Comparative Estimation of Digital Device Ownership via Time Series Models Based on Pisa Data: Lstm, Gru, Arima and Prophet Models

Comparative Estimation of Digital Device Ownership via Time Series Models Based on Pisa Data: Lstm, Gru, Arima and Prophet Models | 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 Article Comparative Estimation of Digital Device Ownership via Time Series Models Based on Pisa Data: Lstm, Gru, Arima and Prophet Models Ahmet Yıldırım, Ayşe Tuğba Yapıcı This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9136798/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract This study aims to make projections regarding digital device ownership among 15-year-old students in Türkiye in 2031, using data from previous PISA cycles. To generate estimations for 2031, time series LSTM, GRU, ARIMA, and Prophet models were employed. To determine the models' performance, MAE, MSE, RMSE, DTW, and R 2 metrics were estimated by categorizing PISA's three-year cycles and dividing them into 80% training and 20% test data. When examining the metric scores, the GRU model demonstrated the highest performance with values of 0.96 R 2 , 0.06 MAE, 0.008 MSE, 0.087 RMSE, and 18 DTW. The prediction results obtained using the GRU method indicate that the number of computers and mobile phones will increase much faster than the number of televisions in the future years. The results of the study demonstrate that the data from PISA cycles can be analyzed using time series methods, while also showing that the GRU model yields better results in educational estimation studies. The GRU model's projections for 2031 not only illustrate how students' access to digital devices may evolve in the future but also provide a temporal window of opportunity for developing countermeasures and policies to address this situation. Physical sciences/Engineering Physical sciences/Mathematics and computing Artifical intelligence Digital device ownership Educational infrastructure PISA Time series estimation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 13 Apr, 2026 Reviews received at journal 05 Apr, 2026 Reviewers agreed at journal 29 Mar, 2026 Reviews received at journal 27 Mar, 2026 Reviewers agreed at journal 27 Mar, 2026 Reviewers invited by journal 23 Mar, 2026 Editor assigned by journal 20 Mar, 2026 Editor invited by journal 20 Mar, 2026 Submission checks completed at journal 19 Mar, 2026 First submitted to journal 19 Mar, 2026 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. 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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-9136798","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":611319973,"identity":"40888d6d-d05c-4f2a-a2a8-1af783c29af8","order_by":0,"name":"Ahmet Yıldırım","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYDACCQjFw8eQ2MCQwGCTYADmEtJyAKiGDaIlDa5FgpAWBjageiA4TFgL/+zuxMcfGA7LsLEnt314mHM+z1wigfHB2zaGOvMGHJbcObvZ4ADDYR42nofNMxK33S62nJHAbDi3jUFC5gAOa27kbpM4+A+oRSKxmQGoJXHDmQNs0rxALbhcJn8jd/sPsC0QLedAWth/49NiALSFAUnLgcQNxxvYmPFpMbyRu1niDEM62C9ALcmJO9sbmyXnnJOQnIFDi9yN3I0fKhis7fnZ0x8z/txml7idmfnghzdlNvy4IwYMmpE5jA0M+GISCuoIKRgFo2AUjIKRDAAvOFrjBWKGzwAAAABJRU5ErkJggg==","orcid":"","institution":"Ankara Hacı Bayram Veli University","correspondingAuthor":true,"prefix":"","firstName":"Ahmet","middleName":"","lastName":"Yıldırım","suffix":""},{"id":611319974,"identity":"734333aa-938e-40ca-8458-91c2488aeeda","order_by":1,"name":"Ayşe Tuğba Yapıcı","email":"","orcid":"","institution":"Doğuş University","correspondingAuthor":false,"prefix":"","firstName":"Ayşe","middleName":"Tuğba","lastName":"Yapıcı","suffix":""}],"badges":[],"createdAt":"2026-03-16 10:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9136798/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9136798/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105565732,"identity":"4436159b-8487-40b6-a6a5-2cf94779efaf","added_by":"auto","created_at":"2026-03-27 12:54:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1083568,"visible":true,"origin":"","legend":"","description":"","filename":"ScientificReports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9136798/v1_covered_4b1aae60-543c-4396-9597-c32d57a2fcd4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eComparative Estimation of Digital Device Ownership via Time Series Models Based on Pisa Data: Lstm, Gru, Arima and Prophet Models\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Artifical intelligence, Digital device ownership, Educational infrastructure, PISA, Time series estimation","lastPublishedDoi":"10.21203/rs.3.rs-9136798/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9136798/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aims to make projections regarding digital device ownership among 15-year-old students in T\u0026uuml;rkiye in 2031, using data from previous PISA cycles. 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