A Reproducible and Regime-Aware SARIMA Modelling Framework for National Air Traffic Forecasting: Evidence from Türkiye (2018–2025)

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dc.authorid0000-0003-2335-2984
dc.contributor.authorKAŞ, Recep
dc.contributor.authorARIK HATİPOĞLU, Seda
dc.contributor.authorKONAR, Mehmet
dc.contributor.authorŞEN, Mehmet
dc.date.accessioned2026-07-22T10:47:47Z
dc.date.available2026-07-22T10:47:47Z
dc.date.issued08/04/2026
dc.departmentKapadokya Üniversitesi, Kapadokya Meslek Yüksekokulu, Uçak Teknolojisi Bölümü
dc.description.abstractReliable short-term air traffic forecasts are important for operational planning in national airspace systems. This study develops a transparent forecasting framework for Türkiye’s monthly aircraft movements using publicly available data from the General Directorate of State Airports Authority (DHMİ) for 2018–2025. Because DHMİ releases may follow cumulative within-year reporting, month-specific increments are reconstructed through within-year differencing and checked through simple audit procedures. The empirical analysis compares seasonal naïve, ETS, and a constrained SARIMA family under leakage-free evaluation, combining a strict 2025 holdout with expanding-window rolling-origin validation. Forecast performance is assessed using standard accuracy metrics and complemented by Diebold–Mariano comparisons, which are interpreted cautiously, given the short holdout length. To examine instability around the pandemic period, this study also reports structural-break and stability diagnostics as supportive evidence rather than definitive identification. Uncertainty is evaluated through backtested 80% and 95% prediction intervals, comparing nominal SARIMA intervals, parametric bootstrap, split conformal prediction, and adaptive conformal inference (ACI). The results show that SARIMA provides the strongest point-forecast performance among the benchmarked models, while adaptive conformal calibration offers a useful balance between empirical coverage and interval width under changing conditions. Overall, this study provides a reproducible and operationally interpretable baseline for national air traffic forecasting in Türkiye and a clear benchmark for future multivariate extensions.
dc.identifier.citationKaş, R., Şen, M., Hatipoğlu, S. A., & Konar, M. (2026). A Reproducible and Regime-Aware SARIMA Modelling Framework for National Air Traffic Forecasting: Evidence from Türkiye (2018–2025). Modelling, 7(2), 77.
dc.identifier.doihttps://doi.org/10.3390/modelling7020077
dc.identifier.endpage77 (MDPI makale numarası kullandığı için sayfa aralığı yerine makale numarasıdır)
dc.identifier.issue2
dc.identifier.startpage77
dc.identifier.urihttps://hdl.handle.net/20.500.12695/4253
dc.identifier.urihttps://www.mdpi.com/3849352
dc.identifier.volume7
dc.institutionauthorKAŞ, Recep
dc.institutionauthorid0000-0003-2335-2984
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofModelling
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectair traffic forecasting
dc.subjectSARIMA
dc.subjectrolling-origin
dc.subjectcross-validation
dc.subjectDiebold–Mariano test
dc.subjectstructural break
dc.subjectprediction intervals
dc.subjectbootstrap
dc.subjectconformal prediction
dc.subjectadaptive conformal inference
dc.titleA Reproducible and Regime-Aware SARIMA Modelling Framework for National Air Traffic Forecasting: Evidence from Türkiye (2018–2025)
dc.typeArticle

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