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

• URL: http://arxiv.org/abs/2509.19654v1
• Date: Wed, 24 Sep 2025 00:18:28 GMT
• Title: Symbol-Temporal Consistency Self-supervised Learning for Robust Time Series Classification
• Authors: Kevin Garcia, Cassandra Garza, Brooklyn Berry, Yifeng Gao,
• Abstract summary: 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.<n>We propose a self-supervised learning framework that is aware of the bag-of-symbol representation.<n>We demonstrate that the proposed method can achieve significantly better performance where significant data shifting exists.
• Score: 12.705047994947597
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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