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

• URL: http://arxiv.org/abs/2303.00409v2
• Date: Thu, 2 Mar 2023 08:04:03 GMT
• Title: RePAD2: Real-Time, Lightweight, and Adaptive Anomaly Detection for Open-Ended Time Series
• Authors: Ming-Chang Lee and Jia-Chun Lin
• Abstract summary: An open-ended time series refers to a series of data points indexed in time order without an end. Several real-time time series anomaly detection approaches have been introduced. They might exhaust system resources when they are applied to open-ended time series for a long time. We propose RePAD2, a lightweight real-time anomaly detection approach for open-ended time series.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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