{"paper_id":"4af73bfd-29ca-4567-90aa-e86b09832386","body_text":"A Hybrid Data Mining Framework for Financial Time-Series Prediction | 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 Hybrid Data Mining Framework for Financial Time-Series Prediction Ujjwal Pradhan, Sourav Mahapatra, Manjusha Pandey, G V S Narayana This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8933577/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 Nowadays stock price forecasting is a challenging task because of inherent unpredictability, noise, and non-linear dynamic stock time-series data. This study proposes a hybrid data mining model that integrates wavelet decomposition, LSTM model, and Light gradient Boosting Machine (LightGBM) to increase the accuracy and stability. Wavelet transformation works to disintegrate the original price series into trend and residual components, that enables effective reduction of noises and separation of features. Stock price forecasting time-series data wavelet transform LSTM LightGBM hybrid model 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-8933577\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":595763966,\"identity\":\"00238b6e-7482-452f-a204-b53ce39b43fa\",\"order_by\":0,\"name\":\"Ujjwal Pradhan\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"\",\"institution\":\"GIET University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Ujjwal\",\"middleName\":\"\",\"lastName\":\"Pradhan\",\"suffix\":\"\"},{\"id\":595763967,\"identity\":\"6a0c453e-50a4-41fc-bde6-e3072dbe0a57\",\"order_by\":1,\"name\":\"Sourav Mahapatra\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"GIET University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Sourav\",\"middleName\":\"\",\"lastName\":\"Mahapatra\",\"suffix\":\"\"},{\"id\":595763968,\"identity\":\"598c0ea2-76d2-4dfa-8afa-27ce6f4e085f\",\"order_by\":2,\"name\":\"Manjusha Pandey\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"GIET University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Manjusha\",\"middleName\":\"\",\"lastName\":\"Pandey\",\"suffix\":\"\"},{\"id\":595763969,\"identity\":\"6fa19d1a-8e88-42ee-aa0f-07dcb7d0a392\",\"order_by\":3,\"name\":\"G V S Narayana\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"GIET University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"G\",\"middleName\":\"V S\",\"lastName\":\"Narayana\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-02-21 12:53:14\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-8933577/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-8933577/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":104739137,\"identity\":\"39928729-db2b-43a0-a221-6befea31b177\",\"added_by\":\"auto\",\"created_at\":\"2026-03-16 15:57:27\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":479998,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"AHybridDataMiningFrameworkforFinancialTime.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8933577/v1_covered_bcf243db-91df-49a7-bf9f-ed252e36936d.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"A Hybrid Data Mining Framework for Financial Time-Series Prediction\",\"fulltext\":[],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"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\":\"info@researchsquare.com\",\"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\":\"Stock price forecasting, time-series data, wavelet transform, LSTM, LightGBM, hybrid model\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8933577/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8933577/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eNowadays stock price forecasting is a challenging task because of inherent unpredictability, noise, and non-linear dynamic stock time-series data. This study proposes a hybrid data mining model that integrates wavelet decomposition, LSTM model, and Light gradient Boosting Machine (LightGBM) to increase the accuracy and stability. Wavelet transformation works to disintegrate the original price series into trend and residual components, that enables effective reduction of noises and separation of features.\\u003c/p\\u003e\",\"manuscriptTitle\":\"A Hybrid Data Mining Framework for Financial Time-Series Prediction\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-02-24 06:47:54\",\"doi\":\"10.21203/rs.3.rs-8933577/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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}}],\"origin\":\"\",\"ownerIdentity\":\"2b6b033b-866a-4e2a-acf8-74e73e2276e4\",\"owner\":[],\"postedDate\":\"February 24th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-03-16T15:55:19+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-02-24 06:47:54\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8933577\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8933577\",\"identity\":\"rs-8933577\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}