Bitcoin Price Prediction Based on ROA-VMDAlgorithm and CNN-SK-Transformer Model

Bitcoin Price Prediction Based on ROA-VMDAlgorithm and CNN-SK-Transformer Model | 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 Bitcoin Price Prediction Based on ROA-VMDAlgorithm and CNN-SK-Transformer Model Chao Li, Lahuan Li, Yuanhua Li, Jinguo Li, Jia Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7290396/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Driven by its unique production, issuance mechanisms, and markettransaction characteristics, Bitcoin's price exhibits pronounced nonlinear fluctuation features, rendering prediction tasks highly complex. To address this challenge, we propose a Bitcoin price prediction model integrating RIME Optimization Algorithm (ROA)-based Variational Mode Decomposition (VMD), Convolutional Neural Network (CNN), and an improved Transformer (SK-Transformer). Firstly, ROA is employed to optimize the core parameters of VMD (number of modes \(K\) and penalty factor \(\alpha\) ). The optimized VMD method is then applied to decompose the Bitcoin price series into multiple subsequences. Subsequently, these subsequences are reorganized into three sequences --- low-frequency, medium-frequency, and high-frequency --- based on their fuzzy entropy values. The low-frequency components are trained using a CNN model, while the medium-frequency and high-frequency components are modeled via the SK-Transformer architecture. Predictions from these models are aggregated to generate the final forecast, which is evaluated using multiple accuracymetrics.Experimental validation demonstrates that the ROA-VMD-CNN-SK-Transformer model outperforms alternative prediction models across all evaluation metrics,showcasing superior predictive precision. ROA-VMD Algorithm CNN SK-Transformer Bitcoin Price Prediction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 18 Apr, 2026 Reviews received at journal 15 Apr, 2026 Reviews received at journal 14 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers invited by journal 08 Apr, 2026 Editor assigned by journal 06 Aug, 2025 Submission checks completed at journal 06 Aug, 2025 First submitted to journal 04 Aug, 2025 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-7290396","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":623595425,"identity":"b73b730a-d559-48ad-9817-10281daa170e","order_by":0,"name":"Chao Li","email":"","orcid":"","institution":"Shanghai University of Electric Power","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Li","suffix":""},{"id":623595426,"identity":"f9cab244-b9cc-4827-b924-46a8a13102c2","order_by":1,"name":"Lahuan Li","email":"","orcid":"","institution":"Shanghai University of Electric Power","correspondingAuthor":false,"prefix":"","firstName":"Lahuan","middleName":"","lastName":"Li","suffix":""},{"id":623595427,"identity":"6557fe8e-4b2b-4574-bfd6-543100775d70","order_by":2,"name":"Yuanhua Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYBACPgYeIGlgI8fY3sDGkAAWS8CvhQ2spSDNmLnnAElaPhxObJ+RwAYVI6RFIveYxA+Dw8a8M58/e/Cg4g4DP3uOAcPPHfi05KVJ9hiky0nOzjE3SDjzjEGy540BY+8ZfFpyzCR4DKyNDWfnsEkkth1mMLiRY8DM2IZfi+QfA+bE/TePP5NI/HeYwZ4YLdI8Bs6JjTMYzCQSG4C2SBDSwvPG2FrGIM2YsQfowoRjz3gkzjwrONiLRwswfAxvvvkDisrjzyR/1NyR429P3vjgJx4tDAIJKNwDPGASjwagNajS+BWPglEwCkbByAQA2phPBBnahzwAAAAASUVORK5CYII=","orcid":"","institution":"Shanghai University of Electric Power","correspondingAuthor":true,"prefix":"","firstName":"Yuanhua","middleName":"","lastName":"Li","suffix":""},{"id":623595428,"identity":"d69a9a35-0086-4542-a07c-6429d9d680c5","order_by":3,"name":"Jinguo Li","email":"","orcid":"","institution":"Shanghai University of Electric Power","correspondingAuthor":false,"prefix":"","firstName":"Jinguo","middleName":"","lastName":"Li","suffix":""},{"id":623595429,"identity":"a8e74687-1b18-4f74-84a8-0688017e66e2","order_by":4,"name":"Jia Lin","email":"","orcid":"","institution":"Shanghai University of Electric Power","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2025-08-04 11:08:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7290396/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7290396/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107481398,"identity":"bd09adca-9189-4b3e-90d6-e96ad5b4b20b","added_by":"auto","created_at":"2026-04-22 02:17:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":564127,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7290396/v1_covered_204457e7-b719-43f4-8001-05b1da89dc6b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bitcoin Price Prediction Based on ROA-VMDAlgorithm and CNN-SK-Transformer Model","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":"the-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"ROA-VMD Algorithm, CNN, SK-Transformer, Bitcoin Price Prediction","lastPublishedDoi":"10.21203/rs.3.rs-7290396/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7290396/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDriven by its unique production, issuance mechanisms, and markettransaction characteristics, Bitcoin's price exhibits pronounced nonlinear fluctuation features, rendering prediction tasks highly complex. To address this challenge, we propose a Bitcoin price prediction model integrating RIME Optimization Algorithm (ROA)-based Variational Mode Decomposition (VMD), Convolutional Neural Network (CNN), and an improved Transformer (SK-Transformer). Firstly, ROA is employed to optimize the core parameters of VMD (number of modes \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(K\\)\u003c/span\u003e\u003c/span\u003e and penalty factor \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\alpha\\)\u003c/span\u003e\u003c/span\u003e). The optimized VMD method is then applied to decompose the Bitcoin price series into multiple subsequences. Subsequently, these subsequences are reorganized into three sequences --- low-frequency, medium-frequency, and high-frequency --- based on their fuzzy entropy values. 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