RePAD2: Real-Time, Lightweight, and Adaptive Anomaly Detection for Open-Ended Time Series

TL;DR

RePAD2 is a lightweight, real-time anomaly detection method for open-ended time series that improves resource efficiency while maintaining accuracy, suitable for IoT applications.

Contribution

RePAD2 introduces an enhanced anomaly detection approach that reduces resource exhaustion in open-ended time series analysis, building upon the previous RePAD method.

Findings

RePAD2 reduces resource consumption compared to RePAD.

RePAD2 maintains comparable detection accuracy.

RePAD2 offers slightly faster detection times.

Abstract

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.