  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.
