HIFS and Probabilistic Similarity Measure-based Intuitionistic Fuzzy Time Series Forecasting Method

HIFS and Probabilistic Similarity Measure-based Intuitionistic Fuzzy Time Series Forecasting Method | 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 HIFS and Probabilistic Similarity Measure-based Intuitionistic Fuzzy Time Series Forecasting Method Shivani Pant, Sanjay Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7937871/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Hesitant intuitionistic fuzzy sets (HIFSs) are more useful than hesitant fuzzy sets (HFSs) and intuitionistic fuzzy sets (IFSs) to model the uncertainty with non-determinism caused by the availability of multiple membership and non-membership grades. In this research paper, we propose the HIFS-based intuitionistic fuzzy time series (IFTS) forecasting method. The proposed method uses adaptive radius clustering for optimal partitioning and incorporates uncertainty with non-determinism in the process of fuzzification of time series data through HIFS. Intuitionistic fuzzy logical relations (IFLRs) used in proposed forecasting method are based on IFSs obtained by aggregating elements of HIFS. Probabilistic λ-cutting algorithm that groups IFSs according to their similarity measure is used in proposed forecasting method to establish IFLRs. Proposed IFTS forecasting method uses a simple computational rule to forecast the outputs and make the process of forecasting simple. Practicability and utility of the suggested forecasting method is shown by implementing it on time series data of the share price of State Bank of India (SBI) at the Bombay Stock Exchange (BSE), India, and the Taiwan Stock Exchange (TAIEX) data. Reduced error measures and valid statistical measure confirm superiority of the proposed method in forecasting of financial time series data. Intuitionistic fuzzy time series Hesitant intuitionistic fuzzy set Probabilistic similarity measure -cutting algorithm forecasting Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Minor Revision 06 Feb, 2026 Reviewers agreed at journal 22 Jan, 2026 Reviewers invited by journal 22 Jan, 2026 Editor invited by journal 08 Jan, 2026 First submitted to journal 24 Oct, 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. 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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-7937871","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":578648262,"identity":"f0132909-7a4e-423c-a28c-4393fdcf1f01","order_by":0,"name":"Shivani Pant","email":"","orcid":"","institution":"Govind Ballabh Pant University of Agriculture and Technology: Govind Ballabh Pant University of Agriculture \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Shivani","middleName":"","lastName":"Pant","suffix":""},{"id":578648263,"identity":"b99a5b55-ad44-4711-a7c7-d446f36ee247","order_by":1,"name":"Sanjay Kumar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYDACHgYGZgYGCwZ+CTBXQoZYLRIMkjMYGBuADB7itRjcAGthIKxFvufsM+mCCgl549vNxx/dqLHgYWA/fHQDPi0GZ9vNpGeckTDcdudYYnPOMaDDeNLSbuDVws/GJs3bJsG47UaOYXMOG1CLBI8ZXi3y/SAt/yTsN88AaflHhBaGs21ALQ0SiRskgFpy24jQYnDmGLM1zzGJ5Bk30hJn5/ZJ8LAR8ot8TxrjbZ4aG9v+GckHPud8q5PjZz98DL/DMAAbacpHwSgYBaNgFGADAIPNP3hvGKmtAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-3659-5387","institution":"Govind Ballabh Pant University of Agriculture and Technology","correspondingAuthor":true,"prefix":"","firstName":"Sanjay","middleName":"","lastName":"Kumar","suffix":""}],"badges":[],"createdAt":"2025-10-24 07:32:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7937871/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7937871/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101250204,"identity":"54d56124-c669-4eca-99f9-4b63f27c29cf","added_by":"auto","created_at":"2026-01-27 17:25:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":618345,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchPaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7937871/v1_covered_3d6cfe97-2c8a-4bfe-ae6c-ceb4ab1e0b98.pdf"}],"financialInterests":"","formattedTitle":"HIFS and Probabilistic Similarity Measure-based Intuitionistic Fuzzy Time Series Forecasting Method","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"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":"Intuitionistic fuzzy time series, Hesitant intuitionistic fuzzy set, Probabilistic similarity measure, -cutting algorithm, forecasting","lastPublishedDoi":"10.21203/rs.3.rs-7937871/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7937871/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHesitant intuitionistic fuzzy sets (HIFSs) are more useful than hesitant fuzzy sets (HFSs) and intuitionistic fuzzy sets (IFSs) to model the uncertainty with non-determinism caused by the availability of multiple membership and non-membership grades. 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