CNN-Trans: A Two-Branch CNN Transformer Model for Multivariate Time Series Classification

Sarra Hassine, Sourour Ammar, Sourour Ammar, Ilef Ben Slima, Ilef Ben Slima

2025

Abstract

The extensive presence of sensors in multiple domains has led to the generation of enormous amounts of multivariate time series data, presenting significant challenges for efficient classification. Although contemporary artificial intelligence methods show promising performance in addressing such data, they often struggle to capture both long-range dependencies and intricate local patterns within the sequences. This paper introduces CNN-Trans, an innovative deep learning model designed specifically for multivariate time series classification to address the mentioned challenge. CNN-Trans combines the strengths of transformers and convolutional neural networks (CNN). The proposed model uses a parallel strategy with both a transformer encoder and a CNN encoder working simultaneously on the time series data. The transformer captures global relationships through self-attention, while the CNN extracts localized spatial features tailored to each variable. We evaluate CNN-Trans on various benchmark datasets encompassing diverse sensor applications. The results show that our model is robust and highly effective for complex data. CNN-Trans outperforms others with 93.33% on NATOPS and 98.37% on PenDigits, excelling in high-dimensional datasets like Kitchen (95.74%) and HAR (87.41%). Additionally, CNN-Trans exhibits robustness and generalizability across different input features, showcasing its practical utility in real-world scenarios.

Paper Citation

in Harvard Style

Hassine S., Ammar S. and Ben Slima I. (2025). CNN-Trans: A Two-Branch CNN Transformer Model for Multivariate Time Series Classification. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 418-428. DOI: 10.5220/0013169500003890

in Bibtex Style

@conference{icaart25,
author={Sarra Hassine and Sourour Ammar and Ilef Ben Slima},
title={CNN-Trans: A Two-Branch CNN Transformer Model for Multivariate Time Series Classification},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2025},
pages={418-428},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013169500003890},
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 - CNN-Trans: A Two-Branch CNN Transformer Model for Multivariate Time Series Classification
SN - 978-989-758-737-5
AU - Hassine S.
AU - Ammar S.
AU - Ben Slima I.
PY - 2025
SP - 418
EP - 428
DO - 10.5220/0013169500003890
PB - SciTePress