Forecasting of Bitcoin Prices Using Hashrate Features: Wavelet and Deep Stacking approach

Forecasting of Bitcoin Prices Using Hashrate Features: Wavelet and Deep Stacking approach | 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 of Bitcoin Prices Using Hashrate Features: Wavelet and Deep Stacking approach Gholamreza Heidary, Meysam Afrookhteh, Hooman Khaloo, Ramin Mousa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4406210/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 Digital currencies have become popular in the last decade due to their non-dependency and decentralized nature. The price of these currencies has seen a lot of fluctuations at times, which has increased the need for prediction. As their most popular, Bitcoin(BTC) has become a research hotspot. The main challenge and trend of digital currencies, especially BTC, is price fluctuations, which require studying the basic price prediction model. This research presents a classification and regression model based on stack deep learning that uses a wavelet to remove noise to predict movements and prices of BTC at different time intervals. The proposed model based on the stacking technique uses models based on deep learning, especially neural networks and transformers, for one, seven, thirty and ninety-day forecasting. Three feature selection models, Chi2, RFE and Embedded, were also applied to the data in the pre-processing stage. The classification model achieved 63% accuracy for predicting the next day and 64%, 67% and 82% for predicting the seventh, thirty and ninety days, respectively. For daily price forecasting, the percentage error was reduced to 0.58, while the error ranged from 2.72% to 2.85% for seven- to ninety-day horizons. These results show that the proposed model performed better than other models in the literature. Artificial Intelligence and Machine Learning Price prediction Price classification Hash rate deep Stack hybrid models Full Text Additional Declarations The authors declare no competing interests. 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. 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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-4406210","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":301390421,"identity":"1f468b50-4dc1-499f-8328-2dac7974440b","order_by":0,"name":"Gholamreza Heidary","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Gholamreza","middleName":"","lastName":"Heidary","suffix":""},{"id":301390422,"identity":"1d609af4-832f-4c6f-b453-f15b30497aa6","order_by":1,"name":"Meysam Afrookhteh","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Meysam","middleName":"","lastName":"Afrookhteh","suffix":""},{"id":301390423,"identity":"0beccc87-a84c-498c-989c-5597cdc568f1","order_by":2,"name":"Hooman Khaloo","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hooman","middleName":"","lastName":"Khaloo","suffix":""},{"id":301390424,"identity":"f076245a-030b-4d74-836e-3ab883b92db2","order_by":3,"name":"Ramin Mousa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYBAC+wYGhgMQJjOUZmBswKvF4ABcC1sC8VqggMeAOIcZHD+deLgwxyZxfvuZz58L2xjk+RuY2z7g02Lfk7vh8MxtaYkbzuRuMJ7ZxmA44wBj8wx8WuwYgFp4tx1O3ABkJPO2MTBuYGBsxuswY/63EC3z+988OAzUYk9Qi+EMqC0NN3IYm4FaEglqMbgBtiXNeMONZ8bMPOckkmccJqTlfO7mz7zbbGTn9yc//sxTZmPb397+GK8WdCABTAUkaRgFo2AUjIJRgA0AAL6wTj73j+mcAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Ramin","middleName":"","lastName":"Mousa","suffix":""}],"badges":[],"createdAt":"2024-05-11 16:55:03","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-4406210/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4406210/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56425588,"identity":"d52ec8a4-068e-4443-8243-ace138d09409","added_by":"auto","created_at":"2024-05-14 04:23:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":522143,"visible":true,"origin":"","legend":"","description":"","filename":"snarticle.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4406210/v1_covered_f1cb99a5-60a4-47ca-aa1b-6660d61412e3.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eForecasting of Bitcoin Prices Using Hashrate Features: Wavelet and Deep Stacking approach\u003c/p\u003e","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":"Price prediction, Price classification, Hash rate, deep Stack, hybrid models","lastPublishedDoi":"10.21203/rs.3.rs-4406210/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4406210/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDigital currencies have become popular in the last decade due to their non-dependency and decentralized nature. 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