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.

中文翻译：

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时间序列中的在线异常搜索：重大的在线矛盾

这项工作的目的是为在线时间序列分析获得有用的异常定义。这个想法是要开发一种异常概念，这种异常概念对于长期和频繁的流媒体来说是可持续的。作为解决方案，我们提供了对不和谐概念的改编，该概念已成功用于时间序列的异常检测。在线方法意味着频繁处理数据流以及时提供异常警报。这需要进行修改，因为不和谐搜索在其原始定义中无法完全分解。通过一种统计方法，可以对每种分析的不一致性的重要性进行评估，从而有可能获得使误报次数最小化的解决方案。新的在线异常称为重大在线不和谐（sods）。草皮搜索作为一项新颖功能，可确定所调查时间序列中的异常数量。搜索草皮已实施，其性质已通过合成和真实数据验证。结果，我们发现sod可以被视为在快速流时间序列或大数据上下文中进行异常检测的有用的新工具。