USFF: A Unified Sales Forecasting Framework with Fourier-Enhanced Decomposed Net

USFF: A Unified Sales Forecasting Framework with Fourier-Enhanced Decomposed Net | 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 USFF: A Unified Sales Forecasting Framework with Fourier-Enhanced Decomposed Net Qianyang Li, Xingjun Zhang, Shaoxun Wang, Jiawei Cao, Peng Tao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5877784/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 With the widespread adoption of big data science, the retail industry has seen an increasing demand for data-driven decision-making. As a critical task within the sector, product sales forecasting faces significant challenges. Traditional statistical methods are limited by their reliance on assumptions of data stationarity, while DL and ML techniques enhance accuracy but struggle to fully address complex features such as temporal dependencies, nonlinear trends, and external influences. No single method proves universally effective across all scenarios. To tackle these challenges, this paper introduces a Unified Sales Forecasting Framework (USFF) for vending machine sales prediction, which integrates statistical methods, machine learning, and deep learning techniques. By classifying data based on statistical features and employing multi-model fusion strategies, the USFF framework effectively captures the complex nature of sales data, significantly improving prediction accuracy by leveraging the strengths of various models. Furthermore, the paper presents a novel deep learning forecasting model, FEDNet (Fourier-Enhanced Decomposed Net), designed to address the dynamic dependencies and seasonal patterns inherent in long time series. FEDNet offers substantial advantages in capturing long-term dependencies and dynamic changes in time series data through the integration of reversible instance normalization, seasonal-trend decomposition, and Fourier decomposition.Experiments conducted on over 30 million real-world data points, along with five public datasets, demonstrate that the proposed framework and deep learning model outperform baseline methods across multiple evaluation metrics, confirming their effectiveness and reliability. The proposed framework and algorithm have been successfully implemented in a retail company in China, providing valuable business decision-making support for operational strategies. Deep learning Sales forecasting Multi-model fusion Fourier decomposition 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-5877784","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":407309367,"identity":"417384a4-7ee4-4f9c-89a8-36adf53c8f8f","order_by":0,"name":"Qianyang Li","email":"","orcid":"","institution":"Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Qianyang","middleName":"","lastName":"Li","suffix":""},{"id":407309368,"identity":"9063bbcc-f427-469b-ba42-228e4be4a5b9","order_by":1,"name":"Xingjun Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIie2RrQ7CMBSFLyHZzJbZLgvwCltmSPYym6oBNAKWq+rING+BRHZZ0pkR7CQYHAkWAWE/oEhKJaKfOD3ifuKkABrNX8KNNsdNad+huhICFMoKdEqC/bWC4ufl5XTfr+kuPwoCyyhB88ClioucBpuqnG+xMAhUNEFrEUsVB7ggNhPzDBplwIoEieX/GJIz98EENTrlqaA4zaVns1XsdAoqKC4KwxsxHjRbwmksaMismVzx6/LiXlk68ev8XN9W0SgzK7kChLdZ9CWG9zfJx2Cb6adoNBqN5psXHy5HqLs5Y2UAAAAASUVORK5CYII=","orcid":"","institution":"Xi'an Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Xingjun","middleName":"","lastName":"Zhang","suffix":""},{"id":407309369,"identity":"c57a14f8-1a97-42c1-9c32-2d44d05ea8a3","order_by":2,"name":"Shaoxun Wang","email":"","orcid":"","institution":"Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Shaoxun","middleName":"","lastName":"Wang","suffix":""},{"id":407309370,"identity":"0d23b34f-fe4d-41b6-8a13-7e98d4458d87","order_by":3,"name":"Jiawei Cao","email":"","orcid":"","institution":"Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Jiawei","middleName":"","lastName":"Cao","suffix":""},{"id":407309371,"identity":"16b8eac1-cef1-4e0c-b257-32d9a297d22b","order_by":4,"name":"Peng Tao","email":"","orcid":"","institution":"Shandong New Beiyang Information Technology Co., Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Tao","suffix":""}],"badges":[],"createdAt":"2025-01-22 05:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5877784/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5877784/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79872502,"identity":"42eda12f-b259-4598-8aec-9aa6977ceb19","added_by":"auto","created_at":"2025-04-03 23:01:26","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":721853,"visible":true,"origin":"","legend":"","description":"","filename":"USFF.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5877784/v1_covered_14336cd8-d5fe-4ae9-9bf0-d085a7c654f1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"USFF: A Unified Sales Forecasting Framework with Fourier-Enhanced Decomposed Net","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":"Deep learning, Sales forecasting, Multi-model fusion, Fourier decomposition","lastPublishedDoi":"10.21203/rs.3.rs-5877784/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5877784/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e With the widespread adoption of big data science, the retail industry has seen an increasing demand for data-driven decision-making. As a critical task within the sector, product sales forecasting faces significant challenges. Traditional statistical methods are limited by their reliance on assumptions of data stationarity, while DL and ML techniques enhance accuracy but struggle to fully address complex features such as temporal dependencies, nonlinear trends, and external influences. No single method proves universally effective across all scenarios. To tackle these challenges, this paper introduces a Unified Sales Forecasting Framework (USFF) for vending machine sales prediction, which integrates statistical methods, machine learning, and deep learning techniques. By classifying data based on statistical features and employing multi-model fusion strategies, the USFF framework effectively captures the complex nature of sales data, significantly improving prediction accuracy by leveraging the strengths of various models. 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