RePAD3: Advanced Lightweight Adaptive Anomaly Detection for Univariate Time Series of Any Pattern

Ming-Chang Lee, Jia-Chun Lin, Sokratis Katsikas

2025

Abstract

Univariate time series anomaly detection is crucial for early risk identification and prompt response, making it essential for diverse applications such as energy usage monitoring, temperature monitoring, heart rate monitoring. To be applicable and valuable in the real world, anomaly detection must process time series data on the fly, detect anomalies in real time, and adapt to unexpected pattern changes in an efficient and lightweight manner. Several anomaly detection approaches with such capability have been introduced; however, they often generate frequent false positives. In this paper, we present a lightweight and adaptive anomaly detection approach named RePAD3 by leveraging the strengths of two state-of-the-art methods and mitigating their shortcomings with advanced detection and pattern inspection. According to our extensive experiments with real-world time series datasets, RePAD3 demonstrates superior detection accuracy and lower false positives across various patterns presented in the time series, thereby broadening its real-world applicability.

Paper Citation

in Harvard Style

Lee M., Lin J. and Katsikas S. (2025). RePAD3: Advanced Lightweight Adaptive Anomaly Detection for Univariate Time Series of Any Pattern. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 574-585. DOI: 10.5220/0013242700003890

in Bibtex Style

@conference{icaart25,
author={Ming-Chang Lee and Jia-Chun Lin and Sokratis Katsikas},
title={RePAD3: Advanced Lightweight Adaptive Anomaly Detection for Univariate Time Series of Any Pattern},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2025},
pages={574-585},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013242700003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - RePAD3: Advanced Lightweight Adaptive Anomaly Detection for Univariate Time Series of Any Pattern
SN - 978-989-758-737-5
AU - Lee M.
AU - Lin J.
AU - Katsikas S.
PY - 2025
SP - 574
EP - 585
DO - 10.5220/0013242700003890
PB - SciTePress