Symbol-Temporal Consistency Self-supervised Learning for Robust Time Series Classification
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912602349633536 |
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| author | Garcia, Kevin Garza, Cassandra Berry, Brooklyn Gao, Yifeng |
| author_facet | Garcia, Kevin Garza, Cassandra Berry, Brooklyn Gao, Yifeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19654 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Symbol-Temporal Consistency Self-supervised Learning for Robust Time Series Classification Garcia, Kevin Garza, Cassandra Berry, Brooklyn Gao, Yifeng Machine Learning I.2.6 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. |
| title | Symbol-Temporal Consistency Self-supervised Learning for Robust Time Series Classification |
| topic | Machine Learning I.2.6 |
| url | https://arxiv.org/abs/2509.19654 |