Authors:
Ming-Chang Lee
1
and
Jia-Chun Lin
2
Affiliations:
1
Department of Computer science, Electrical engineering and Mathematical Sciences, Høgskulen på Vestlandet (HVL), Bergen, Norway
;
2
Department of Information Security and Communication Technology, Norwegian University of Science and Technology (NTNU), Gjøvik, Norway
Keyword(s):
Time Series, Open-Ended Time Series, Univariate Time Series, Real-Time Anomaly Detection, LSTM, Adaptive Detection Threshold, Sliding Window.
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 demonst
rated that RePAD2 can address the mentioned resource exhaustion issue while offering comparable detection accuracy and slightly less time consumption.
(More)