Time series to imaging based deep learning model for detecting abnormal fluctuation in agriculture product price

Time series to imaging based deep learning model for detecting abnormal fluctuation in agriculture product price | 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 Time series to imaging based deep learning model for detecting abnormal fluctuation in agriculture product price Jiang Wentao, Dabin Zhang, Liwen Ling, Guotao Cai, Liling Zeng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1652363/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Aug, 2023 Read the published version in Soft Computing → Version 1 posted 4 You are reading this latest preprint version Abstract In the analysis of agricultural product price time series, the detection of abnormal fluctuation is the primary task. Accurately judging the abnormal fluctuation of agricultural product prices will give the policy support to the government and also assist farmers increase production and income. A deep convolutional neural network model based on time series image(TSI) is introduced to identify the abnormal fluctuation of agricultural prices under the improved standard deviation-Slope judgment. Markov Transfer Field(MTF) method is used to transform the pre-processed sparse one-dimensional time series of agricultural prices into two-dimensional dense images, and a deep convolutional neural network(CNN) model is used for automatic feature extraction and classification of time series images containing abnormal fluctuations. The empirical evaluation of China's corn and wheat price datasets are performed in our paper, and compared with other abnormal fluctuation judgment methods, the accuracy of the proposed algorithm is about 20% higher on average, which confirms the applicability of the standard deviation-slope time series Image-Resnet-34 (SDS-TSI-Resnet34) model in practical scenarios. Finally, some feasible suggestions for the efficient development of agricultural economy are proposed based on the abnormal fluctuation judgment method proposed in this paper. standard deviation-Slope(SDS) time series image(TSI) convolutional neural network(CNN) abnormal fluctuation detection Full Text Cite Share Download PDF Status: Published Journal Publication published 24 Aug, 2023 Read the published version in Soft Computing → Version 1 posted Reviewers agreed at journal 21 Sep, 2022 Reviewers invited by journal 25 Aug, 2022 Editor assigned by journal 19 May, 2022 First submitted to journal 13 May, 2022 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-1652363","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":131568535,"identity":"bfb9c021-119e-4202-be28-9d65a89b5de1","order_by":0,"name":"Jiang Wentao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYBAC9mYILcfPzHz4AVFaGCFaDIwl29nSDIjT0gDRkrjhPI+CBHFa2pmfPfza9odx82EeBgOGGptoIhzGZm4s22bAbHaY98ADhmNpuQ2EtTCYSUtuM2AzO8yXYMDYcJgYLezfQFp4jJt5DCSI0iLYzGMm+XGbgYQBM7FapJl5yqQZ/xkbSBwGBnICMX7h4z++TfLHGbn6/v7Dhx98qLEhrAUEmHlgrARilIMA4w9iVY6CUTAKRsHIBAAJGzgtEhR/5wAAAABJRU5ErkJggg==","orcid":"","institution":"South China Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Jiang","middleName":"","lastName":"Wentao","suffix":""},{"id":131568536,"identity":"b565714b-d391-4ffa-85ba-fe463265c81c","order_by":1,"name":"Dabin Zhang","email":"","orcid":"","institution":"South China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Dabin","middleName":"","lastName":"Zhang","suffix":""},{"id":131568537,"identity":"12f244c4-0c89-45ed-b252-6b9b6120be3e","order_by":2,"name":"Liwen Ling","email":"","orcid":"","institution":"South China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Liwen","middleName":"","lastName":"Ling","suffix":""},{"id":131568538,"identity":"1b90d705-4cc6-4b48-a567-6d693dd1b825","order_by":3,"name":"Guotao Cai","email":"","orcid":"","institution":"South China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Guotao","middleName":"","lastName":"Cai","suffix":""},{"id":131568539,"identity":"1c985819-48fa-49b6-b130-fa6bd0e0c26c","order_by":4,"name":"Liling Zeng","email":"","orcid":"","institution":"South China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Liling","middleName":"","lastName":"Zeng","suffix":""}],"badges":[],"createdAt":"2022-05-13 08:52:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1652363/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1652363/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00500-023-09121-9","type":"published","date":"2023-08-24T15:01:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":25796000,"identity":"bbd64f35-889e-4d4f-9991-83bb5562922d","added_by":"auto","created_at":"2022-08-29 14:22:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":855738,"visible":true,"origin":"","legend":"","description":"","filename":"WentaoJiang1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1652363/v1_covered.pdf"}],"financialInterests":"","formattedTitle":"Time series to imaging based deep learning model for detecting abnormal fluctuation in agriculture product price","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1652363/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"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":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"standard deviation-Slope(SDS), time series image(TSI), convolutional neural network(CNN), abnormal fluctuation detection","lastPublishedDoi":"10.21203/rs.3.rs-1652363/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1652363/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In the analysis of agricultural product price time series, the detection of abnormal fluctuation is the primary task. Accurately judging the abnormal fluctuation of agricultural product prices will give the policy support to the government and also assist farmers increase production and income. A deep convolutional neural network model based on time series image(TSI) is introduced to identify the abnormal fluctuation of agricultural prices under the improved standard deviation-Slope judgment. Markov Transfer Field(MTF) method is used to transform the pre-processed sparse one-dimensional time series of agricultural prices into two-dimensional dense images, and a deep convolutional neural network(CNN) model is used for automatic feature extraction and classification of time series images containing abnormal fluctuations. The empirical evaluation of China's corn and wheat price datasets are performed in our paper, and compared with other abnormal fluctuation judgment methods, the accuracy of the proposed algorithm is about 20% higher on average, which confirms the applicability of the standard deviation-slope time series Image-Resnet-34 (SDS-TSI-Resnet34) model in practical scenarios. Finally, some feasible suggestions for the efficient development of agricultural economy are proposed based on the abnormal fluctuation judgment method proposed in this paper.","manuscriptTitle":"Time series to imaging based deep learning model for detecting abnormal fluctuation in agriculture product price","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-08-29 14:22:27","doi":"10.21203/rs.3.rs-1652363/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2022-09-21T09:03:44+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-08-25T08:15:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-05-19T14:43:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Soft Computing","date":"2022-05-13T04:50:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"79c56fad-e65f-4828-907a-a9ffc6be9cb5","owner":[],"postedDate":"August 29th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-09-07T15:03:46+00:00","versionOfRecord":{"articleIdentity":"rs-1652363","link":"https://doi.org/10.1007/s00500-023-09121-9","journal":{"identity":"soft-computing","isVorOnly":false,"title":"Soft Computing"},"publishedOn":"2023-08-24 15:01:13","publishedOnDateReadable":"August 24th, 2023"},"versionCreatedAt":"2022-08-29 14:22:27","video":"","vorDoi":"10.1007/s00500-023-09121-9","vorDoiUrl":"https://doi.org/10.1007/s00500-023-09121-9","workflowStages":[]},"version":"v1","identity":"rs-1652363","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1652363","identity":"rs-1652363","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}