Mask the Redundancy: Evolving Masking Representation Learning for Multivariate Time-Series Clustering

• URL: http://arxiv.org/abs/2511.17008v1
• Date: Fri, 21 Nov 2025 07:26:40 GMT
• Title: Mask the Redundancy: Evolving Masking Representation Learning for Multivariate Time-Series Clustering
• Authors: Zexi Tan, Xiaopeng Luo, Yunlin Liu, Yiqun Zhang,
• Abstract summary: This paper proposes the Evolving-masked MTS Clustering (EMTC) method, with its model architecture composed of Importance-aware Variate-wise Masking (IVM) and Multi-Endogenous Views (MEV) representation learning modules.<n>Experiments on 15 real benchmark datasets demonstrate the superiority of EMTC in comparison with eight SOTA methods, where the EMTC achieves an average improvement of 4.85% over the strongest baselines.
• Score: 8.21087649188741
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multivariate Time-Series (MTS) clustering discovers intrinsic grouping patterns of temporal data samples. Although time-series provide rich discriminative information, they also contain substantial redundancy, such as steady-state machine operation records and zero-output periods of solar power generation. Such redundancy diminishes the attention given to discriminative timestamps in representation learning, thus leading to performance bottlenecks in MTS clustering. Masking has been widely adopted to enhance the MTS representation, where temporal reconstruction tasks are designed to capture critical information from MTS. However, most existing masking strategies appear to be standalone preprocessing steps, isolated from the learning process, which hinders dynamic adaptation to the importance of clustering-critical timestamps. Accordingly, this paper proposes the Evolving-masked MTS Clustering (EMTC) method, with its model architecture composed of Importance-aware Variate-wise Masking (IVM) and Multi-Endogenous Views (MEV) representation learning modules. IVM adaptively guides the model in learning more discriminative representations for clustering, while the MEV-based reconstruction and contrastive learning pathways enhance the generalization. That is, the MEV reconstruction facilitates multi-perspective complementary to prevent the masking from premature convergence, and the clustering-guided contrastive learning facilitates the joint optimization of representation and clustering. Extensive experiments on 15 real benchmark datasets demonstrate the superiority of EMTC in comparison with eight SOTA methods, where the EMTC achieves an average improvement of 4.85% over the strongest baselines.

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