Wavelet transform and Deep Weight Averaging model for price and illiquidity prediction cryptocurrencies using high-dimensional features

Wavelet transform and Deep Weight Averaging model for price and illiquidity prediction cryptocurrencies using high-dimensional features | 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 Method Article Wavelet transform and Deep Weight Averaging model for price and illiquidity prediction cryptocurrencies using high-dimensional features Alireza, Amir Ali, Mohammad, Ramin, Sasan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6324973/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 Cryptocurrencies are alternative payment methods that are created using encrypted algorithms. Encryption technologies mean that cryptocurrencies act as both a currency and a virtual accounting system. The global crypto market value is \$2.9 trillion. Hence, it requires high investment requirements. One of the challenging issues in cryptocurrencies is illiquidity. Due to behavioural chaos in the market, some currencies have severe dumps and pumps, which cause concerns for investors. This paper deals with price prediction and illiquidity prediction (converting one asset to another while maintaining its value). The proposed Wavelet Deep average model uses a combination of Wavelet transform and average deep learning models for the final prediction. This model uses the hash rate information of currencies as the main inputs. Then, it achieves the selection of a subset of features using a Random Forest(RF). The selected features are designed by a Wavelet and are considered as the input to the deep network. Four currencies, BTC, Dogecoin, Ethereum(ETH), and Bitcoin Cash(BCH), were considered for model evaluation. In Bitcoin prediction, the lowest MAE for price prediction and illiquidity was achieved, which was 1.19 and 1.49, respectively. Also, the proposed model achieved MAE of 1.99, 3.69, and 2.99 for the illiquidity of three currencies Dogecoin, ETH, and BCH. The implementation codes are available in Illiquidity prediction Price prediction Deep weight averaging model Wavelet transform High-dimensional features Hashrate feature 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. 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-6324973","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":435145709,"identity":"2daff8f9-b093-4c90-8169-9b3bd0d290d0","order_by":0,"name":"Alireza","email":"","orcid":"","institution":"Maleki","correspondingAuthor":false,"prefix":"","firstName":"","middleName":"","lastName":"Alireza","suffix":""},{"id":435145710,"identity":"f81814f4-33a8-416f-babc-d8694ca95540","order_by":1,"name":"Amir Ali","email":"","orcid":"","institution":"Bengari","correspondingAuthor":false,"prefix":"","firstName":"Amir","middleName":"","lastName":"Ali","suffix":""},{"id":435145711,"identity":"291c0628-555e-4be4-ac96-6cc7063c8799","order_by":2,"name":"Mohammad","email":"","orcid":"","institution":"Ghadiri","correspondingAuthor":false,"prefix":"","firstName":"","middleName":"","lastName":"Mohammad","suffix":""},{"id":435145712,"identity":"d7f0cdb2-5bcc-4084-880d-f13cd5681235","order_by":3,"name":"Ramin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYBAC+wYGhgMQJjOUZmBswKvF4ABcC1sC8VqggMeAOIcZHD+deLgwxyZxfvuZz58L2xjk+RuY2z7g02Lfk7vh8MxtaYkbzuRuMJ7ZxmA44wBj8wx8WuwYgFp4tx1O3ABkJPO2MTBuYGBsxuswY/63EC3z+988OAzUYk9Qi+EMqC0NN3IYm4FaEglqMbgBtiXNeMONZ8bMPOckkmccJqTlfO7mz7zbbGTn9yc//sxTZmPb397+GK8WdCABTAUkaRgFo2AUjIJRgA0AAL6wTj73j+mcAAAAAElFTkSuQmCC","orcid":"","institution":"Mousa","correspondingAuthor":true,"prefix":"","firstName":"","middleName":"","lastName":"Ramin","suffix":""},{"id":435145713,"identity":"fc24b577-7541-4fb5-82a5-855774a47f33","order_by":4,"name":"Sasan","email":"","orcid":"","institution":"Mazaheri","correspondingAuthor":false,"prefix":"","firstName":"","middleName":"","lastName":"Sasan","suffix":""}],"badges":[],"createdAt":"2025-03-28 05:22:06","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-6324973/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6324973/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79540853,"identity":"87e743c5-dd45-4d53-8382-7620f65cc992","added_by":"auto","created_at":"2025-03-31 03:32:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":508709,"visible":true,"origin":"","legend":"","description":"","filename":"Submition.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6324973/v1_covered_06e9d819-8b0e-45e2-9eb6-49dacf0e7123.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eWavelet transform and Deep Weight Averaging model for price and illiquidity prediction cryptocurrencies using\u003c/p\u003e\n\u003cp\u003ehigh-dimensional features\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":"Illiquidity prediction, Price prediction, Deep weight averaging model, Wavelet transform, High-dimensional features, Hashrate feature","lastPublishedDoi":"10.21203/rs.3.rs-6324973/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6324973/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCryptocurrencies are alternative payment methods that are created using encrypted algorithms. Encryption technologies mean that cryptocurrencies act as both a currency and a virtual accounting system. The global crypto market value is \\$2.9 trillion. Hence, it requires high investment requirements. One of the challenging issues in cryptocurrencies is illiquidity. Due to behavioural chaos in the market, some currencies have severe dumps and pumps, which cause concerns for investors. This paper deals with price prediction and illiquidity prediction (converting one asset to another while maintaining its value). The proposed Wavelet Deep average model uses a combination of Wavelet transform and average deep learning models for the final prediction. This model uses the hash rate information of currencies as the main inputs. Then, it achieves the selection of a subset of features using a Random Forest(RF). The selected features are designed by a Wavelet and are considered as the input to the deep network. Four currencies, BTC, Dogecoin, Ethereum(ETH), and Bitcoin Cash(BCH), were considered for model evaluation. In Bitcoin prediction, the lowest MAE for price prediction and illiquidity was achieved, which was 1.19 and 1.49, respectively. Also, the proposed model achieved MAE of 1.99, 3.69, and 2.99 for the illiquidity of three currencies Dogecoin, ETH, and BCH. 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