TFEC: Multivariate Time-Series Clustering via Temporal-Frequency Enhanced Contrastive Learning
By: Zexi Tan , Tao Xie , Haoyi Xiao and more
Potential Business Impact:
Finds patterns in changing data better.
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.
Similar Papers
Mask the Redundancy: Evolving Masking Representation Learning for Multivariate Time-Series Clustering
Machine Learning (CS)
Finds important moments in data for better grouping.
TS2Vec-Ensemble: An Enhanced Self-Supervised Framework for Time Series Forecasting
Machine Learning (CS)
Predicts future events better by combining learned patterns and cycles.
Intelligently Augmented Contrastive Tensor Factorization: Empowering Multi-dimensional Time Series Classification in Low-Data Environments
Machine Learning (CS)
Teaches computers to understand complex data with less examples.