Timing anomaly detection based on GRU-INEncoder

Timing anomaly detection based on GRU-INEncoder | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Timing anomaly detection based on GRU-INEncoder Shiqian Han, Junxia Wu, Jun Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4819809/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In the field of unsupervised timing anomaly detection, existing methods face challenges in capturing long-range dependencies and dynamic timings due to the scale of the data and multiple feature dimensions. This paper presents a novel method for timing anomaly detection that effectively extracts long-range dependencies and dynamic timing features by leveraging stacked encoders and gated recurrent units (GRUs). Moreover, it introduces a multi-branch attention mechanism to extract local and global features, thereby enhancing the model's ability to perceive information at different scales. The local attention captures fine-grained time series changes, while the global attention focuses on long-term trends and overarching patterns. Experimental results demonstrate that our method significantly outperforms existing time-series anomaly detection methods across several publicly available datasets, such as SMD, MSL, and SMAP, affirming its superiority in terms of accuracy and robustness. Encoder GRU Multi-Branch Attention Anomaly Detection Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4819809","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":334639042,"identity":"11a21669-10e3-49c2-88bb-aa634863e657","order_by":0,"name":"Shiqian Han","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYBACAyBmBpI8QOoAsiBRWtgSQAISRGoBAx4D4rSYs589/LmgwFqGf3bP58+8bXZ1DOzN2yQYau7g1GLZk5cmPcMgnUfiztlt0rxtyRIMPMfKJBiOPcPtsAM5Zsw8Bod5GG7kbmPmbTsgwSCRYybB2HAYt5bzb4w/g7TI38h5/BmsRf4NAS03cgykQVqADAZpiC08hLS8MQNqSecxvJFmJjnnXLJkG09asUXCMXwOywE67I+1vdyN5Mcf3pTZ8fOzH95440MNbi1QAIkaJmCEMrCBWAmENMC0MP4grHIUjIJRMApGIAAAQ6tL8Zr++d0AAAAASUVORK5CYII=","orcid":"","institution":"Shenyang University of Chemical Technology","correspondingAuthor":true,"prefix":"","firstName":"Shiqian","middleName":"","lastName":"Han","suffix":""},{"id":334639043,"identity":"68888171-2466-4145-9b4e-d642ee559c71","order_by":1,"name":"Junxia Wu","email":"","orcid":"","institution":"Shenyang University of Chemical Technology","correspondingAuthor":false,"prefix":"","firstName":"Junxia","middleName":"","lastName":"Wu","suffix":""},{"id":334639044,"identity":"c17b32f6-6bcf-4f46-a42b-3aecb5230728","order_by":2,"name":"Jun Wang","email":"","orcid":"","institution":"Shenyang University of Chemical Technology","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-07-29 07:14:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4819809/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4819809/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62920654,"identity":"aa9d0c21-c27b-40a6-844f-b0dfcb276194","added_by":"auto","created_at":"2024-08-21 05:29:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":644123,"visible":true,"origin":"","legend":"","description":"","filename":"TiminganomalydetectionbasedonGRU.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4819809/v1_covered_b49985cf-8fbe-4d27-bdc1-5d322683a778.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Timing anomaly detection based on GRU-INEncoder","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Encoder, GRU, Multi-Branch Attention, Anomaly Detection","lastPublishedDoi":"10.21203/rs.3.rs-4819809/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4819809/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In the field of unsupervised timing anomaly detection, existing methods face challenges in capturing long-range dependencies and dynamic timings due to the scale of the data and multiple feature dimensions. 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