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Using the ARIMA and ARIMAX Methodologies to Estimate, Model, Forecast, and Compare Airline Passenger Transportation Demand in Türkiye

Yıl 2023, , 242 - 273, 18.01.2024
https://doi.org/10.26650/JTL.2023.1270944

Öz

Determining air transportation demand is a very important input that impacts countries’ economies in terms of both micro and macroeconomics. Thus, almost all countries continuously conduct research studies on estimating and forecasting airline passenger demand. However, new estimating and forecasting methods have been improved since the past. The purpose of this study is to estimate and forecast airline passenger demand based on the autoregressive integrated moving average (ARIMA) and ARIMA with explanatory variables (ARIMAX) methods in light of updated time series data and to determine the best model between the two econometric methods. The data used in the study belong to quarterly data between 2010-2022, in which the total incoming and outgoing airline passengers and total incoming and outgoing aircraft are obtained from Türkiye’s General Directorate of State Airports Authority (DHMI), GDP values are obtained from the Turkish Statistical Institute (TurkSTAT), and crude oil import prices are obtained from the US Energy Information Administration (EIA). The collected data have been analyzed using the software program EViews 10.00. The study presents estimations and predictions for total number of airline passengers by first using ARIMA modeling based on the Box-Jenkins method for univariate time series analysis and then ARIMAX modeling by adding exogenous variables to the first model. The study first tested for effects from COVID-19 and found no significant structural break in the studied period. Thus, the seasonal ARIMA (1,1,0) x (1,1,2) effect model (SARIMA) was understood to have the best fit. The second part of the study added the exogenous variables to the model and found ARIMAX (1,1) x (0,0) to have the best fit. Therefore, while the number of aircraft movements and GDP variables are found to be significant and to support the model, imported crude oil price was found to not be significant and to not support the model. The forecasting analysis results found the Theil index; bias proportion; RMSE, MAE, and MAPE; variance, and covariance explanation ratios; and R square values to be at satisfactory levels for both models. When comparing the two models, the ARIMAX model is seen to perform better than the ARIMA model.

JEL Classification : M31 , L93 , R41

Proje Numarası

-

Kaynakça

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  • Anvari, S., Tuna, S., Canci, M. and Turkay, M. (2016). “Automated Box-Jenkins forecasting tool with an application for passenger demand in urban rail systems”, Journal of Advanced Transportation, Vol. 50 No. 1, pp. 25-49. google scholar
  • Arya, P.; Paul, R. K.; Kumar, A.; Singh, K. N.; Sivaramne, N.; Chaudhary, P. (2015) Predicting pest population using weather variables: an ARIMAX time series Framework, International Journal of Agricultural and Statistics Sciences, 11(2),381-386. google scholar
  • Asteriou, D. ve Hall, S.G. (2011). ARIMA models and the Box-Jenkins methodology. Applied Econometrics, 2(2), 265-286. google scholar
  • BaFail (2004). BaFail, A. Applying Data Mining Techniques to Forecast Number of Airline Passengers in Saudi Arabia (domestic and international travels). Journal of Air Transport, Vol. 9, No. 1, pp. 100-115. google scholar
  • Balık, M. (2015). Hava Kargo Taşımacılığı Ve Türkiye’deki Gelişimini Etkileyen Faktörler, Afyon Kocatepe Üniversitesi Sosyal Bilimler Enstitüsü, Yüksek Lisan Tezi. google scholar
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ARIMA-ARIMAX Yöntemiyle Türkiye Havayolu Yolcu Talep Tahmin Modellemesi, Öngörüsü Ve Karşılaştırması

