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

arXiv: https://arxiv.org/abs/2511.17008 · HTML: https://mlsanity.com/paper/2511.17008/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 41 references that are in the map.
- First posted on arXiv 21 Nov 2025.

## Builds on (most cited first, 13 of 13)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [TS2Vec: Towards Universal Representation of Time Series](https://mlsanity.com/paper/2106.10466/) (2021)
- [The UEA multivariate time series classification archive, 2018](https://mlsanity.com/paper/1811.00075/) (2018)
- [TSLANet: Rethinking Transformers for Time Series Representation Learning](https://mlsanity.com/paper/2404.08472/) (2024)
- [Ti-MAE: Self-Supervised Masked Time Series Autoencoders](https://mlsanity.com/paper/2301.08871/) (2023)
- [Cluster-guided Contrastive Graph Clustering Network](https://mlsanity.com/paper/2301.01098/) (2023)
- [TimesURL: Self-supervised Contrastive Learning for Universal Time Series Representation Learning](https://mlsanity.com/paper/2312.15709/) (2023)
- [Self-Supervised Time Series Representation Learning via Cross Reconstruction Transformer](https://mlsanity.com/paper/2205.09928/) (2022)
- [Learning Self-Growth Maps for Fast and Accurate Imbalanced Streaming Data Clustering](https://mlsanity.com/paper/2404.09243/) (2024)
- [Frequency-Masked Embedding Inference: A Non-Contrastive Approach for Time Series Representation Learning](https://mlsanity.com/paper/2412.20790/) (2024)
- [Fuzzy Cluster-Aware Contrastive Clustering for Time Series](https://mlsanity.com/paper/2503.22211/) (2025)
- [MEET-Sepsis: Multi-Endogenous-View Enhanced Time-Series Representation Learning for Early Sepsis Prediction](https://mlsanity.com/paper/2510.15985/) (2025)
- [TC-DTW: Accelerating Multivariate Dynamic Time Warping Through Triangle Inequality and Point Clustering](https://mlsanity.com/paper/2101.07731/) (2021)

## Built on by (most cited first, 6 of 6)

- [AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE](https://mlsanity.com/paper/2606.03631/) (2026)
- [Detecting Autism Spectrum Disorder with Deep Eye Movement Features](https://mlsanity.com/paper/2601.05812/) (2026)
- [SCAN: Enhance Time Series Anomaly Detection via Multi-Scale Neighborhood-Centered Clustering](https://mlsanity.com/paper/2606.19255/) (2026)
- [FMMVCC: Fuzzy Mamba-based Multi-View Contrastive Clustering for Univariate Time Series](https://mlsanity.com/paper/2607.07258/) (2026)
- [AOI-Net: Structural Face AOI-Guided Eye-Gaze Track Representation Learning for Autism Spectrum Disorder Detection](https://mlsanity.com/paper/2608.29289/) (2026)
- [Seeing Time: Visual-Temporal Representation Learning for Interpretable Time Series Clustering](https://mlsanity.com/paper/2609.36873/) (2026)

## Abstract (arXiv)

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.