Online anomaly search in time series: significant online discords

The aim of this work is to obtain a useful anomaly definition for online analysis of time series. The idea is to develop an anomaly concept which is sustainable for long-lived and frequent streamings. As a solution, we provide an adaptation of the discord concept, which has been successfully used for anomaly detection on time series. An online approach implies the frequent processing of a data streaming for timely providing anomaly alerts. This requires a modification since discord search is not exactly decomposable in its original definition. With a statistical approach, allowing to rate the significance of the discords of each analysis, it has been possible to obtain a solution where the number of false positives is minimized. The new online anomalies are called significant online discords (sods). As a novel feature, sod search determines the quantity of anomalies in the time series under investigation. The search for sods has been implemented and its properties validated with synthetic and real data. As a result, we found that sods can be considered as a useful new tool for anomaly detection in fast streaming time series or Big Data contexts.