Yıl 2023, , 242 - 273, 18.01.2024
https://doi.org/10.26650/JTL.2023.1270944

Öz

Havayolu yolcu talebinin belirlenmesi makro ve mikro ekonomik açından ülkelerin ekonomisine etki eden önemli bir girdi unsurudur. Bundan dolayı hemen hemen tüm ülkeler havayolu yolcu talebi tahmin ve öngörüsüne yönelik sürekli araştırmalar yapmaktadır. Bununla birlikte geçmişten günümüze tahmin ve öngörü modellerinde bir çok yeni yöntemler geliştirilmiştir. Bu çalışmanın amacı, güncel verilerin ışığı altında Türkiye havayolu yolcu sayısını ARIMA ve ARIMAX yöntemlerine dayalı olarak model tahminlemesi, öngörüsünün yapılması ve kullanılan ekonometrik modellerden hangisinin daha iyi olduğunun belirlenmesidir. Çalışmada kullanılan veriler 2010-2022 dönemleri çeyrek dönemlere ait olup; Devlet Hava Meydanları İşletmesinden (DHMİ) gelen- giden toplam yolcu sayıları, ticari gelen-giden toplam uçak hareket sayısı, TUIK’ten Gayri Safi Milli Hasıla (GHSY) ve Birleşik Devletler Enerji Bilgi İşletmesinden Ham Petrol Alış Fiyatı ve kullanılarak sağlanmıştır. Toplanan veriler Eviews 10.00 yardımıyla analiz edilmişlerdir. Araştırmada tek değişkenli zaman serisi analizi Box-Jenkins metoduna dayalı ARIMA modellemesi ve daha sonra modele dışsal değişkenlerin ilave edilerek ARIMAX modeliyle toplam yolcu sayısı tahminlemesi ve öngörüleri ortaya çıkarılmıştır. Yapılan çalışmada, ilk aşamada Covid-19 etkisi incelenmiş ve bu dönemde yapısal kırılmanın anlamlı bir etkisi olmadığı belirlenmiş, akabinde yapılan model tahimininde mevsimsel etkiyi de dikkate alan SARIMA (1,1,0)(1,1,2) modelinin en uygun model olduğu belirlenmiştir. İkinci çalışmada ARIMAX modellemesi yapılarak eklenen dışsal değişkenlerle birlikte en uygun modelin (1,1)(0,0) olduğu tespit edilmiş, dışsal değişkenlerden ülkeye giriş ve çıkış yapan ticari uçak sayısının, ve Gayri Safi Milli Hasılanın modeli anlamlı şekilde desteklediği, ham petrol fiyatı alış fiyatının ise yolcu sayısının belirlenmesine etkisinin anlamlı olmadığı belirlenmiştir. Yapılan her iki modelin öngörü analizleri sonucunda elde edilen Theil katsayıları, yansızlık oranları (Bias) varyans açıklama oranları, hataların ortalama kare kökü (RMSE), hataların mutlak ortalaması (MAE), hataların ortalama mutlak yüzdesi (MAPE) ve modeli açıklama gücü R kare değerlerinin oldukça iyi seviyede oldukları belirlenmiş olup bununla birlikte, yapılan model karşılaştırmaları neticesinde ARIMAX modelinin daha iyi öngörü gücü olduğu sonucuna ulaşılmıştır.

