Long sequence Time-Series forecasting of rare earth price based on Variational Mode Decomposition and improved Random Forest

Long sequence Time-Series forecasting of rare earth price based on Variational Mode Decomposition and improved Random Forest | 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 Long sequence Time-Series forecasting of rare earth price based on Variational Mode Decomposition and improved Random Forest Rongxiu Lu, Kaiyuan Yao, Hui Yang, Wenhao Dai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5286488/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Jun, 2025 Read the published version in International Journal of Machine Learning and Cybernetics → Version 1 posted 13 You are reading this latest preprint version Abstract Given the complex and prolonged industrial processes involved in rare earth production, including the extraction and separation stages, the utility of short-term price predictions is limited due to the extensive times required to adjust production schedules. Consequently, accurately forecasting the long-term price trends of rare earth products is a pressing challenge. To address this, this paper introduces a VMD-SRF hybrid model tailored for Long Sequence Time-Series Forecasting (LSTF). To simplify the complexity of the initial data and improve the model's predictive accuracy, Variational Mode Decomposition (VMD) is first employed to analyze the periodicity and random components in price time series; Then, it combines the Series Random Forest model, which is improved based on the Random Forest (RF) algorithm. Series Random Forest (SRF) model uses Dynamic Time Warping (DTW) distance as heuristic information to address the deficiencies of Random Forest in long time series forecasting. This hybrid approach, leveraging the strengths of both VMD and SRF, enhances the handling of LSTF issues. An experimental comparative analysis using four representative datasets of rare earth product prices indicates superior prediction accuracy of the proposed method. These advancements present a promising and applicable strategy for addressing LSTF challenges in various practical settings. Dynamic Time Warping Random Forest Series Random Forest Long Sequence Time-Series Forecasting Variational Mode Decomposition Rare Earth Product Price Forecast Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Jun, 2025 Read the published version in International Journal of Machine Learning and Cybernetics → Version 1 posted Editorial decision: Revision requested 19 Feb, 2025 Reviews received at journal 17 Feb, 2025 Reviewers agreed at journal 27 Jan, 2025 Reviewers agreed at journal 27 Jan, 2025 Reviews received at journal 06 Jan, 2025 Reviewers agreed at journal 22 Dec, 2024 Reviews received at journal 08 Dec, 2024 Reviewers agreed at journal 19 Nov, 2024 Reviewers agreed at journal 19 Nov, 2024 Reviewers invited by journal 01 Nov, 2024 Editor assigned by journal 23 Oct, 2024 Submission checks completed at journal 19 Oct, 2024 First submitted to journal 18 Oct, 2024 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. 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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-5286488","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":369753764,"identity":"b3b383b7-46df-4a37-8966-80496d097397","order_by":0,"name":"Rongxiu Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYFAC5gaGD0ASzOYhTgtjA+MMoBYekrSAlROvxXxGYpu0TYUdu71EAuODt20M8uaEtMicOdgmnXMmmZlHIoHZcG4bg+HOBgJaJNgb26Rz2w6AtLBJ87YxJBgcIKSFmbFN2vIfWAv7b+K0gGxhbIDYwkycFp6DzZY9x4B+OfOwWXLOOQnDDQS1SCQfvPGjxi6ZvT354Ic3ZTbyBG2BgWRQBIGMIFI9ENgRr3QUjIJRMApGHAAAXCQ05/AMhKYAAAAASUVORK5CYII=","orcid":"","institution":"East China Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Rongxiu","middleName":"","lastName":"Lu","suffix":""},{"id":369753765,"identity":"14f12560-24f2-4299-a8b3-b5877172ad7e","order_by":1,"name":"Kaiyuan Yao","email":"","orcid":"","institution":"East China Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Kaiyuan","middleName":"","lastName":"Yao","suffix":""},{"id":369753766,"identity":"58d2a1d2-dcec-48d6-a0ab-ad699684fff5","order_by":2,"name":"Hui Yang","email":"","orcid":"","institution":"East China Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Yang","suffix":""},{"id":369753767,"identity":"b3d3329a-ea76-4bf7-92d1-86b4b5ff8359","order_by":3,"name":"Wenhao Dai","email":"","orcid":"","institution":"East China Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Wenhao","middleName":"","lastName":"Dai","suffix":""}],"badges":[],"createdAt":"2024-10-18 05:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5286488/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5286488/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s13042-025-02704-y","type":"published","date":"2025-06-24T16:05:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85686500,"identity":"0f99a07d-ea4e-4df2-a98b-c7e0d7d02f3b","added_by":"auto","created_at":"2025-06-30 16:08:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":941745,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscriptfile.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5286488/v1_covered_313c36f0-3128-4dd0-baee-4b5b593440b9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Long sequence Time-Series forecasting of rare earth price based on Variational Mode Decomposition and improved Random Forest","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-machine-learning-and-cybernetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmlc","sideBox":"Learn more about [International Journal of Machine Learning and Cybernetics](http://actavetscand.biomedcentral.com/)","snPcode":"13042","submissionUrl":"https://submission.nature.com/new-submission/13042/3","title":"International Journal of Machine Learning and Cybernetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Dynamic Time Warping, Random Forest, Series Random Forest, Long Sequence Time-Series Forecasting, Variational Mode Decomposition, Rare Earth Product Price Forecast","lastPublishedDoi":"10.21203/rs.3.rs-5286488/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5286488/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGiven the complex and prolonged industrial processes involved in rare earth production, including the extraction and separation stages, the utility of short-term price predictions is limited due to the extensive times required to adjust production schedules. 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