasing detection time with the increase in data length. In this paper, we propose an algorithm that, at first, clusters subsequences of the input time series and selects centroid from each clusters (called as “exemplar subsequence”), then detect anomaly by calculating nearest neighbor distance between input subsequence and exemplar subsequence. We also show that our proposed method is more faster detection speed than the discord method and have equivalent detection ability as the discord method by an experiment with two time series data.