JEL Classification : M31 , L93 , R41

Destekleyen Kurum

Bulunmamaktador

Proje Numarası

-

Teşekkür

Yer almamaktadır

Kaynakça

  • Abbas, K. A. (2004). Conceptual and Regression Models for Passenger Demand Prediction: A Case Study of Cairo Airport and Egyptair. Aerlines Magazine. E-zine ed., Issue 26. google scholar
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  • Alam, M. J. B., and D. M. Karim (1998). Air Travel Demand for Domestic Air Transportation in Bangladesh. Journal of Civil Engineering, Vol. CE26, No. 1, pp. 1-13. google scholar
  • Alekseev, K. P. G., and J. M. Seixa (2009). A Multivariate Neural Forecasting Modeling for Air Transport Preprocessed Decomposition: A Brazilian Application. Journal of Air Transport Management, Vol. 15, pp. 212-216. google scholar
  • Andreoni, A. and Postorino, M.N. (2006), “A multivariate ARIMA model to forecast air transport demand”, Proceedings of the Association for European Transport and Contributors, pp. 1-14. google scholar
  • Anderson, T. W. (2011). The statistical analysis of time series. John Wiley & Sons. google scholar
  • Anvari, S., Tuna, S., Canci, M. and Turkay, M. (2016). “Automated Box-Jenkins forecasting tool with an application for passenger demand in urban rail systems”, Journal of Advanced Transportation, Vol. 50 No. 1, pp. 25-49. google scholar
  • Arya, P.; Paul, R. K.; Kumar, A.; Singh, K. N.; Sivaramne, N.; Chaudhary, P. (2015) Predicting pest population using weather variables: an ARIMAX time series Framework, International Journal of Agricultural and Statistics Sciences, 11(2),381-386. google scholar
  • Asteriou, D. ve Hall, S.G. (2011). ARIMA models and the Box-Jenkins methodology. Applied Econometrics, 2(2), 265-286. google scholar
  • BaFail (2004). BaFail, A. Applying Data Mining Techniques to Forecast Number of Airline Passengers in Saudi Arabia (domestic and international travels). Journal of Air Transport, Vol. 9, No. 1, pp. 100-115. google scholar
  • Balık, M. (2015). Hava Kargo Taşımacılığı Ve Türkiye’deki Gelişimini Etkileyen Faktörler, Afyon Kocatepe Üniversitesi Sosyal Bilimler Enstitüsü, Yüksek Lisan Tezi. google scholar
  • Bierens H J. 1987. ARMAX model specification testing, with an application to unemployment in the Netherlands. Journal Econometrics 35: 161-90. google scholar
  • Benton, W. K. (1972). Forecasting for management. Reading, Mass: Addison-Wesley Publishing Company. google scholar
  • Bigovic', M. (2012). “Demand forecasting within Montenegrin tourism using Box-Jenkins methodology for seasonal ARIMA models”, Tourism and Hospitality Management, Vol. 18 No. 1, pp. 1-18. google scholar
  • Box, G.E.P ve Jenkins, G.M. (1976) Time Series Analysis Forecasting and Control. Revised Edition, Holden. DayInc., California, 170p. google scholar
  • Bozkurt, H.Y. (2013). Zaman Serileri Analizi, Ekin Yayınevi, Kocaeli. google scholar
  • Brown, R. G. (1963). Smoothing, forecasting, prediction. Engle-wood Cliffs. google scholar
  • BTCE (1995). Demand Elasticities for Air Travel to and from Australia. BTCE Working Paper 20. Bureau of Transport and Communications Economics, Canberra, Australia. google scholar
  • Cheze, B., P. Gastineau, and J. Chevallier (2010). Forecasting Air Traffic and Corresponding Jet-Fuel Demand Until 2025. Les cahiers de l’economie-77, Institut Francais du Petrole, Rueil-Malmaison, France. google scholar
  • Chudy-Laskowska, K. ve Pisula, T. (2017, January). Seasonal Forecasting for Air Passenger Trafficic. In 4th international multidisciplinary scientific conference on social sciences and arts SGEM 2017 (pp. 681-692). google scholar
  • Chu, F.-L. (2008). “A fractionally integrated autoregressive moving average approach to forecasting tourism demand”, Tourism Management, Vol. 29 No. 1, pp. 79-88. google scholar
  • Chu, F.-L. (2009). “Forecasting tourism demand with ARMA-based methods”, Tourism Management, Vol. 30 No. 5, pp. 740-51. google scholar
  • Cools, M., Moons, E., & Wets, G. (2009). Investigating the variability in daily traffic counts through use of ARIMAX and SARIMAX models: assessing the effect of holidays on two site locations. Transportation Research Record: Journal of the Transportation Research Board, (2136), 57-66. google scholar
  • Cryer, J.D. (1986). Time series analysis (Vol. 286). Boston: Duxbury Press. google scholar
  • Dargay, J., and M. Hanly (2002). The Determinants of the Demand for International Air Travel to and from the UK. Presented at Annual Conference of the University Transport Studies Group, Napier, New Zealand, 2002. google scholar
  • DfT (2009). UK Air Passenger Demand and CO2 Forecasts. Department for Transport (DfT), London. google scholar
  • DHMI (2022). www.DHMI.org.tr, (Erişim Tarihi: Aralık 2022). google scholar
  • EIA (2022). www.eia.gov/outlooks/steo/realprices, (Erişim Tarihi, Aralık 2022). google scholar
  • Granger, C. W. J. (1990). Spectral analysis. Time Series and Statistics, 263-267. google scholar
  • Gujarati, D. (2014). Econometrics by example. Macmillan International Higher Education. google scholar
  • Ghalehkhondabi, I., Ardjmand, E., Young, W. A., & Weckman, G. R. (2019). A review of demand forecasting models and methodological developments within tourism and passenger transportation industry. Journal of Tourism Futures, 5(1), 75-93. google scholar
  • Gong, W. (2010). “ARMA-GRNN for passenger demand forecasting”, 2010 Sixth International Conference on Natural Computation, IEEE, pp. 1577-81. google scholar
  • Fan, J., Shan, R., Cao, X., & Li, P. (2009). The analysis to tertiary-industry with ARIMAX model. Journal of Mathematics Research, 1(2), 156. google scholar
  • Fernandes, E., and R. R. Pacheco (2007). Air Transportation Analysis: Passenger Demand in Brazil. Aerlines Magazine. E-zine ed., Issue 33, 2023. http://www.aerlines.nl/index.php/magazine/volumes-115/volume-13/issue-33/. Erişim Tarihi: Şubat 2023. google scholar
  • Hassan, S.A. ve Quadi, A.T. (2018). Forecasting Passenger Numbers in Saudi Arabian Airlines Flights, International Journal of Engineering Science Invention (UESI) ISSN (Online): 2319 - 6734, ISSN (Print): 2319 - 6726. google scholar
  • IATA (2022). https://www.iata.org/en/pressroom/2022-releases/2022-12-06-01/#:~Erişim Tarihi (15.02.2023). google scholar
  • Janic M., (2000). An assessment of risk and safety in civil aviation. Journal of Air Transport Management 6 (1), 43-50. google scholar
  • Jenkins, G. M., & Watts, D. G. (1968). Spectraş Analysis and Its Applications, Holden-Day. google scholar
  • Johnson, L. A., Montgomery, D. C., & Gardiner, J. S. (1976). Forecasting and time series analysis. Ed: Mc Graw Hill. google scholar
  • Krasic', D. and Gatti, P. (2009), “Forecasting methodology of maritime passenger demand in a tourist destination”, PROMET-Traffic & Transportation, Vol. 21 No. 3, pp. 183-90. google scholar
  • Kulendran, N., and M. L. King (1997). Forecasting International Quarterly Tourist Flows Using Error-Correction and Time-Series Models. International Journal of Forecasting, Vol. 13, 1997, pp. 319-327. google scholar
  • Kutlar, A. (2017). Adım Adım Eviews ile Panel Veri Ekonometrisi Uygulamaları. Kocaeli: Umuttepe Yayınları. google scholar
  • Lewis, C. D. (1982). Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting. Butterworth-Heinemann. google scholar
  • Lim, C., and M. McAleer (2002). Time Series Forecasting of International Travel Demand for Australia. Tourism Management, Vol. 23, pp. 389-396. google scholar
  • Lim, C., J. C. H. Min, and M. McAleer (2008). Modelling Income Effects on Long and Short Haul International Travel from Japan. Tourism Management, Vol. 29, pp. 1099-1109. google scholar
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Toplam 72 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Yöneylem
Bölüm Araştırma Makalesi
Yazarlar

