Research on Carbon Asset Price Prediction Algorithm Based on Time Series Analysis

Research on Carbon Asset Price Prediction Algorithm Based on Time Series Analysis | 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 Research on Carbon Asset Price Prediction Algorithm Based on Time Series Analysis Jiawen Liu, Chungeng He This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6827086/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 One of the most important market mechanisms for fostering sustainable development and lowering carbon emissions is carbon trading. A more efficient carbon market and more effective environmental policies depend on reliable carbon price forecasts. However, end consequences and the disorderly character of price for carbon sequences have prevented significant advances in the validity of these predictions. Using the EPO-CCPLSTM time series analysis technique, this study introduces a novel model for carbon price prediction. With EPO-CPPLSTM configured to predict carbon price series components, carbon prices are estimated by combining the outputs of the LSTM components. According to the findings of the empirical study, the suggested model achieves better prediction accuracy than the comparative models. The proposed model has provided an accuracy of 99.5% and 95.6% respectively. Carbon market trading operations will become more efficient with the model's implementation, which will also promote clean development across a number of industries. carbon asset price carbon trading prediction algorithm time series analysis 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-6827086","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":472367045,"identity":"118f78fa-3855-463c-9e11-f05e1557aaea","order_by":0,"name":"Jiawen Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYBAC9mYwdYCHgZn/4YMPFSAhBgbGBjxaGOFa2HmYDWecYWDgYSakBSJ5gIGBn4dNmreNGC3tzM8kPu44IGNwmPewMe88m8T9zMwHH85gsJPTxaGPsZnNTHLmmQM8Bof5Eh/O3ZaW2MPMlmy4gSHZ2OwATr+Y3eZtA2lhMDZ4u+0wUAuPmeQDhgOJ23BqYf92+y9Ei5kE7xwitAg285jdZgRrAarkbYBq2YBHizQzT/nPXqAWycNAL8w4lmbcA2YY4PYLH//xzQY/2/7Z850/fPDBhxob2fb25oMPeyrs5HBpwQUMSFM+CkbBKBgFowAVAABo512KPX3jhQAAAABJRU5ErkJggg==","orcid":"","institution":"Guangdong Power Grid Company","correspondingAuthor":true,"prefix":"","firstName":"Jiawen","middleName":"","lastName":"Liu","suffix":""},{"id":472367046,"identity":"4fa67bd7-bc3a-4dad-9e05-4f5f244a62ba","order_by":1,"name":"Chungeng He","email":"","orcid":"","institution":"Guangdong Power Grid Company","correspondingAuthor":false,"prefix":"","firstName":"Chungeng","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2025-06-05 08:38:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6827086/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6827086/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88687548,"identity":"d8792c01-975a-4deb-af3b-7da8cf5db703","added_by":"auto","created_at":"2025-08-09 13:02:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":613649,"visible":true,"origin":"","legend":"","description":"","filename":"EI405.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6827086/v1_covered_5c5f68d3-b2b8-4cb0-b1a4-b603bfb30688.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on Carbon Asset Price Prediction Algorithm Based on Time Series Analysis","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":"carbon asset price, carbon trading, prediction algorithm, time series analysis","lastPublishedDoi":"10.21203/rs.3.rs-6827086/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6827086/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOne of the most important market mechanisms for fostering sustainable development and lowering carbon emissions is carbon trading. 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