Comparative Analysis of Real-Time Time Series Representation Across RNNs, Deep Learning Frameworks, and Early Stopping

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

2024

Abstract

Real-Time time series representation is becoming increasingly crucial in data mining applications, enabling timely clustering and classification of time series without requiring parameter configuration and tuning in advance. Currently, the implementation of real-time time series representation relies on a fixed setting, consisting of a single type of recurrent neural network (RNN) within a specific deep learning framework, along with the adoption of early stopping. It remains unclear how leveraging different types of RNNs available in various deep learning frameworks, combined with the use of early stopping, influences the quality of representation and the efficiency of representation time. Arbitrarily selecting an RNN variant from a deep learning framework and activating the early stopping function for implementing a real-time time series representation approach may negatively impact the performance of the representation. Therefore, in this paper, we aim to investigate the impact of these factors on real-time time series representation. We implemented a state-of-the-art real-time time series representation approach using multiple well-established RNN variants supported by three widely used deep learning frameworks, with and without the adoption of early stopping. We analyzed the performance of each implementation using real-world open-source time series data. The findings from our evaluation provide valuable guidance on selecting the most appropriate RNN variant, deciding whether to adopt early stopping, and choosing a deep learning framework for real-time time series representation.

Paper Citation

in Harvard Style

Lee M., Lin J. and Katsikas S. (2024). Comparative Analysis of Real-Time Time Series Representation Across RNNs, Deep Learning Frameworks, and Early Stopping. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN 978-989-758-716-0, SciTePress, pages 371-382. DOI: 10.5220/0012996700003838

in Bibtex Style

@conference{kdir24,
author={Ming-Chang Lee and Jia-Chun Lin and Sokratis Katsikas},
title={Comparative Analysis of Real-Time Time Series Representation Across RNNs, Deep Learning Frameworks, and Early Stopping},
booktitle={Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2024},
pages={371-382},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012996700003838},
isbn={978-989-758-716-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Comparative Analysis of Real-Time Time Series Representation Across RNNs, Deep Learning Frameworks, and Early Stopping
SN - 978-989-758-716-0
AU - Lee M.
AU - Lin J.
AU - Katsikas S.
PY - 2024
SP - 371
EP - 382
DO - 10.5220/0012996700003838
PB - SciTePress