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

Ming-Chang Lee, Jia-Chun Lin

2023

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.

Paper Citation

in Harvard Style

Lee M. and Lin J. (2023). RePAD2: Real-Time Lightweight Adaptive Anomaly Detection for Open-Ended Time Series. In Proceedings of the 8th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS, ISBN 978-989-758-643-9, SciTePress, pages 208-217. DOI: 10.5220/0011981700003482

in Bibtex Style

@conference{iotbds23,
author={Ming-Chang Lee and Jia-Chun Lin},
title={RePAD2: Real-Time Lightweight Adaptive Anomaly Detection for Open-Ended Time Series},
booktitle={Proceedings of the 8th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,},
year={2023},
pages={208-217},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011981700003482},
isbn={978-989-758-643-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,
TI - RePAD2: Real-Time Lightweight Adaptive Anomaly Detection for Open-Ended Time Series
SN - 978-989-758-643-9
AU - Lee M.
AU - Lin J.
PY - 2023
SP - 208
EP - 217
DO - 10.5220/0011981700003482
PB - SciTePress