Symbol-Temporal Consistency Self-supervised Learning for Robust Time Series Classification

Authors

• Kevin Garcia, The University of Texas Rio Grande Valley
• Cassandra Garza, The University of Texas Rio Grande Valley
• Brooklyn Berry, The University of Texas Rio Grande Valley
• Yifeng Gao, The University of Texas Rio Grande Valley

Document Type

Conference Proceeding

Publication Date

2025

Abstract

The surge in the significance of time series in digital health domains necessitates advanced methodologies for extracting meaningful patterns and representations. Self-supervised contrastive learning has emerged as a promising approach for learning directly from raw data. However, time series data in digital health is known to be highly noisy, inherently involves concept drifting, and poses a challenge for training a generalizable deep learning model. In this paper, we specifically focus on data distribution shift caused by different human behaviors and propose a self-supervised learning framework that is aware of the bag-of-symbol representation. The bag-of-symbol representation is known for its insensitivity to data warping, location shifts, and noise existed in time series data, making it potentially pivotal in guiding deep learning to acquire a representation resistant to such data shifting. We demonstrate that the proposed method can achieve significantly better performance where significant data shifting exists.

Comments

Copyright © 2025, IEEE

Recommended Citation

Garcia, Kevin, Cassandra Garza, Brooklyn Berry, and Yifeng Gao. 2025. “Symbol-Temporal Consistency Self-Supervised Learning for Robust Time Series Classification.” 2025 IEEE 21st International Conference on Body Sensor Networks (BSN), November, 1–4. https://doi.org/10.1109/BSN66969.2025.11337455.

Publication Title

2025 IEEE 21st International Conference on Body Sensor Networks (BSN)

DOI

10.1109/BSN66969.2025.11337455