FMMVCC: Fuzzy Mamba-based Multi-View Contrastive Clustering for Univariate Time Series

• URL: http://arxiv.org/abs/2607.07258v1
• Date: Wed, 08 Jul 2026 10:46:02 GMT
• Title: FMMVCC: Fuzzy Mamba-based Multi-View Contrastive Clustering for Univariate Time Series
• Abstract summary: This paper introduces FMMVCC, a Mamba-based deep clustering framework for time series.<n>It leverages state space sequence modeling to efficiently learn temporal representations with linear complexity.<n> FMMVCC consistently outperforms state-of-the-art baselines in 15 benchmark datasets.
• Score: 3.137177972404253
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

Related papers

• Learning by Shifting: Temporal View Construction for Time Series Contrastive Learning [4.7318594191739765]
  Supervised learning demands large quantities of labeled data, a bottleneck that is expensive and reliant on domain-specific expertise.<n>Self-supervised learning, particularly contrastive learning, has emerged as a compelling alternative, enabling rich representation learning directly from unlabeled data.<n>Existing approaches for time series rely on hand-crafted augmentations and maskings that embed strong domain assumptions.<n>We present a systematic analysis of contrastive learning dynamics in time series settings, examining the effects of batch size and the number of negatives on downstream performance.
  arXiv  Detail & Related papers  (2026-06-20T09:07:30Z)
• Generalized Deep Multi-view Clustering via Causal Learning with Partially Aligned Cross-view Correspondence [72.41989962665285]
  Multi-view clustering (MVC) aims to explore the common clustering structure across multiple views.<n>However, real-world scenarios often present a challenge as only partial data is consistently aligned across different views.<n>We design a causal multi-view clustering network, termed CauMVC, to tackle this problem.
  arXiv  Detail & Related papers  (2025-09-19T14:31:40Z)
• Time-RA: Towards Time Series Reasoning for Anomaly with LLM Feedback [55.284574165467525]
  Time-series Reasoning for Anomaly (Time-RA) transforms classical time series anomaly detection into a generative, reasoning-intensive task.<n>Also, we introduce the first real-world multimodal benchmark dataset, RATs40K, explicitly annotated for anomaly reasoning.
  arXiv  Detail & Related papers  (2025-07-20T18:02:50Z)
• FreRA: A Frequency-Refined Augmentation for Contrastive Learning on Time Series Classification [56.925103708982164]
  We present a novel perspective from the frequency domain and identify three advantages for downstream classification: global, independent, and compact.<n>We propose the lightweight yet effective Frequency Refined Augmentation (FreRA) tailored for time series contrastive learning on classification tasks.<n>FreRA consistently outperforms ten leading baselines on time series classification, anomaly detection, and transfer learning tasks.
  arXiv  Detail & Related papers  (2025-05-29T07:18:28Z)
• FCPCA: Fuzzy clustering of high-dimensional time series based on common principal component analysis [11.138320457692288]
  This work introduces a novel fuzzy clustering approach based on common principal component analysis.<n>We show that our proposed clustering method outperforms several existing approaches in the literature.<n>An interesting application involving brain signals from different drivers recorded from a simulated driving experiment illustrates the potential of the approach.
  arXiv  Detail & Related papers  (2025-05-12T06:59:17Z)
• Fuzzy Cluster-Aware Contrastive Clustering for Time Series [1.435214708535728]
  Traditional unsupervised clustering methods often fail to capture the complex nature of time series data.<n>We propose a fuzzy cluster-aware contrastive clustering framework (FCACC) that jointly optimize representation learning and clustering.<n>Our approach introduces a novel three-view data augmentation strategy to enhance feature extraction by leveraging various characteristics of time series data.
  arXiv  Detail & Related papers  (2025-03-28T07:59:23Z)
• Contrastive Continual Multi-view Clustering with Filtered Structural Fusion [57.193645780552565]
  Multi-view clustering thrives in applications where views are collected in advance. It overlooks scenarios where data views are collected sequentially, i.e., real-time data. Some methods are proposed to handle it but are trapped in a stability-plasticity dilemma. We propose Contrastive Continual Multi-view Clustering with Filtered Structural Fusion.
  arXiv  Detail & Related papers  (2023-09-26T14:18:29Z)
• Time Series Clustering With Random Convolutional Kernels [0.0]
  Time series data, spanning applications ranging from climatology to finance to healthcare, presents significant challenges in data mining. One open issue lies in time series clustering, which is crucial for processing large volumes of unlabeled time series data. We introduce R-Clustering, a novel method that utilizes convolutional architectures with randomly selected parameters.
  arXiv  Detail & Related papers  (2023-05-17T06:25:22Z)
• Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models [61.10851158749843]
  Key insights can be obtained by discovering lead-lag relationships inherent in the data. We develop a clustering-driven methodology for robust detection of lead-lag relationships in lagged multi-factor models.
  arXiv  Detail & Related papers  (2023-05-11T10:30:35Z)
• MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time Series [20.008535430484475]
  Masked Hierarchical Cluster-wise Contrastive Learning model is presented. It exploits semantic information obtained from the hierarchical structure consisting of multiple latent partitions for time series. It is shown to be superior to state-of-the-art approaches for unsupervised time series representation learning.
  arXiv  Detail & Related papers  (2022-12-02T12:42:53Z)
• Cluster-and-Conquer: A Framework For Time-Series Forecasting [94.63501563413725]
  We propose a three-stage framework for forecasting high-dimensional time-series data. Our framework is highly general, allowing for any time-series forecasting and clustering method to be used in each step. When instantiated with simple linear autoregressive models, we are able to achieve state-of-the-art results on several benchmark datasets.
  arXiv  Detail & Related papers  (2021-10-26T20:41:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.