TFEC: Multivariate Time-Series Clustering via Temporal-Frequency Enhanced Contrastive Learning

• URL: http://arxiv.org/abs/2601.07550v1
• Date: Mon, 12 Jan 2026 13:59:27 GMT
• Title: TFEC: Multivariate Time-Series Clustering via Temporal-Frequency Enhanced Contrastive Learning
• Authors: Zexi Tan, Tao Xie, Haoyi Xiao, Baoyao Yang, Yuzhu Ji, An Zeng, Xiang Zhang, Yiqun Zhang,
• Abstract summary: This paper proposes a Temporal-Frequency Enhanced Contrastive (TFEC) learning framework.<n>To preserve temporal structure while generating low-distortion representations, a temporal-frequency Co-EnHancement (CoEH) mechanism is introduced.<n>Experiments on six real-world benchmark datasets demonstrate TFEC's superiority, achieving 4.48% average NMI gains over SOTA methods.
• Score: 22.300584288382012
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multivariate Time-Series (MTS) clustering is crucial for signal processing and data analysis. Although deep learning approaches, particularly those leveraging Contrastive Learning (CL), are prominent for MTS representation, existing CL-based models face two key limitations: 1) neglecting clustering information during positive/negative sample pair construction, and 2) introducing unreasonable inductive biases, e.g., destroying time dependence and periodicity through augmentation strategies, compromising representation quality. This paper, therefore, proposes a Temporal-Frequency Enhanced Contrastive (TFEC) learning framework. To preserve temporal structure while generating low-distortion representations, a temporal-frequency Co-EnHancement (CoEH) mechanism is introduced. Accordingly, a synergistic dual-path representation and cluster distribution learning framework is designed to jointly optimize cluster structure and representation fidelity. Experiments on six real-world benchmark datasets demonstrate TFEC's superiority, achieving 4.48% average NMI gains over SOTA methods, with ablation studies validating the design. The code of the paper is available at: https://github.com/yueliangy/TFEC.

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