Dynamic Pricing with Elastic-ARIMA Demand

Dynamic Pricing with Elastic-ARIMA Demand | 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PDF Research Article Dynamic Pricing with Elastic-ARIMA Demand J.Christopher Westland This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6641288/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 Dynamic pricing is a fundamental problem in operations research, requiring accurate demand estimation for revenue optimization. This study introduces Elastic-ARIMA Demand, a novel stochastic demand model that integrates price elasticity with autoregressive integrated moving average (ARIMA) processes to more accurately reflect real-world demand behavior. We investigate the sufficiency of linear demand models in dynamic pricing scenarios, comparing their performance against Elastic-ARIMA demand across Our findings demonstrate that, despite their misspecification, linear models approximate optimal pricing decisions with bounded regret and near-optimal revenue outcomes. The research further quantifies the ‘price of misspecification’ in dynamic pricing contexts, evaluating how quickly regret converges under linear assumptions and whether such models are practically viable. We show that while Elastic-ARIMA models offer a more accurate structural representation of demand, their performance gains over linear models are marginal in finite time horizons, reinforcing the robustness of simplified linear pricing strategies. The study concludes with implications for revenue management and open questions for future research on competitive pricing dynamics and real-world demand forecasting. Dynamic Pricing Elastic-ARIMA Demand Linear Models Regret Revenue Optimization 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. 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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-6641288","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":479562813,"identity":"16c64221-94d8-4b07-bc45-2542ea8b7aa5","order_by":0,"name":"J.Christopher Westland","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYLCCBwwMjP0MjA1AJjNRGhgbEoDEzAaStWw4AOYQocXgePvzBwk1d2Q330hu/MBQYZ3YQFDLmTOGDQnHnhlvu5HYLMFwJp2wFrMbOUCHsR1OBGppY2BsO0yElvvPHzYk/DucuHkGSMs/YrTcYDBsSAQavkECpKWBCC32Z3IMZyT2HTaeceZhs0TCsXRjglok248/+PDh22HZ/vb0hx8+1FjLEtSCChJIUz4KRsEoGAWjABcAALCbSde6xVI6AAAAAElFTkSuQmCC","orcid":"","institution":"University of Illinois at Chicago","correspondingAuthor":true,"prefix":"","firstName":"J.Christopher","middleName":"","lastName":"Westland","suffix":""}],"badges":[],"createdAt":"2025-05-11 19:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6641288/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6641288/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94678866,"identity":"c4c21b03-b5ca-44ec-9845-e338498d33b0","added_by":"auto","created_at":"2025-10-29 14:23:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":286742,"visible":true,"origin":"","legend":"","description":"","filename":"ElasticARIMALinearModelsBlind.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6641288/v1_covered_05dc0c90-61da-4a08-b5cd-ac762441ed3c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dynamic Pricing with Elastic-ARIMA Demand","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":"Dynamic Pricing, Elastic-ARIMA Demand, Linear Models, Regret, Revenue Optimization","lastPublishedDoi":"10.21203/rs.3.rs-6641288/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6641288/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDynamic pricing is a fundamental problem in operations research, requiring accurate demand estimation for revenue optimization. This study introduces Elastic-ARIMA Demand, a novel stochastic demand model that integrates price elasticity with autoregressive integrated moving average (ARIMA) processes to more accurately reflect real-world demand behavior. We investigate the sufficiency of linear demand models in dynamic pricing scenarios, comparing their performance against Elastic-ARIMA demand across Our findings demonstrate that, despite their misspecification, linear models approximate optimal pricing decisions with bounded regret and near-optimal revenue outcomes. The research further quantifies the \u0026lsquo;price of misspecification\u0026rsquo; in dynamic pricing contexts, evaluating how quickly regret converges under linear assumptions and whether such models are practically viable. 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