用Mamba模型高效聚类单变量时间序列，提升长程依赖捕捉能力。
FMMVCC: Fuzzy Mamba-based Multi-View Contrastive Clustering for Univariate Time Series
- 基于Mamba的线性复杂度建模，捕捉时间序列长期依赖。
- 多视图自监督学习结合时序掩码与增强，提升表征质量。
- 在15个数据集上29次超越顶尖方法，适合无标签时间序列分析。
在真实场景中，大量时间序列数据生成但标注有限或成本高昂，导致监督学习难以应用，促使无监督方法成为发现原始数据中潜在结构的关键。聚类在此类任务中至关重要，能将具有相似时间模式的数据分组，支持探索性分析和下游任务而无需人工标注。然而，现有深度聚类方法常难以有效捕捉长程时间依赖，或依赖计算开销高的架构。本文提出FMMVCC，一种基于Mamba的时序聚类框架，利用状态空间序列建模以线性复杂度高效学习时间表示，并引入多视图自监督学习，结合时序掩码与数据增强策略。在15个基准数据集上的实验表明，FMMVCC持续优于当前最优基线，在60项指标评估中取得最佳表现29次，所有测试场景下平均排名最高。
原文摘要 · Abstract (English)
In many realistic scenarios, large volumes of time series data are generated with limited or expensive annotations. This limitation makes supervised learning methods difficult to apply and leads to the use of unsupervised approaches capable of discovering meaningful structures directly from raw data. Clustering therefore plays a crucial role in organizing time series into groups that share similar temporal patterns, enabling exploratory analysis and downstream tasks without requiring manual labeling. However, existing deep clustering methods often struggle to capture long-range temporal dependencies or rely on architectures with high computational cost. This paper introduces FMMVCC, a Mamba-based deep clustering framework for time series that leverages state space sequence modeling to efficiently learn temporal representations with linear complexity. Additionally, it utilizes multi-view self-supervised learning with temporal masking and augmentations. Experimental evaluation in 15 benchmark datasets proves that FMMVCC consistently outperforms state-of-the-art baselines, achieving the best overall performance in 29 of 60 total metric evaluations and the highest average rank in all tested scenarios.
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