ST Stock Price Forecasting with the PSO-KAN-Transformer Hybrid Model

ST Stock Price Forecasting with the PSO-KAN-Transformer Hybrid Model | 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 ST Stock Price Forecasting with the PSO-KAN-Transformer Hybrid Model Shuaiqi Peng, Yinkui Li, Bin Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8927196/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 The rapid advancement of big data and Internet of Things (IoT) technologies has expanded the application scope of high-dimensional time series data. To address the limited interpretability of Multilayer Perceptron models in ST stock price prediction, this study enhances the internal structure of the Transformer by replacing the MLP layer with a KAN layer, thereby improving the interpretability of the Transformer in stock price forecasting. For experimental analysis, empirical research is conducted on a dataset including the SSE 50 Index, SZSE 100 Index, and two ST stocks. Experimental results show that the PSO-KAN-Transformer hybrid model achieves superior performance in time series prediction, reducing the mean square error by an average of 18.85% compared with the original Transformer. This indicates that the proposed model has improved accuracy and interpretability. KAN Transformer PSO-KAN-Transformer Stock price prediction 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-8927196","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":602779899,"identity":"f95163ce-d241-4a88-aaad-6d54937b2aa4","order_by":0,"name":"Shuaiqi Peng","email":"","orcid":"","institution":"Qinghai Minzu University","correspondingAuthor":false,"prefix":"","firstName":"Shuaiqi","middleName":"","lastName":"Peng","suffix":""},{"id":602779908,"identity":"5e68caa0-eb51-4277-99fc-21c6ceec4fcd","order_by":1,"name":"Yinkui Li","email":"","orcid":"","institution":"Qinghai Minzu University","correspondingAuthor":false,"prefix":"","firstName":"Yinkui","middleName":"","lastName":"Li","suffix":""},{"id":602779909,"identity":"82b9ed22-4eb4-4754-ac40-48daaaa73d16","order_by":2,"name":"Bin Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYBACA2YGBmYQg03+YfuPDzYgJmPjAaK08DMkN0jOSANracCvhQGqRbIhvUGaJw0iileLOTvv4c8FNXfsNhw42GBsk2CTuLb9MNCWGptoXFosm/kSjGcce5a84WBjQ3JOQpqx2ZlEoJZjabkNuBx2mMcgmYftcLLBYcaGw7k/DsuZHQBqAbHxaTnM8w+o5RhjY7NFwmEes/MPCWoxbOZtO2wn2cPYzMyQALTlBgFbLJt5jJl5+w4n8EswtjH2gPxyA2hLAh6/mPOfMf7M8+2wPZsE+zOGH8AQ23Y+/eGDDzU2OLXAQCKqggQCykHAngg1o2AUjIJRMFIBAG0FYiYGZofPAAAAAElFTkSuQmCC","orcid":"","institution":"Qinghai Minzu University","correspondingAuthor":true,"prefix":"","firstName":"Bin","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-02-20 15:09:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8927196/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8927196/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105035378,"identity":"7575e8f9-6663-43c1-bfe8-da66e3d351c7","added_by":"auto","created_at":"2026-03-20 07:25:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":617714,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8927196/v1_covered_55dbafa0-381f-468a-97a0-e80b5c7397a4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"ST Stock Price Forecasting with the PSO-KAN-Transformer Hybrid Model","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":"KAN, Transformer, PSO-KAN-Transformer, Stock price prediction","lastPublishedDoi":"10.21203/rs.3.rs-8927196/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8927196/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The rapid advancement of big data and Internet of Things (IoT) technologies has expanded the application scope of high-dimensional time series data. 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