A  new method of financial multivariate time series forecasting based on complex network attention mechanism

A new method of financial multivariate time series forecasting based on complex network attention mechanism | 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 Article A new method of financial multivariate time series forecasting based on complex network attention mechanism Xiaoli Xiong, Dongji Zhang, Dongze Xu, Hong Chen, Qin Xiao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4319238/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 Due to the high degree of correlation and interaction among multiple financial series, as well as the non-stationary and nonlinear characteristics of these series, predicting financial multivariate time series accurately using traditional time series analysis methods is challenging. However, neural network models can effectively handle nonlinearity and instability, enabling relatively accurate predictions. In this paper, we propose a novel method called MTP-CVGAT(Multivariate Time-Series Prediction via Correlation and Visible Graphs Attention Network), which combines the correlation and visibility features of multivariate series. Taking into account accommodation and catering stocks of companies listed on China's Shanghai-Shenzhen A-share market as an example, our results demonstrate that the prediction performance of MTP-CVGAT surpasses that of other models including Long short-term memory neural network model(LSTM), multi-variable time series anomaly detection model based on graph attention network, and transfer learning model based on Wang X G et al.'s approach. Physical sciences/Physics/Statistical physics thermodynamics and nonlinear dynamics/Complex networks Physical sciences/Physics/Statistical physics thermodynamics and nonlinear dynamics/Nonlinear phenomena 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-4319238","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":300497780,"identity":"4ae2aa78-b848-49ca-bf54-8f7863ecb01d","order_by":0,"name":"Xiaoli Xiong","email":"","orcid":"","institution":"Shanghai Institude of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiaoli","middleName":"","lastName":"Xiong","suffix":""},{"id":300497783,"identity":"dac324c9-6b61-48b0-9b40-dbff02641e82","order_by":1,"name":"Dongji Zhang","email":"","orcid":"","institution":"Shanghai Institude of Technology","correspondingAuthor":false,"prefix":"","firstName":"Dongji","middleName":"","lastName":"Zhang","suffix":""},{"id":300497785,"identity":"6bf72839-2ed4-45bd-b811-5c7766af2cee","order_by":2,"name":"Dongze Xu","email":"","orcid":"","institution":"Shanghai Institude of Technology","correspondingAuthor":false,"prefix":"","firstName":"Dongze","middleName":"","lastName":"Xu","suffix":""},{"id":300497787,"identity":"4c392a53-c3cc-4b8b-bffc-856bd4c43543","order_by":3,"name":"Hong Chen","email":"","orcid":"","institution":"Shanghai Institude of Technology","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Chen","suffix":""},{"id":300497789,"identity":"f3916c94-96e6-43c4-a629-1236906c96f8","order_by":4,"name":"Qin Xiao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYJACZiC2Y2NmbHyQUFFDvJZkfvbmwwYPzhwjXgvjzJ5jaZIPW5gJK5efkXv4c0GNDbPBjRyzisQGNgb+9u4EvFoMbuSlSc84lsYH0nIjcYcMg8SZsxvwa5HIMWPmYTvMDNFyhg0okotfi/yMHOPPPP8OM24AailIbGMmrIXhRo6BNG/bYbD3GYjSYnDmjZk0b18aOJAlEs4c4yHoF/l2kMO+2YCj8uOPiho5/vZeAg5DBzykKR8Fo2AUjIJRgBUAAGRHR5l0h2NVAAAAAElFTkSuQmCC","orcid":"","institution":"Shanghai Institude of Technology","correspondingAuthor":true,"prefix":"","firstName":"Qin","middleName":"","lastName":"Xiao","suffix":""}],"badges":[],"createdAt":"2024-04-24 15:23:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4319238/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4319238/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59554995,"identity":"b7a26c89-29cc-4cf6-973e-f9d91d466fa9","added_by":"auto","created_at":"2024-07-03 07:10:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2048987,"visible":true,"origin":"","legend":"","description":"","filename":"anewmethod.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4319238/v1_covered_47cfda9d-14ff-49bc-bc7e-ebdd6c0ad909.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A new method of financial multivariate time series forecasting based on complex network attention mechanism","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":"","lastPublishedDoi":"10.21203/rs.3.rs-4319238/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4319238/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Due to the high degree of correlation and interaction among multiple financial series, as well as the non-stationary and nonlinear characteristics of these series, predicting financial multivariate time series accurately using traditional time series analysis methods is challenging. 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