Real-Time Multi-Step Time Series Forecasting Using a GA-Optimized VMD-RF Framework

Real-Time Multi-Step Time Series Forecasting Using a GA-Optimized VMD-RF Framework | 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 Real-Time Multi-Step Time Series Forecasting Using a GA-Optimized VMD-RF Framework Ying Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7493076/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 In vital industries including finance, energy, transportation, and meteorology, time series forecasting is essential. Prediction accuracy is frequently hampered by issues including nonlinearity, multi-scale patterns, and significant noise in real-world data. In order to tackle these problems, this study suggests a new hybrid forecasting framework called GA-VMD-RF, which integrates a real-time decomposition mechanism for deployment-ready multi-step forecasting and combines Genetic Algorithm-optimized Variational Mode Decomposition (VMD) with Random Forest (RF) regression. The proposed model introduces a permutation entropy-guided GA to dynamically tune VMD parameters, enhancing decomposition quality and preserving modal predictability. Each decomposed sub-series is modeled independently using RF, capturing distinct temporal dynamics. Unlike traditional full-series decomposition methods that risk information leakage, our real-time strategy performs VMD solely on the training data and incrementally updates decomposition during inference, ensuring fair and practical evaluations. Furthermore, an Average Window Reconstruction (AWR) mechanism is employed to fuse predictions from overlapping sliding windows, improving robustness and reducing temporal drift. Extensive experiments on diverse datasets—including U.S. Treasury yields, Brent crude oil prices, and wind speed observations—demonstrate that GA-VMD-RF outperforms classical models (SVM, LSTM) and VMD-based baselines in both short- and long-horizon tasks. Comparative results show improvements of up to 51.9% in MAPE over single-model baselines and 8.1% over traditional VMD-RF setups. Physical sciences/Engineering Physical sciences/Mathematics and computing Time Series Forecasting Variational Mode Decomposition (VMD) Genetic Algorithm (GA) Random Forest (RF) Multi-step Prediction Real-time Decomposition Sliding Window Reconstruction Anomaly Detection 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-7493076","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":524899018,"identity":"a0f9fa92-d8db-4b33-8ba7-911e1ce4d2c6","order_by":0,"name":"Ying Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIie3QMQrCMBSA4VcCcUns2iKoR2gJSAfFq1QEJw9R6Ko4FzxEQXBueYOL4gUcFEEcHNrNoYMPxcHB2NEh//AIIR8hATCZ/jH2nBktGA0IAERt0uAhQOjUIPAmtvDqEW8j8QrVoWMzUZ7l3WnbIrOKcqoh2JwEIC5+EsuVEqGj3HnE3GStI6LngYNWinLdIjJK98CZ/Ek8HKYoLrWJOkKIIyL8RXaRnrh0C30YjpOYK385obfM8lj7luZ+qwqocLCw8XS89fv0Y+O8KDWkmwFvVZ97VvT9PNWJgBXaEyaTyWR6ADMWS40+vE+hAAAAAElFTkSuQmCC","orcid":"","institution":"Zhengzhou Preschool Education College","correspondingAuthor":true,"prefix":"","firstName":"Ying","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2025-08-30 06:08:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7493076/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7493076/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92822714,"identity":"a34e8c0a-4159-4a5d-8e84-ddfc56c55f73","added_by":"auto","created_at":"2025-10-06 03:05:59","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3751,"visible":true,"origin":"","legend":"","description":"","filename":"ce8731c8fd9b46ae97388eb209cd5f80.json","url":"https://assets-eu.researchsquare.com/files/rs-7493076/v1/b65ef97e22b7720e7a9322a0.json"},{"id":94177368,"identity":"43c92e98-f7e2-47a1-bc90-c51caa1e6ec4","added_by":"auto","created_at":"2025-10-23 08:39:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":479716,"visible":true,"origin":"","legend":"","description":"","filename":"AGAOptimizedVMDRFSRnew.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7493076/v1_covered_92cf6462-81a8-4d4f-81e9-396b663c38d5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Real-Time Multi-Step Time Series Forecasting Using a GA-Optimized VMD-RF Framework","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":"Time Series Forecasting, Variational Mode Decomposition (VMD), Genetic Algorithm (GA), Random Forest (RF), Multi-step Prediction, Real-time Decomposition, Sliding Window Reconstruction, Anomaly Detection","lastPublishedDoi":"10.21203/rs.3.rs-7493076/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7493076/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In vital industries including finance, energy, transportation, and meteorology, time series forecasting is essential. 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