“Now you see it, now you don’t!” Detecting suspicious pattern absences in continuous time series

Abstract

Given its large applicational potential, time series anomaly detection has become a crucial data mining task. Its goal is to identify periods of a time series where there is a deviation from the expected behavior. Existing approaches focus on analyzing whether the currently observed behavior differs from previously seen, normal behavior. In contrast, this paper tackles the the task where the absence of a previously observed behavior is indicative of an anomaly. In other words, a pattern that is expected to recur in the time series is absent. In real-world use cases, absent patterns can be linked to serious problems. For instance, if a scheduled, regular maintenance operation of a machine does not take place, this can be harmful to the machine at a later time. In this paper, we introduce the task of detecting when a specific pattern is absent in a real-valued time series. We propose a novel technique called FZapPa that can address this task. Empirically, FZapPa outperforms existing anomaly techniques on a benchmark of real-world datasets.