Discord Monitoring for Streaming Time-Series

Shinya Kato, Daichi Amagata, Shunya Nishio, Takahiro Hara

Abstract: Many applications generate time-series and analyze it. One of the most important time-series analysis tools is anomaly detection, and discord discovery aims at finding an anomaly subsequence in a time-series. Time-series is essentially dynamic, so monitoring the discord of a streaming time-series is an important problem. This paper addresses this problem and proposes SDM (Streaming Discord Monitoring), an algorithm that efficiently updates the discord of a streaming time-series over a sliding window. We show that SDM is approximation-friendly, i.e., the computational efficiency is accelerated by monitoring an approximate discord with theoretical bound. Our experiments on real datasets demonstrate the efficiency of SDM and its approximate version.