Generalisation Bounds of Zero-Shot Economic Forecasting using Time Series Foundation Models

Generalisation Bounds of Zero-Shot Economic Forecasting using Time Series Foundation Models | 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 Generalisation Bounds of Zero-Shot Economic Forecasting using Time Series Foundation Models Jittarin Jetwiriyanon, Teo Susnjak, Surangika Ranathunga This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6750307/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 This study investigates zero-shot forecasting capabilities of Time Series Foundation Models (TSFMs) for macroeconomic indicators. We apply TSFMs to forecasting economic indicators under univariate conditions, bypassing the need for train bespoke econometric models using and extensive training datasets. Our experiments were conducted on a case study dataset, without additional customisation. We rigorously back-tested three state-of-the-art TSFMs (Chronos, TimeGPT and Moirai) under data-scarce conditions and structural breaks. Our results demonstrate that appropriately engineered TSFMs can internalise rich economic dynamics, accommodate regime shifts, and deliver well-behaved uncertainty estimates out of the box, while matching state-of-the-art multivariate models on this domain. Our findings suggest that, without any fine-tuning, TSFMs can match or exceed classical models during stable economic conditions. However, they are vulnerable to degradation in performances during periods of rapid shocks. The findings offer guidance to practitioners on when zero-shot deployments are viable for macroeconomic monitoring and strategic planning. GDP forecasting time series foundation models time series forecasting zero-shot forecasting Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementary.zip 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-6750307","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":467061371,"identity":"eb51699a-852e-4d1a-ba3f-0d49badbd0db","order_by":0,"name":"Jittarin Jetwiriyanon","email":"data:image/png;base64,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","orcid":"","institution":"Massey University","correspondingAuthor":true,"prefix":"","firstName":"Jittarin","middleName":"","lastName":"Jetwiriyanon","suffix":""},{"id":467061372,"identity":"d4f934ad-f705-4fbc-a1eb-aba1e78f8a80","order_by":1,"name":"Teo Susnjak","email":"","orcid":"","institution":"Massey University","correspondingAuthor":false,"prefix":"","firstName":"Teo","middleName":"","lastName":"Susnjak","suffix":""},{"id":467061373,"identity":"8a022524-d034-48ea-bb82-f61ee040d3f4","order_by":2,"name":"Surangika Ranathunga","email":"","orcid":"","institution":"Massey University","correspondingAuthor":false,"prefix":"","firstName":"Surangika","middleName":"","lastName":"Ranathunga","suffix":""}],"badges":[],"createdAt":"2025-05-26 11:23:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6750307/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6750307/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87449346,"identity":"e8be3aa8-828c-49b2-bc8b-ec0aed154157","added_by":"auto","created_at":"2025-07-24 02:17:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":922161,"visible":true,"origin":"","legend":"","description":"","filename":"GeneralisationBoundsofZeroShotEconomicForecastingusingTimeSeriesFoundationModels.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6750307/v1_covered_fd52327a-ac8f-4643-a76e-51fd52e9386c.pdf"},{"id":84180123,"identity":"991c0bbd-f490-4472-a141-bf3a5361cd53","added_by":"auto","created_at":"2025-06-09 03:34:14","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":838126,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.zip","url":"https://assets-eu.researchsquare.com/files/rs-6750307/v1/33145d0d0026eaada1acde59.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Generalisation Bounds of Zero-Shot Economic Forecasting using Time Series Foundation Models","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":"GDP forecasting, time series foundation models, time series forecasting, zero-shot forecasting","lastPublishedDoi":"10.21203/rs.3.rs-6750307/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6750307/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates zero-shot forecasting capabilities of Time Series Foundation Models (TSFMs) for macroeconomic indicators. 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