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

TL;DR

This paper introduces EMTC, a novel evolving masking approach for multivariate time-series clustering that adaptively emphasizes critical timestamps, leading to improved clustering performance over existing methods.

Contribution

The paper proposes EMTC with importance-aware masking and multi-endogenous views, integrating dynamic masking into the learning process for better representation and clustering.

Findings

EMTC outperforms 8 state-of-the-art methods on 15 datasets.

Achieves an average 4.85% F1-Score improvement.

Demonstrates effective adaptive masking for discriminative feature learning.

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, whose model architecture comprises Importance-aware Variate-wise Masking (IVM) and Multi-Endogenous Views (MEV) generation modules. IVM adaptively guides the model in learning more discriminative representations for clustering, while the reconstruction and cluster-guided contrastive learning pathways enhance and connect the representation learning to clustering tasks. Extensive experiments on 15 benchmark datasets demonstrate the superiority of EMTC over eight SOTA methods, where the EMTC achieves an average improvement of 4.85% in F1-Score over the strongest baselines.