A Time Series Decomposition-Driven Combined Model for Agricultural Price Forecasting

A Time Series Decomposition-Driven Combined Model for Agricultural Price Forecasting | 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 A Time Series Decomposition-Driven Combined Model for Agricultural Price Forecasting Guo Yan, Wang Haoran, Su Chang, Hou Songsong, Jia Mengjing, Tang Qichao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7492291/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Accurate forecasting of agricultural product prices is vital for stabilizing market dynamics and informing effective policy decisions. However, the strong nonlinearity and complexity of influencing factors often limit the performance of traditional models. To address these challenges, this study proposes a hybrid forecasting framework—GRU-IHEOA-N-BEATS-BP—targeted at apple and crucian carp price prediction. The model first utilizes STL decomposition to separate the time series into seasonal, trend, and residual components. Pearson correlation analysis is applied to identify key influencing factors for each component. A GRU network is then constructed to model sequential dependencies, with its parameters optimized by an improved Human Evolutionary Optimization Algorithm (IHEOA) to enhance prediction accuracy. Concurrently, an N-BEATS model captures high-order nonlinear patterns from the decomposed components. Finally, a BP neural network aggregates outputs from all forecasting submodels to generate the final prediction. Experimental results validate the effectiveness of the proposed model. For crucian carp, the value R 2 improves from 64. 67% (with N-BEATS standalone) to 97. 95\%, while MAE and MSE decrease by 81.5% and 93.6%, respectively. These results demonstrate the model’s superior ability to capture complex temporal and nonlinear characteristics. In summary, this hybrid approach significantly enhances the accuracy and robustness of forecasting. It provides a valuable tool for intelligent agricultural management, contributing to food security, higher farmer income, and more precise market regulation. Agricultural Product Price Forecasting Metaheuristic Algorithm Secondary Decomposition Combined Model Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 18 Oct, 2025 Reviews received at journal 13 Oct, 2025 Reviewers agreed at journal 13 Oct, 2025 Reviewers invited by journal 13 Oct, 2025 Editor assigned by journal 30 Aug, 2025 Submission checks completed at journal 30 Aug, 2025 First submitted to journal 29 Aug, 2025 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-7492291","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":531528129,"identity":"9929fd9d-832b-45d5-ac54-28c1594964ea","order_by":0,"name":"Guo Yan","email":"","orcid":"","institution":"Sichuan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Guo","middleName":"","lastName":"Yan","suffix":""},{"id":531528130,"identity":"09b3a6d6-6d90-4d0f-8c7f-ef08fc59d375","order_by":1,"name":"Wang Haoran","email":"","orcid":"","institution":"Sichuan Agricultural 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However, the strong nonlinearity and complexity of influencing factors often limit the performance of traditional models. To address these challenges, this study proposes a hybrid forecasting framework—GRU-IHEOA-N-BEATS-BP—targeted at apple and crucian carp price prediction. The model first utilizes STL decomposition to separate the time series into seasonal, trend, and residual components. Pearson correlation analysis is applied to identify key influencing factors for each component. A GRU network is then constructed to model sequential dependencies, with its parameters optimized by an improved Human Evolutionary Optimization Algorithm (IHEOA) to enhance prediction accuracy. Concurrently, an N-BEATS model captures high-order nonlinear patterns from the decomposed components. Finally, a BP neural network aggregates outputs from all forecasting submodels to generate the final prediction. Experimental results validate the effectiveness of the proposed model. For crucian carp, the value \u003cem\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e improves from 64. 67% (with N-BEATS standalone) to 97. 95\\%, while MAE and MSE decrease by 81.5% and 93.6%, respectively. These results demonstrate the model’s superior ability to capture complex temporal and nonlinear characteristics. In summary, this hybrid approach significantly enhances the accuracy and robustness of forecasting. It provides a valuable tool for intelligent agricultural management, contributing to food security, higher farmer income, and more precise market regulation.\u003c/p\u003e","manuscriptTitle":"A Time Series Decomposition-Driven Combined Model for Agricultural Price Forecasting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 19:43:55","doi":"10.21203/rs.3.rs-7492291/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-18T07:22:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-13T06:43:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196084728366422522946832729961888674716","date":"2025-10-13T06:17:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-13T05:31:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-30T09:01:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-30T09:01:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Evolutionary Intelligence","date":"2025-08-30T02:44:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"evolutionary-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"evin","sideBox":"Learn more about [Evolutionary Intelligence](http://link.springer.com/journal/12065)","snPcode":"12065","submissionUrl":"https://submission.nature.com/new-submission/12065/3","title":"Evolutionary Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"199d0144-6c6b-4ce9-b45c-488eeb9dc976","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-03T04:53:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-27 19:43:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7492291","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7492291","identity":"rs-7492291","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}