Forecasting Climate Trends with Stochastic Models: Comparative Analysis of TRAMO/SEATS-Based RegARIMA vs Seasonal ARIMA Models

Forecasting Climate Trends with Stochastic Models: Comparative Analysis of TRAMO/SEATS-Based RegARIMA vs Seasonal ARIMA 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 Forecasting Climate Trends with Stochastic Models: Comparative Analysis of TRAMO/SEATS-Based RegARIMA vs Seasonal ARIMA Models Münevver Gizem Gümüş, Hasan Çağatay Çiftçi, Kutalmış Gümüş This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6335915/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Time series analysis plays a crucial role in understanding climate variability and supporting reliable forecasting efforts. In this study, the forecasting performance of TRAMO/SEATS-based RegARIMA and SARIMA models is evaluated using long-term meteorological data (1960–2020) from Beyşehir and Seydişehir stations in Konya, Turkey. The TRAMO (Time Series Regression with ARIMA Noise, Missing Observations, and Outliers) approach corrects for outliers, missing data, and deterministic components, while SEATS (Signal Extraction in ARIMA Time Series) extract stochastic components such as trend and seasonality. The seasonality structure of the data is assessed using the Webel-Ollech test, and stationarity is examined via the Augmented Dickey-Fuller test. Following seasonal adjustment with TRAMO/SEATS, more robust forecasting models are constructed. Comparative analyses reveal that TRAMO/SEATS-based RegARIMA models outperform SARIMA models, yielding lower Mean Square Error (MSE: 0.25–5.22) and Root Mean Square Error (RMSE: 0.50–2.29), alongside higher Nash-Sutcliffe Efficiency (NSE: 0.69–1.00). These findings demonstrate the superiority of the TRAMO/SEATS approach in modeling complex seasonal structures and improving forecast accuracy. This study provides a scientific basis for understanding regional climate dynamics and supports the development of effective climate change adaptation and risk mitigation strategies. Forecasting Meteorological Time Series RegARIMA SARIMA TRAMO/SEATS Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 15 Jan, 2026 Reviews received at journal 22 Oct, 2025 Reviews received at journal 09 Oct, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviewers agreed at journal 01 Jul, 2025 Reviewers invited by journal 07 May, 2025 Editor assigned by journal 14 Apr, 2025 Submission checks completed at journal 01 Apr, 2025 First submitted to journal 29 Mar, 2025 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-6335915","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":454644097,"identity":"dc9911e9-5d2a-4b67-8f8a-1b4943f45851","order_by":0,"name":"Münevver Gizem Gümüş","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYDACZhBxQALC+cBwAEQZEK+FcQZcSwIhqw5AtfMQo0Xenf3hoxtnLOz5249f/GxTcSexgb15mwTjj3s4tRgeZkg2zrkhkTjjTE6xdM6ZZ4kNPMfKJBgSinFraWY4Jp3zQSKB4UBOgnRu2+HEBokcM6AW3C4zbGZs/w3UYi9//k3yb0uQFvk3+LXIMzOzMQMdxrjhRvoxaUawLTz4tRgAdQC9IJG48cYbNsueM4eN23jSii0S0vDY0n/84eecY3X2cufTH9/4UXFYtp/98MYbH2zw2HIAzuSBRDobiMCtAWhLA5zJ/gCPulEwCkbBKBjJAAAgVFgBRsMQlgAAAABJRU5ErkJggg==","orcid":"","institution":"Niğde Ömer Halisdemir University","correspondingAuthor":true,"prefix":"","firstName":"Münevver","middleName":"Gizem","lastName":"Gümüş","suffix":""},{"id":454644098,"identity":"3d1cafa9-09c3-4200-aaed-df91cedca9a0","order_by":1,"name":"Hasan Çağatay Çiftçi","email":"","orcid":"","institution":"Erciyes University","correspondingAuthor":false,"prefix":"","firstName":"Hasan","middleName":"Çağatay","lastName":"Çiftçi","suffix":""},{"id":454644099,"identity":"483de23a-3917-4906-88f2-ab8a1b9379b3","order_by":2,"name":"Kutalmış Gümüş","email":"","orcid":"","institution":"Niğde Ömer Halisdemir University","correspondingAuthor":false,"prefix":"","firstName":"Kutalmış","middleName":"","lastName":"Gümüş","suffix":""}],"badges":[],"createdAt":"2025-03-29 21:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6335915/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6335915/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82623321,"identity":"615679ff-ed9a-4130-8d36-10833651459f","added_by":"auto","created_at":"2025-05-13 12:41:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1444478,"visible":true,"origin":"","legend":"","description":"","filename":"MANUSCRIPTSERRA.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6335915/v1_covered_d3186e98-eb1a-4ace-a01f-87058a15d4fe.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Forecasting Climate Trends with Stochastic Models: Comparative Analysis of TRAMO/SEATS-Based RegARIMA vs Seasonal ARIMA Models","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":"environmental-modeling-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enmo","sideBox":"Learn more about [Environmental Modeling \u0026 Assessment](https://www.springer.com/journal/10666)","snPcode":"10666","submissionUrl":"https://submission.nature.com/new-submission/10666/3","title":"Environmental Modeling \u0026 Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Forecasting, Meteorological Time Series, RegARIMA, SARIMA, TRAMO/SEATS","lastPublishedDoi":"10.21203/rs.3.rs-6335915/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6335915/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTime series analysis plays a crucial role in understanding climate variability and supporting reliable forecasting efforts. In this study, the forecasting performance of TRAMO/SEATS-based RegARIMA and SARIMA models is evaluated using long-term meteorological data (1960\u0026ndash;2020) from Beyşehir and Seydişehir stations in Konya, Turkey. The TRAMO (Time Series Regression with ARIMA Noise, Missing Observations, and Outliers) approach corrects for outliers, missing data, and deterministic components, while SEATS (Signal Extraction in ARIMA Time Series) extract stochastic components such as trend and seasonality. The seasonality structure of the data is assessed using the Webel-Ollech test, and stationarity is examined via the Augmented Dickey-Fuller test. Following seasonal adjustment with TRAMO/SEATS, more robust forecasting models are constructed. 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