Impact of Deep Learning Libraries on Online Adaptive Lightweight Time Series Anomaly Detection

Ming-Chang Lee, Jia-Chun Lin

2023

Abstract

Providing online adaptive lightweight time series anomaly detection without human intervention and domain knowledge is highly valuable. Several such anomaly detection approaches have been introduced in the past years, but all of them were only implemented in one deep learning library. With the development of deep learning libraries, it is unclear how different deep learning libraries impact these anomaly detection approaches since there is no such evaluation available. Randomly choosing a deep learning library to implement an anomaly detection approach might not be able to show the true performance of the approach. It might also mislead users in believing one approach is better than another. Therefore, in this paper, we investigate the impact of deep learning libraries on online adaptive lightweight time series anomaly detection by implementing two state-of-the-art anomaly detection approaches in three well-known deep learning libraries and evaluating how these two approaches are individually affected by the three deep learning libraries. A series of experiments based on four real-world open-source time series datasets were conducted. The results provide a good reference to select an appropriate deep learning library for online adaptive lightweight anomaly detection.

Paper Citation

in Harvard Style

Lee M. and Lin J. (2023). Impact of Deep Learning Libraries on Online Adaptive Lightweight Time Series Anomaly Detection. In Proceedings of the 18th International Conference on Software Technologies - Volume 1: ICSOFT; ISBN 978-989-758-665-1, SciTePress, pages 106-116. DOI: 10.5220/0012082900003538

in Bibtex Style

@conference{icsoft23,
author={Ming-Chang Lee and Jia-Chun Lin},
title={Impact of Deep Learning Libraries on Online Adaptive Lightweight Time Series Anomaly Detection},
booktitle={Proceedings of the 18th International Conference on Software Technologies - Volume 1: ICSOFT},
year={2023},
pages={106-116},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012082900003538},
isbn={978-989-758-665-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Software Technologies - Volume 1: ICSOFT
TI - Impact of Deep Learning Libraries on Online Adaptive Lightweight Time Series Anomaly Detection
SN - 978-989-758-665-1
AU - Lee M.
AU - Lin J.
PY - 2023
SP - 106
EP - 116
DO - 10.5220/0012082900003538
PB - SciTePress