An Optimized ARIMA Model for Emergency Medical Services Time Series Demand Forecasting Using Bayesian Methods

An Optimized ARIMA Model for Emergency Medical Services Time Series Demand Forecasting Using Bayesian Methods | 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 An Optimized ARIMA Model for Emergency Medical Services Time Series Demand Forecasting Using Bayesian Methods Hanaa Ghareib Hendi, Mohamed Hasan Ibrahim, Mohamed Hassan Farrag This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4785386/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Predicting future demand for emergency services through time series forecasting is a useful tool for emergency medical services (EMS). Accurate forecasting of emergency needs is critical to EMS success and efficiency. Spatial management can be improved by better transportation before incidents, leading to significant improvements in response time, prehospital care, better outcomes, and survival quantitative Autoregressive Integrated Moving Average (ARIMA) models are popularly used for time series forecasting. A systematic approach used a grid search to find the parameter space (p, d, q). Bayesian optimization was used to improve our model by identifying the best over-parameters of the ARIMA model, resulting in improved prediction performance f guarantees Our results suggest automatic and heuristic approaches to state together can be effective for optimizing EMS time-series forecasting, to provide valuable information to optimize EMS availability and resource management Emergency Medical Services ARIMA Model Bayesian Optimization Time series Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 26 Jul, 2024 Submission checks completed at journal 26 Jul, 2024 First submitted to journal 22 Jul, 2024 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-4785386","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":332068911,"identity":"d16896a3-b462-4ef4-86dd-d46e22ec31b1","order_by":0,"name":"Hanaa Ghareib Hendi","email":"data:image/png;base64,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","orcid":"","institution":"Fayoum University","correspondingAuthor":true,"prefix":"","firstName":"Hanaa","middleName":"Ghareib","lastName":"Hendi","suffix":""},{"id":332068912,"identity":"c833bfc9-034f-432f-afb5-8df50bfd0f12","order_by":1,"name":"Mohamed Hasan Ibrahim","email":"","orcid":"","institution":"Fayoum University","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"Hasan","lastName":"Ibrahim","suffix":""},{"id":332068913,"identity":"5649d004-3de1-4a30-9946-a03be6215564","order_by":2,"name":"Mohamed Hassan Farrag","email":"","orcid":"","institution":"Fayoum University","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"Hassan","lastName":"Farrag","suffix":""}],"badges":[],"createdAt":"2024-07-23 03:44:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4785386/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4785386/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63010100,"identity":"48c514f9-2a3b-4cc7-9a2b-e621021f0dbb","added_by":"auto","created_at":"2024-08-22 05:31:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":532106,"visible":true,"origin":"","legend":"","description":"","filename":"AnOptimizedARIMAModel.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4785386/v1_covered_96504f1e-06e5-4060-a868-ed593cce418b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Optimized ARIMA Model for Emergency Medical Services Time Series Demand Forecasting Using Bayesian Methods","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":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-intelligent-information-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jiis","sideBox":"Learn more about [Journal of Intelligent Information Systems](http://link.springer.com/journal/10844)","snPcode":"10844","submissionUrl":"https://submission.nature.com/new-submission/10844/3","title":"Journal of Intelligent Information Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Emergency Medical Services, ARIMA Model, Bayesian Optimization, Time series","lastPublishedDoi":"10.21203/rs.3.rs-4785386/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4785386/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePredicting future demand for emergency services through time series forecasting is a useful tool for emergency medical services (EMS). Accurate forecasting of emergency needs is critical to EMS success and efficiency. Spatial management can be improved by better transportation before incidents, leading to significant improvements in response time, prehospital care, better outcomes, and survival quantitative Autoregressive Integrated Moving Average (ARIMA) models are popularly used for time series forecasting. A systematic approach used a grid search to find the parameter space (p, d, q). 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