A granularity time series forecasting model combining three-way decision and trend information granularity

A granularity time series forecasting model combining three-way decision and trend information granularity | 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 granularity time series forecasting model combining three-way decision and trend information granularity Jianuan Qiu, Shuhua Su, Jingjing Qian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4136524/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 recent decades, significant advancements have been made in the field of time series data mining, leading to its widespread application in various domains. However, the existence of data correlation in time series data sets brings challenges to long-term prediction. One approach to address this issue is to transform the original time series into granular time series (GTS). Therefore, a prediction model based on GTS is proposed to meet this challenge in this study. Firstly, the improved L 1 -trend filtering is used to achieve the optimal segmentation of information particles. Then, a three-way decisions (TWD) model based on shape similarity is used to compress and aggregate information granules. Finally, a GTS prediction model based on LSTM neural network is established. The model effectively retains the trend information of the time series and overcomes the limitation that the existing models cannot adjust the granularity length of the original information. In addition, the proposed model is applied to several real datasets for sensitivity analysis and comparative analysis. The results show that the model has strong performance in long-term forecasting. Three-way decision Clustering Trend information granularity Time series forecasting L1-trend filtering Long short-term memory neural network 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-4136524","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":282196781,"identity":"fb8aa3b3-6d5b-4504-a28b-60b4e820861f","order_by":0,"name":"Jianuan Qiu","email":"","orcid":"","institution":"Shanghai University of Engineering Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jianuan","middleName":"","lastName":"Qiu","suffix":""},{"id":282196782,"identity":"0c348779-f3a8-41e4-bf2c-169a0576bb48","order_by":1,"name":"Shuhua Su","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYHACNgbGBgYGfmb2gw8SKmpI0CLZzpNs8ODMMRK0GJxnMJN82MJMWL38jPRnj3l3HJY3OMyQVpHYwMbA396dgFeLwY0cc2PeM4cNZx5mPHYjcYcMg8SZsxvwa5HIYZPmbTvM2Ae05UbiGTagSC5+LSCHgbTYNxxmMCtIbGMmrIXhRoIZSEviBKAWBqK0GJx5YyY5ty09eWYzT7JEwpljPAT9It+e/kzibZu1bT//8YMff1TUyPG39xJwGDrgIU35KBgFo2AUjAKsAAA6k0mBfJ15JwAAAABJRU5ErkJggg==","orcid":"","institution":"Shanghai University of Engineering Sciences","correspondingAuthor":true,"prefix":"","firstName":"Shuhua","middleName":"","lastName":"Su","suffix":""},{"id":282196783,"identity":"1ff97c1c-ac2b-45fa-8241-05ed34b491bf","order_by":2,"name":"Jingjing Qian","email":"","orcid":"","institution":"Shanghai University of Engineering Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jingjing","middleName":"","lastName":"Qian","suffix":""}],"badges":[],"createdAt":"2024-03-20 10:59:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4136524/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4136524/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64275111,"identity":"b1c6205c-5e77-49c9-9680-a836b4eec0f7","added_by":"auto","created_at":"2024-09-11 06:23:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1474777,"visible":true,"origin":"","legend":"","description":"","filename":"Agranularitytimeseriesforecastingmodelcombiningthreewaydecisionandtrendinformationgranularity.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4136524/v1_covered_cb1655c8-8819-4686-99f3-0511bbb9f81d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A granularity time series forecasting model combining three-way decision and trend information granularity","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":"Three-way decision, Clustering, Trend information granularity, Time series forecasting, L1-trend filtering, Long short-term memory neural network","lastPublishedDoi":"10.21203/rs.3.rs-4136524/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4136524/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn recent decades, significant advancements have been made in the field of time series data mining, leading to its widespread application in various domains. 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