An open-ended time series refers to a series of data points indexed in time order without an end. Such a time series can be found everywhere due to the prevalence of Internet of Things. Providing lightweight and real-time anomaly detection for open-ended time series is highly desirable to industry and organizations since it allows immediate response and avoids potential financial loss. In the last few years, several real-time time series anomaly detection approaches have been introduced. However, they might exhaust system resources when they are applied to open-ended time series for a long time. To address this issue, in this paper we propose RePAD2, a lightweight real-time anomaly detection approach for open-ended time series by improving its predecessor RePAD, which is one of the state-of-the-art anomaly detection approaches. We conducted a series of experiments to compare RePAD2 with RePAD and another similar detection approach based on real-world time series datasets, and demonstrated that RePAD2 can address the mentioned resource exhaustion issue while offering comparable detection accuracy and slightly less time consumption.

翻译：开放时间序列是指一系列按时间顺序索引、无终点的数据点。由于物联网的普及，此类时间序列广泛存在于各类场景中。为开放时间序列提供轻量级且实时的异常检测对工业界与组织极具价值，因为它能实现即时响应并避免潜在经济损失。近年来，已有多种实时时间序列异常检测方法被提出。然而，当这些方法长期应用于开放时间序列时，可能会耗尽系统资源。为解决该问题，本文提出RePAD2——一种基于其前身RePAD（当前最先进的异常检测方法之一）改进的轻量级实时异常检测方法。我们通过一系列实验，基于真实世界时间序列数据集将RePAD2与RePAD及另一种相似检测方法进行对比，结果表明RePAD2在保持相近检测精度的同时，可解决上述资源耗尽问题，且时间消耗略有降低。