On the integration of large scale time seriesdistance matrices into deep visual analytic tools

On the integration of large scale time seriesdistance matrices into deep visual analytic tools | 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 On the integration of large scale time seriesdistance matrices into deep visual analytic tools Inmaculada Santamaria-Valenzuela, Victor Rodriguez-Fernandez, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5196699/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Dec, 2024 Read the published version in Cognitive Computation → Version 1 posted You are reading this latest preprint version Abstract Time series are essential for modelling a lot of activities such as software behavior, heart beats per time, business processes. The analysis of the series data can prevent errors, boost profits, and improve the understanding of behaviors. Among the many techniques available, we can find Deep Learning techniques and Data Mining techniques. In Data Mining, distance matrices between subsequences (similarity matrices, recurrence plots) have already shown their potential on fast large-scale time series behavior analysis. In the Deep Learning, there exists different tools for analyzing the models embedding space for getting insights of the data behavior. DeepVATS is a tool for large time series analysis that allows the visual interaction within the embedding space (latent space) of Deep Learning models and the original data. The training and analysis of the model may result on a large use of computational resources, resulting in a lack of interactivity. To solve this issue, we integrate distance matrices plots within the tool. The incorporation of these plots with the associated downsampling techniques makes DeepVATS a more efficient and user-friendly tool for a first quick analysis of the data, achieving runtimes reductions of up to \(10^4\) seconds, allowing fast preliminary analysis of datasets of up to 7M elements. Also, this incorporation allows us to detect trends, extending its capabilities. The new functionality is tested in three use cases: the M-Toy synthetic dataset for anomaly detection, the S3 synthetic dataset for trend detection and the real-world dataset Pulsus Paradoxus for anomaly checking. Time Series Analysis MPlot Visual Analytics Machine Learning Deep Learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 16 Dec, 2024 Read the published version in Cognitive Computation → 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-5196699","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":379866945,"identity":"cffaa45b-a674-409b-a1ad-8241c04f6812","order_by":0,"name":"Inmaculada Santamaria-Valenzuela","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYDACZiT2AYYK4rUYQLWcId4uiBYGxjYi1Oq2MzB/+Lnjj7xu+9mHB37OO5yn28B8+AM+LWaHGdgke88YGG47k25wsHfb4WKzA2xpEoS0MPC2GTBuO5DGcIB32+HEbQd4zPA6DKiF+ePfNgP7beefMRz8Owekhf8zIYcxSANtSdx2I43hMG8D2BYGAg5jbJOWbTNO3nbjGcNhmWPpxWaH2czwazl/+PDHt21yttvOpzF/fFNjnWd2vPkxXocB46IBhZuAkh6IAgmkahgFo2AUjILhDwBzTE9eJlhN7QAAAABJRU5ErkJggg==","orcid":"","institution":"Technical University of Madrid","correspondingAuthor":true,"prefix":"","firstName":"Inmaculada","middleName":"","lastName":"Santamaria-Valenzuela","suffix":""},{"id":379866946,"identity":"5f9f86d9-5416-4a4f-8b2f-1204971d1b3d","order_by":1,"name":"Victor Rodriguez-Fernandez","email":"","orcid":"","institution":"Technical University of Madrid","correspondingAuthor":false,"prefix":"","firstName":"Victor","middleName":"","lastName":"Rodriguez-Fernandez","suffix":""},{"id":379866947,"identity":"394e7f6e-5715-4562-82e6-d4480f90819c","order_by":2,"name":"David Camacho","email":"","orcid":"","institution":"Technical University of Madrid","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Camacho","suffix":""}],"badges":[],"createdAt":"2024-10-03 07:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5196699/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5196699/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12559-024-10394-x","type":"published","date":"2024-12-16T15:56:59+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":72201849,"identity":"e7d3502b-19a5-4f44-a24c-c46197d36aed","added_by":"auto","created_at":"2024-12-23 16:11:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6969775,"visible":true,"origin":"","legend":"","description":"","filename":"CCMPlot.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5196699/v1_covered_c7887b9f-f7ad-4083-ad2f-efc635ebc3b7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"On the integration of large scale time seriesdistance matrices into deep visual analytic tools","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Time Series Analysis, MPlot, Visual Analytics, Machine Learning, Deep Learning","lastPublishedDoi":"10.21203/rs.3.rs-5196699/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5196699/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTime series are essential for modelling a lot of activities such as software behavior, heart beats per time, business processes. 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