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：开放式时间序列是指按时间顺序索引且无终点的数据点序列。由于物联网的普及，此类时间序列无处不在。为开放式时间序列提供轻量级、实时的异常检测对工业界和组织极具价值，因为它能实现即时响应并避免潜在的经济损失。近年来，已有多种实时时间序列异常检测方法被提出。然而，当长时间应用于开放式时间序列时，这些方法可能会耗尽系统资源。为解决此问题，本文通过改进其前身——当前最先进的异常检测方法之一RePAD，提出了一种面向开放式时间序列的轻量级实时异常检测方法RePAD2。我们基于真实世界的时间序列数据集进行了一系列实验，将RePAD2与RePAD及另一种类似检测方法进行对比，结果表明：RePAD2在解决前述资源耗尽问题的同时，能够提供相当的检测精度并略微降低时间消耗。