A Novel Multi-Window Time-Series Forecasting of Major Cryptocurrencies Using Hybrid 1D-CNN–LSTM Framework

A Novel Multi-Window Time-Series Forecasting of Major Cryptocurrencies Using Hybrid 1D-CNN–LSTM 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 A Novel Multi-Window Time-Series Forecasting of Major Cryptocurrencies Using Hybrid 1D-CNN–LSTM Framework Hoa Tran Thai, Thanh Manh Le, Cuong H. Nguyen-Dinh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8217257/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 Predicting cryptocurrency prices is challenging due to high market volatility and nonlinear dynamics of digital asset markets. This paper proposes a novel hybrid deep learning model combining framework that integrates one-dimensional Convolutional Neural Networks (1D-CNN) and Long Short-Term Memory (LSTM) networks to forecast the closing prices of Bitcoin (BTC), Ethereum (ETH), and BinanceCoin (BNB). The 1D-CNN component captures short-term local patterns captures local price patterns, while the LSTM component models long-term temporal dependencies. Using historical daily transaction data and a sliding window approach, we evaluated the hybrid model against standalone 1D-CNN and LSTM baseline deep learning models across multiple window configurations and various window sizes. Experimental results demonstrate that the proposed hybrid model consistently outperforms the baseline models, especially in medium- and long-term forecasting horizons, achieving lower prediction errors (MSE, MAE, RMSE) and higher R² scores. This highlights the effectiveness of integrating CNN and LSTM for improved cryptocurrency price forecasting and provides practical value enhanced cryptocurrency price prediction accuracy, offering valuable insights for investors and researchers navigating these complex markets. Hybrid deep learning model 1D-CNN LSTM cryptocurrency price forecasting 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-8217257","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":552227082,"identity":"4995332b-45e7-4ae0-92b5-be4174e0218c","order_by":0,"name":"Hoa Tran Thai","email":"","orcid":"","institution":"Hue University","correspondingAuthor":false,"prefix":"","firstName":"Hoa","middleName":"Tran","lastName":"Thai","suffix":""},{"id":552227083,"identity":"8d0987e2-f537-4b2d-a348-f4558b841412","order_by":1,"name":"Thanh Manh Le","email":"","orcid":"","institution":"Hue University","correspondingAuthor":false,"prefix":"","firstName":"Thanh","middleName":"Manh","lastName":"Le","suffix":""},{"id":552227084,"identity":"f98d8344-57da-4d55-9c34-f02c8dd395b7","order_by":2,"name":"Cuong H. 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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":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":"Hybrid deep learning model, 1D-CNN, LSTM, cryptocurrency price forecasting","lastPublishedDoi":"10.21203/rs.3.rs-8217257/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8217257/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePredicting cryptocurrency prices is challenging due to high market volatility and nonlinear dynamics of digital asset markets. This paper proposes a novel hybrid deep learning model combining framework that integrates one-dimensional Convolutional Neural Networks (1D-CNN) and Long Short-Term Memory (LSTM) networks to forecast the closing prices of Bitcoin (BTC), Ethereum (ETH), and BinanceCoin (BNB). The 1D-CNN component captures short-term local patterns captures local price patterns, while the LSTM component models long-term temporal dependencies. Using historical daily transaction data and a sliding window approach, we evaluated the hybrid model against standalone 1D-CNN and LSTM baseline deep learning models across multiple window configurations and various window sizes. Experimental results demonstrate that the proposed hybrid model consistently outperforms the baseline models, especially in medium- and long-term forecasting horizons, achieving lower prediction errors (MSE, MAE, RMSE) and higher R\u0026sup2; scores. This highlights the effectiveness of integrating CNN and LSTM for improved cryptocurrency price forecasting and provides practical value enhanced cryptocurrency price prediction accuracy, offering valuable insights for investors and researchers navigating these complex markets.\u003c/p\u003e","manuscriptTitle":"A Novel Multi-Window Time-Series Forecasting of Major Cryptocurrencies Using Hybrid 1D-CNN–LSTM Framework","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-03 08:41:05","doi":"10.21203/rs.3.rs-8217257/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"edbcf1d4-8135-403d-9a94-f53f887d7568","owner":[],"postedDate":"December 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-03T10:24:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-03 08:41:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8217257","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8217257","identity":"rs-8217257","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}