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Adiatma T., Mohidin R., Allagabo O., Mustafa O., Yulianti N.L.P., Irianto O., 2024: Forecasting the number of domestic airplane passenger arrivals using the ARIMA model, Global Advances in Business Studies 3(2), 70–82.
Al-Sultan A.T., Al-Rukaibi A., Alsaber A., Pan J., 2021: Forecasting Air Passenger Traffic Volume: Evaluating Time Series Models in Long-Term Forecasting of Kuwait Air Passenger Data, Advances and Applications in Statistics 70(1), 69–89, DOI:10.17654/AS070010069 (Crossref)
Alam M.S., Deb J.B., Al Amin A., Chowdhury S., 2024: An artificial neural network for predicting air traffic demand based on socio-economic parameters, Decision Analytics Journal 10, 100382. (Crossref)
Andreoni A., Postorino M.N. V., 2006: A multivariate ARIMA model to forecast air transport demand, Association for European Transport and Contributors, https://www.researchgate.net/publication/267254649_Amultivariate_ARIMA_model_to_forecast_air_transport_demand [accessed: 23.10.2025]. (Crossref)
Borkowski B., Krawiec M., 2009: Ryzyko cenowe na rynku surowców rolnych, [in:] M. Hamulczuk, S. Stańko (eds.), Zarządzanie ryzykiem cenowym a możliwości stabilizowania dochodów producentów rolnych – aspekty poznawcze i aplikacyjne, Wydawnictwo SGGW, Warszawa.
Borkowski T., Marcinkowski A., 1999: Bezrobocie w perspektywie socjologicznej, [in:] Socjologia bezrobocia, Wyd. Śląsk, Katowice.
Box G.E.P., Jenkins G.M., Reinsel G.C., Ljung G.M., 2015: Time Series Analysis: Forecasting and Control, Wiley, Hoboken, NJ.
Chen R., Wang L., 2014: Multistep-Ahead Air Passengers Traffic Prediction with Hybrid ARIMASVMs Models, The Scientific World Journal, 946026, https://doi.org/10.1155/2014/567246 (Crossref)
Chrabołowska J., Nazarko J., 2003: Arima Models in Forecasting Sales Gross Income, Zagadnienia Techniczno-Ekonomiczne 48(3).
Cieślak M. (red.), 2005: Prognozowanie gospodarcze. Metody i zastosowania, PWN, Warszawa.
Dantas T.M., Oliveira F.L.C., Repolho H.M.V., 2017: Air transportation demand forecast through Bagging Holt Winters methods, Journal of Air Transport Management 59, 116–123. (Crossref)
Do Q.H., Lo S., Chen J., Le C., Anh L.H., 2020: Forecasting Air Passenger Demand: A Comparison of LSTM and SARIMA, Journal of Computer Science 16(7), 1063–1084. (Crossref)
Ehsani S., Sergeeva E., Murdy W., Fox B., 2024: Predicting the Skies: A Novel Model for Flight-Level Passenger Traffic Forecasting, arXiv preprint, arXiv:2401.03397.
Firat M., Yiltas-Kaplan D., Samli R., 2021: Forecasting Air Travel Demand for Selected Destinations Using Machine Learning Methods, Journal of Universal Computer Science 27(6), 564–581. (Crossref)
Givoni M., Banister D., 2009: Airline and railway integration, Transport Policy 13(5), 386–397. (Crossref)
Guo X., Grushka-Cockayne Y., De Reyck B., Wang Z., 2019: Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning, Harvard Business School Working Paper 19-040. (Crossref)
He H., Chen L., Wang S., 2023: Flight short-term booking demand forecasting based on a long short-term memory network, Computers & Industrial Engineering 186, 109707. (Crossref)
Hyndman R.J., Athanasopoulos G., 2021: Forecasting: Principles and Practice, OTexts, Melbourne.
Jafari N., 2022: The chaos on U.S. domestic airline passenger demand forecasting caused by COVID-19, International Journal of Business Forecasting & Market Intelligence 7(3). (Crossref)
Jafari N., Lewison M., 2024: Forecasting air passenger traffic and market share using deep neural networks with multiple inputs and outputs, Frontiers in Artificial Intelligence 7, 1429341, https://doi.org/10.3389/frai.2024.1429341 (Crossref)
Jin F., Li Y., Sun S., Li H., 2020: Forecasting air passenger demand with a new hybrid ensemble approach, Journal of Air Transport Management 83, 101744. (Crossref)
Kanavos A., Kounelis F., Iliadis L., Makris C., 2021: Deep learning models for forecasting aviation demand time series, Neural Computing and Applications 33(21), 14481–14497. (Crossref)
Lundaeva K.A., Saranin Z.A., Pospelov K.N., Gintciak A.M., 2024: Demand Forecasting Model for Airline Flights Based on Historical Passenger Flow Data, Applied Sciences 14(23), 11413. (Crossref)
Muros Anguita J.G., Díaz Olariaga O., 2022: Air passenger demand forecast through the use of Artificial Neural Networks algorithms, International Journal of Aviation, Aeronautics, and Aerospace 9(3), 7. (Crossref)
Osińska M., 2006: Ekonometria finansowa, Polskie Wydawnictwo Ekonomiczne, Warszawa.
Ramadhani S., Dhini A., Laoh E., 2020: Airline Passenger Forecasting using ARIMA and Artificial Neural Networks Approaches, Proceedings of the 7th International Conference on ICT for Smart Society, 1–5. (Crossref)
Şimşek K., Tuğrul N.Ö.Ö., Karaçuha K., Tabatadze V., Karaçuha E. 2024: Modeling and Predicting Passenger Load Factor in Air Transportation: A Deep Assessment Methodology with Fractional Calculus Approach Utilizing Reservation Data, Fractal and Fractional 8(4), 214. (Crossref)
Tsui W.K.H., Balli H., Gilbey A., Gow H., 2014: Forecasting of Hong Kong airport’s passenger throughput, Tourism Management 42, 62–76, https://doi.org/10.1016/j.tourman.2013.10.008 (Crossref)
Verma N., 2025: Air Passenger and Freight Demand Forecast (Master’s thesis/Academic research), National College of Ireland, Dublin.
Wang L., Mykityshyn A., Johnson C., Marple B.D., 2021: Deep Learning for Flight Demand Forecasting, arXiv preprint, arXiv:2011.04476. (Crossref)
Witkowska D., Matuszewska-Janica A., Kompa K., 2012: Wprowadzenie do ekonometrii dynamicznej i finansowej, Wydawnictwo SGGW, Warszawa.
Wizz Air Airlines, 2025: www.wizzair.com [accessed: 23.10.2025].
Zachariah R.A., Sharma S., Kumar V., 2023: Systematic review of passenger demand forecasting in aviation industry, Multimedia Tools and Applications 82(30), 46483–46519, doi:10.1007/s11042-023-15552-1 (Crossref)
Zeliaś A., Pawełek B., Wanat S., 2013: Prognozowanie ekonomiczne: teoria, przykłady, zadania, PWN, Warszawa.
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