Theses and Dissertations
Date of Award
12-1-2025
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
Yifeng Gao
Second Advisor
Li Zhang
Third Advisor
Haoteng Tang
Abstract
Recently, there has been a significant advancement in designing Self-Supervised Learning (SSL) frameworks for time series data to reduce the dependency on data labels. Among these works, hierarchical contrastive learning-based SSL frameworks, which learn representations by contrasting data embeddings at multiple resolutions, have gained considerable attention. Due to their ability to gather more information, they exhibit better generalization in various downstream tasks. However, when the time series data length is significant long, the computational cost is often significantly higher than that of other SSL frameworks. In this paper, to address this challenge, we propose an efficient way to train hierarchical contrastive learning models. Inspired by the fact that each resolution’s data embedding is highly dependent, we introduce importance-aware resolution selection based training framework to reduce the computational cost. In the experiment, we demonstrate that the proposed method significantly improves training time while preserving the original model’s integrity in extensive time series classification performance evaluations.
Recommended Citation
Garcia, K. (2025). Efficient Self-Supervised Representation Learning for Large-Scale Time Series Classification [Master's thesis, The University of Texas Rio Grande Valley]. ScholarWorks @ UTRGV. https://scholarworks.utrgv.edu/etd/1824
Comments
Copyright 2025 Kevin Garcia. All Rights Reserved. https://proquest.com/docview/3292612209