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Clustering supports exploratory analysis and downstream tasks by grouping sequences with similar temporal patterns. The paper presents FMMVCC, a Mamba-based deep clustering framework using state-space sequence modeling with linear complexity, plus multi-view contrastive self-supervision via temporal masking and augmentations, outperforming strong baselines on 15 benchmark datasets.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/fmmvcc-fuzzy-mamba-based-multi-view-contrastive-clustering-for-univariate-time-series-abstract/328206/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/fmmvcc-fuzzy-mamba-based-multi-view-contrastive-clustering-for-univariate-time-series-abstract/328206.png","ImageObject",300,407,{"name":92,"@type":93},"Quinn","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-01","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is unsupervised time series clustering important in practice?","Question",{"text":112,"@type":113},"Because labeled data in many real deployments is expensive, time-consuming, or infeasible, unsupervised clustering helps reveal latent temporal structures without manual annotation.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does FMMVCC model long-range temporal dependencies efficiently?",{"text":117,"@type":113},"It uses Mamba-based state space sequence modeling designed for linear-time processing while maintaining strong capacity for long temporal contexts.",{"name":119,"@type":110,"acceptedAnswer":120},"What self-supervised training strategy does FMMVCC use?",{"text":121,"@type":113},"It generates multiple views of each time series using temporal masking and augmentations, then applies multi-view contrastive learning to learn discriminative representations.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},328206,1790466521,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":39,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":46,"preview_page_count":4},962075114765,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","FMMVCC: Fuzzy Mamba-based Multi-View Contrastive Clustering for Univariate Time Series  \n1st Donato Cerciello*  Departamento de Sistemas Inform a´ticos, Universidad Polite´cnica de Madrid Madrid, Spain [donato.cerciello@upm.es](donato.cerciello@upm.es)  \n2nd Leonardo Schiavo  Departamento de Sistemas Inform a´ticos, Universidad Polite´cnica de Madrid Madrid, Spain [leonardo.schiavo@upm.es](leonardo.schiavo@upm.es)  \n3rd Angel Panizo-LLedot  Departamento de Sistemas Inform a´ticos, Universidad Polite´cnica de Madrid Madrid, Spain [angel.panizo@upm.es](angel.panizo@upm.es)  \n4th Javier Huertas Tato  Departamento de Sistemas Inform a´ticos, Universidad Polite´cnica de Madrid Madrid, Spain [javier.huertas.tato@upm.es](javier.huertas.tato@upm.es)  \n5th David Camacho  Departamento de Sistemas Inform a´ticos, Universidad Polite´cnica de Madrid Madrid, Spain [david.camacho@upm.es](david.camacho@upm.es)  \narXiv :2607 .07258v 1 [ cs .LG] 8 Jul 2026  \nAbstract—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.  \nIndex Terms—Time Series Clustering, Contrastive Learning, Mamba SSM, Time Series Analysis.  \nI. INTRODUCTION  \nThe rapid expansion of devices and sensor networks on the Internet of Things (IoT) has led to an explosion of time series data in domains such as smart cities, industrial systems, healthcare and environmental monitoring [1] . These continuous streams of temporal data contain valuable information for understanding patterns, detecting anomalies, and monitoring system behavior. However, obtaining labeled data in such environments is often expensive, time-consuming, or even infeasible due to the scale and complexity of sensor deployments. As a result, unsupervised learning methods, and in particular time series clustering, have become increasingly important for discovering latent structures in sequential data [2] .  \nTime series clustering is defined as an unsupervised learning  \n*Corresponding author: Donato Cerciello  \nmethod that groups similar time series patterns without any supervision. Traditional clustering methods, such as K-means and hierarchical clustering, have been shown to usually require handcrafted measures of similarity, such as Euclidean distance and DTW [3] . Although these methods have been shown tobe successful in some cases, they have some difficulties in dealing with the complex temporal relationships and nonlinear dynamics that can be observed in recent high-dimensional time series data. For that reason, recent research has proposed deep learning-based clustering methods.  \nIn recent years, contrastive learning (CL) has been established as an effective framework for the self-supervised representation learning method. By maximizing the agreement between different views of the same data and minimizing the agreement between different samples, CL has the ability","cbCaiakRCQupEqiL","https://ap.wps.com/l/cbCaiakRCQupEqiL","pdf",37421071,"English","# Introduction\n## Problem setting: clustering for unlabeled time series\n## Contrastive learning for self-supervised representation\n## Efficient long-range modeling with state space models\n# Proposed method: FMMVCC","[{\"question\":\"Why is unsupervised time series clustering important in practice?\",\"answer\":\"Because labeled data in many real deployments is expensive, time-consuming, or infeasible, unsupervised clustering helps reveal latent temporal structures without manual annotation.\"},{\"question\":\"How does FMMVCC model long-range temporal dependencies efficiently?\",\"answer\":\"It uses Mamba-based state space sequence modeling designed for linear-time processing while maintaining strong capacity for long temporal contexts.\"},{\"question\":\"What self-supervised training strategy does FMMVCC use?\",\"answer\":\"It generates multiple views of each time series using temporal masking and augmentations, then applies multi-view contrastive learning to learn discriminative representations.\"}]","FMMVCC: Fuzzy Mamba-based Multi-View Contrastive Clustering for Univariate Time Series - abstract | PDF",1789978842]