Vahap Önen 0000-0001-8592-9430

Proje Numarası -
Yayımlanma Tarihi 18 Ocak 2024
Gönderilme Tarihi 26 Mart 2023
Kabul Tarihi 5 Ekim 2023
Yayımlandığı Sayı Yıl 2023

Kaynak Göster

APA Önen, V. (2024). ARIMA-ARIMAX Yöntemiyle Türkiye Havayolu Yolcu Talep Tahmin Modellemesi, Öngörüsü Ve Karşılaştırması. Journal of Transportation and Logistics, 8(2), 242-273. https://doi.org/10.26650/JTL.2023.1270944
AMA Önen V. ARIMA-ARIMAX Yöntemiyle Türkiye Havayolu Yolcu Talep Tahmin Modellemesi, Öngörüsü Ve Karşılaştırması. JTL. Ocak 2024;8(2):242-273. doi:10.26650/JTL.2023.1270944
Chicago Önen, Vahap. “ARIMA-ARIMAX Yöntemiyle Türkiye Havayolu Yolcu Talep Tahmin Modellemesi, Öngörüsü Ve Karşılaştırması”. Journal of Transportation and Logistics 8, sy. 2 (Ocak 2024): 242-73. https://doi.org/10.26650/JTL.2023.1270944.
EndNote Önen V (01 Ocak 2024) ARIMA-ARIMAX Yöntemiyle Türkiye Havayolu Yolcu Talep Tahmin Modellemesi, Öngörüsü Ve Karşılaştırması. Journal of Transportation and Logistics 8 2 242–273.
IEEE V. Önen, “ARIMA-ARIMAX Yöntemiyle Türkiye Havayolu Yolcu Talep Tahmin Modellemesi, Öngörüsü Ve Karşılaştırması”, JTL, c. 8, sy. 2, ss. 242–273, 2024, doi: 10.26650/JTL.2023.1270944.
ISNAD Önen, Vahap. “ARIMA-ARIMAX Yöntemiyle Türkiye Havayolu Yolcu Talep Tahmin Modellemesi, Öngörüsü Ve Karşılaştırması”. Journal of Transportation and Logistics 8/2 (Ocak 2024), 242-273. https://doi.org/10.26650/JTL.2023.1270944.
JAMA Önen V. ARIMA-ARIMAX Yöntemiyle Türkiye Havayolu Yolcu Talep Tahmin Modellemesi, Öngörüsü Ve Karşılaştırması. JTL. 2024;8:242–273.
MLA Önen, Vahap. “ARIMA-ARIMAX Yöntemiyle Türkiye Havayolu Yolcu Talep Tahmin Modellemesi, Öngörüsü Ve Karşılaştırması”. Journal of Transportation and Logistics, c. 8, sy. 2, 2024, ss. 242-73, doi:10.26650/JTL.2023.1270944.
Vancouver Önen V. ARIMA-ARIMAX Yöntemiyle Türkiye Havayolu Yolcu Talep Tahmin Modellemesi, Öngörüsü Ve Karşılaştırması. JTL. 2024;8(2):242-73.



The JTL is being published twice (in April and October of) a year, as an official international peer-reviewed journal of the School of Transportation and Logistics at Istanbul University.