Forecasting Cryptocurrency Prices Using Hybrid Long Short – Term Memory and Long Short – Term Memory Variants

Forecasting Cryptocurrency Prices Using Hybrid Long Short – Term Memory and Long Short – Term Memory Variants | 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 Forecasting Cryptocurrency Prices Using Hybrid Long Short – Term Memory and Long Short – Term Memory Variants Thuan Dinh Nguyen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5374266/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 Nowadays, virtual currency has become mainstream and attracts many investors and the environment. People spend large amounts of money in the hope that their trades are buying which occurs when the price of a cryptocurrency falls and selling when it rises in search of a profit. However, due to the volatility of these currencies, it is difficult for investors to decide whether to buy, sell or hold. So in this research, we build a valuable daily data set of virtual currencies, especially Bitcoin, Ethereum, and Binance. We rely on it to build deep learning models along with model improvement techniques and model fusion techniques to improve model performance. We use basic regulatory response measurements to evaluate the model’s results, i.e. MAE, MAPE, MSE, and RMSE. Cryptocurrency Deep Learning Long Short-Term Memory 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-5374266","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":375745416,"identity":"9939c8b6-6896-4416-ad92-21b33bb83f87","order_by":0,"name":"Thuan Dinh Nguyen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYDACCQjF2M/A2ACkmQnr4IFpmdnA2NhAmpYNB8DWEKHFXrrH8HPhjjuym28ktz9gqLBObJDIPYDfFpkzxtIzzzwz3nYjEeiwM+lALXkJBByWYyDN23Y4EayFEcho4DljQEiL8W+Qls0zQFr+EafFDGzLBgmQlgagFvYeAlpupJVZA7UYzzjzsHFGwrF04zZCWthnJG++DdQi29+e/uDDhxpr2X5mHvxaUEECELORoH4UjIJRMApGAQ4AAB/4R+wvk0KOAAAAAElFTkSuQmCC","orcid":"","institution":"Vietnam National University, Ho Chi Minh City","correspondingAuthor":true,"prefix":"","firstName":"Thuan","middleName":"Dinh","lastName":"Nguyen","suffix":""}],"badges":[],"createdAt":"2024-11-01 15:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5374266/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5374266/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70061583,"identity":"4432dd03-aeca-456c-b9e3-c4e98e67e308","added_by":"auto","created_at":"2024-11-28 03:02:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1435401,"visible":true,"origin":"","legend":"","description":"","filename":"ForecastingCryptocurrencyPricesUsingHybridLSTMandLSTMVariants.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5374266/v1_covered_1c799a5a-9d69-4237-90b1-e8ab2df2e5c5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eForecasting Cryptocurrency Prices Using Hybrid Long Short – Term Memory and Long Short – Term Memory Variants\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Cryptocurrency, Deep Learning, Long Short-Term Memory","lastPublishedDoi":"10.21203/rs.3.rs-5374266/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5374266/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Nowadays, virtual currency has become mainstream and attracts many investors and the environment. 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