Improving Real-Time Concept Drift Detection using a Hybrid Transformer-Autoencoder Framework

Improving Real-Time Concept Drift Detection using a Hybrid Transformer-Autoencoder Framework | 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 Improving Real-Time Concept Drift Detection using a Hybrid Transformer-Autoencoder Framework N Harshit, K Mounvik This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7189931/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 applied machine learning, concept drift, which is either gradual or abrupt changes in data distribution, can significantly reduce model performance. Typ- ical detection methods like statistical tests or reconstruction-based models are generally reactive and not very sensitive in early detection. Our study pro- poses a hybrid framework consisting of Transformers and Autoencoders to model complex temporal dynamics and provide online drift detection. We create a distinct Trust Score methodology, which includes signals on (1) statistical and reconstruction-based drift metrics (more specifically, PSI, JSD, Transformer-AE error, (2) prediction uncertainty, (3) rules violations, and (4) trend of classi- fier error) aligned with the combined metrics defined by the Trust Score. Using a time-sequenced airline passenger data set with synthetic drift, our proposed model allows for a better detection of drift using as a whole and at different detec- tions thresholds for both sensitivity and interpretability compared to baseline methods and provides a strong pipeline for drift detection in real time for applied machine learning. We evaluated performance using a time-sequenced airline pas- senger dataset having the gradually injected stimulus of drift in expectations, e.g., permuted ticket prices in later batches, broken into 10 time segments [ 1 ]. In the data, our results support that the Transformation-Autoencoder detected drift earlier and with more sensitivity than the autoencoders commonly used in the literature, and provided improved modelling above more error rates and log- ical violations. Therefore, a robust framework was developed to reliably monitor concept drift. Transformer-Autoencoder Real-Time Detection Trust Score 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. 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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-7189931","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":490004118,"identity":"71663f08-03d6-45fc-baf6-f582ed9e2b8b","order_by":0,"name":"N Harshit","email":"data:image/png;base64,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","orcid":"","institution":"Vellore Institute of Technology (VIT-AP)","correspondingAuthor":true,"prefix":"","firstName":"N","middleName":"","lastName":"Harshit","suffix":""},{"id":490004119,"identity":"27dc4c18-48c9-48c4-87c8-dd28196ca021","order_by":1,"name":"K Mounvik","email":"","orcid":"","institution":"Vellore Institute of Technology (VIT-AP)","correspondingAuthor":false,"prefix":"","firstName":"K","middleName":"","lastName":"Mounvik","suffix":""}],"badges":[],"createdAt":"2025-07-22 19:08:07","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7189931/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7189931/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88506143,"identity":"c2c264d5-48d0-4525-a685-2fcf796590da","added_by":"auto","created_at":"2025-08-07 07:31:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":424542,"visible":true,"origin":"","legend":"","description":"","filename":"ArticleTitle3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7189931/v1_covered_08865863-371c-4ec1-b6cd-33338d058204.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Improving Real-Time Concept Drift Detection using a Hybrid Transformer-Autoencoder Framework","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":true,"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":"Transformer-Autoencoder, Real-Time Detection, Trust Score","lastPublishedDoi":"10.21203/rs.3.rs-7189931/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7189931/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn applied machine learning, concept drift, which is either gradual or abrupt changes in data distribution, can significantly reduce model performance. Typ- ical detection methods like statistical tests or reconstruction-based models are generally reactive and not very sensitive in early detection. Our study pro- poses a hybrid framework consisting of Transformers and Autoencoders to model complex temporal dynamics and provide online drift detection. We create a distinct Trust Score methodology, which includes signals on (1) statistical and reconstruction-based drift metrics (more specifically, PSI, JSD, Transformer-AE error, (2) prediction uncertainty, (3) rules violations, and (4) trend of classi- fier error) aligned with the combined metrics defined by the Trust Score. Using a time-sequenced airline passenger data set with synthetic drift, our proposed model allows for a better detection of drift using as a whole and at different detec- tions thresholds for both sensitivity and interpretability compared to baseline methods and provides a strong pipeline for drift detection in real time for applied machine learning. We evaluated performance using a time-sequenced airline pas- senger dataset having the gradually injected stimulus of drift in expectations, e.g., permuted ticket prices in later batches, broken into 10 time segments [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In the data, our results support that the Transformation-Autoencoder detected drift earlier and with more sensitivity than the autoencoders commonly used in the literature, and provided improved modelling above more error rates and log- ical violations. 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