Forecasting İstanbul Airport’s Passenger Volume: A Comparative Analysis of Time Series and Machine Learning Models

Hasan Durmuş

doi:10.30518/jav.1992268

Forecasting İstanbul Airport’s Passenger Volume: A Comparative Analysis of Time Series and Machine Learning Models

Abstract

The volume of air transportation has increased dramatically thanks to globalization. In this perspective, accurate passenger demand forecasting gained importance for proactive capacity planning, resource allocation, and operational efficiency in the global aviation industry. This study aims to forecast passenger numbers in İstanbul Airport (IST), which is the biggest airport of Türkiye and one of the busiest airports in Europe. To achieve this, two methods, namely SARIMA, which is one of the traditional time series methods and the Prophet, which is a machine learning method, were utilized in a comparative analysis, and forecasts for passenger numbers were made with both methods. The study dataset consists of 89 months of passenger data of İstanbul Airport (from January 2019 to May 2026) and data used by dividing as a training and a test set. Methods’ performances were evaluated using standard error metrics, MAPE and RMSE. The empirical findings reveal that SARIMA outperformed the Prophet model in terms of errors. Although the Prophet performed similarly to SARIMA in regular periods, in irregular periods SARIMA performed much better. For the future, both methods are expecting the continuation of the growth trend of İstanbul Airport’s passenger volume.

Keywords

Ethical Statement

This article does not contain any studies with human participants or animals performed by any of the authors. Therefore, ethical approval was not required for this study.

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Details

Primary Language

English

Subjects

Economic Models and Forecasting, Air Transportation and Freight Services, Passenger Needs

Journal Section

Research Article

Authors

Hasan Durmuş ^*
0000-0001-8240-4671
Türkiye

Early Pub Date

October 23, 2026

Publication Date

October 25, 2026

Submission Date

July 11, 2026

Acceptance Date

August 26, 2026

Published in Issue

Year 2026 Volume: 10 Number: 3

DOI

https://doi.org/10.30518/jav.1992268

IZ

https://izlik.org/JA75BR62XN

Cite

RIS / Bibtex

APA

Durmuş, H. (2026). Forecasting İstanbul Airport’s Passenger Volume: A Comparative Analysis of Time Series and Machine Learning Models. Journal of Aviation, 10(3), 562-569. https://doi.org/10.30518/jav.1992268

AMA

1.Durmuş H. Forecasting İstanbul Airport’s Passenger Volume: A Comparative Analysis of Time Series and Machine Learning Models. JAV. 2026;10(3):562-569. doi:10.30518/jav.1992268

Chicago

Durmuş, Hasan. 2026. “Forecasting İstanbul Airport’s Passenger Volume: A Comparative Analysis of Time Series and Machine Learning Models”. Journal of Aviation 10 (3): 562-69. https://doi.org/10.30518/jav.1992268.

EndNote

Durmuş H (October 1, 2026) Forecasting İstanbul Airport’s Passenger Volume: A Comparative Analysis of Time Series and Machine Learning Models. Journal of Aviation 10 3 562–569.

IEEE

[1]H. Durmuş, “Forecasting İstanbul Airport’s Passenger Volume: A Comparative Analysis of Time Series and Machine Learning Models”, JAV, vol. 10, no. 3, pp. 562–569, Oct. 2026, doi: 10.30518/jav.1992268.

ISNAD

Durmuş, Hasan. “Forecasting İstanbul Airport’s Passenger Volume: A Comparative Analysis of Time Series and Machine Learning Models”. Journal of Aviation 10/3 (October 1, 2026): 562-569. https://doi.org/10.30518/jav.1992268.

JAMA

1.Durmuş H. Forecasting İstanbul Airport’s Passenger Volume: A Comparative Analysis of Time Series and Machine Learning Models. JAV. 2026;10:562–569.

MLA

Durmuş, Hasan. “Forecasting İstanbul Airport’s Passenger Volume: A Comparative Analysis of Time Series and Machine Learning Models”. Journal of Aviation, vol. 10, no. 3, Oct. 2026, pp. 562-9, doi:10.30518/jav.1992268.

Vancouver

1.Hasan Durmuş. Forecasting İstanbul Airport’s Passenger Volume: A Comparative Analysis of Time Series and Machine Learning Models. JAV. 2026 Oct. 1;10(3):562-9. doi:10.30518/jav.1992268