Comparative Study of Arima, Lstm and Prophet Models for Time Series Forecasting: A Comprehensive Review

Comparative Study of Arima, Lstm and Prophet Models for Time Series Forecasting: A Comprehensive Review | 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 Systematic Review Comparative Study of Arima, Lstm and Prophet Models for Time Series Forecasting: A Comprehensive Review Hiteash Mahajan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8615640/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 Time series forecasting represents one of the most critical challenges in contemporary data science and machine learning, with applications spanning finance, energy systems, weather prediction, traffic management, supply chain optimization, and healthcare. This comprehensive review examines and compares three prominent forecasting methodologies: Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM) neural networks, and Prophet. These models embody distinct paradigms—traditional statistical methods, deep learning architectures, and automated trend-based analysis respectively. Through systematic synthesis of recent literature and empirical studies from 2018–2025, this review analyzes theoretical foundations, practical implementations, strengths, limitations, and optimal application contexts. Our findings reveal that ARIMA exhibits superior performance for simple linear patterns (MAPE 3.2–13.6%), LSTM demonstrates exceptional capability in capturing complex non-linear dependencies with 84–87% error reduction vs. ARIMA, while Prophet excels in handling business time series with strong seasonality (MAPE 2.2–24.2%). Model selection depends critically on data characteristics, forecasting horizon, computational resources, and application requirements. This review synthesizes over two decades of empirical findings to provide principled guidance for practitioners in model selection and implementation. Time series forecasting ARIMA LSTM Prophet Comparative analysis Full Text Additional Declarations The authors declare no competing interests. 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. 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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-8615640","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":575384094,"identity":"9a70353e-13eb-4146-98eb-fe5a57b25de2","order_by":0,"name":"Hiteash Mahajan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYBACA2YwdQDCS6gAEszMDQS0MMO0ABkPzoBoRgJaGJC0MD5sA3EIaDFn5z/44MefO3Ybbvcf/JA4rzaavx2o5UfFNpxaLJuZmQ17254lb7hzmFkicdvx3BmHGRsYe87cxu2ww8xsErwNh5MNbiQzALUcy20AamFmbMOrhf3nnz9gLcw/Euccy51PhBY2Zh62w3ZALWwSiQ01uRuI0GIsLdt2OEHyzmEzi4RjB3I3ArUcxOuX8wcffnzz57A93+3Gxzd/1NTlzjt/GBiGFbi1wEBigwSYPgwmDxBUDwT2DBAtdcQoHgWjYBSMghEGAAzSY5DrMZQbAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0002-3682-4610","institution":"Department of Computer Science Engineering, UIET Kathua","correspondingAuthor":true,"prefix":"","firstName":"Hiteash","middleName":"","lastName":"Mahajan","suffix":""}],"badges":[],"createdAt":"2026-01-16 06:32:31","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8615640/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8615640/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101223982,"identity":"4a8a6647-7251-41f2-8a2b-0112bb387fb2","added_by":"auto","created_at":"2026-01-27 12:12:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1006073,"visible":true,"origin":"","legend":"","description":"","filename":"reviewpaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8615640/v1_covered_3bb9bca5-7e18-456b-8a65-2ef69a950f48.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eComparative Study of Arima, Lstm and Prophet Models for Time Series Forecasting: A Comprehensive Review\u003c/strong\u003e\u003c/p\u003e","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":"[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":"Time series forecasting, ARIMA, LSTM, Prophet, Comparative analysis","lastPublishedDoi":"10.21203/rs.3.rs-8615640/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8615640/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTime series forecasting represents one of the most critical challenges in contemporary data science and machine learning, with applications spanning finance, energy systems, weather prediction, traffic management, supply chain optimization, and healthcare. 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