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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">caht</journal-id><journal-title-group><journal-title xml:lang="ru">Научный вестник МГТУ ГА</journal-title><trans-title-group xml:lang="en"><trans-title>Civil Aviation High Technologies</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2079-0619</issn><issn pub-type="epub">2542-0119</issn><publisher><publisher-name>Moscow State Technical University of Civil Aviation (MSTU CA)</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26467/2079-0619-2025-28-2-8-21</article-id><article-id custom-type="elpub" pub-id-type="custom">caht-2543</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>ТРАНСПОРТНЫЕ СИСТЕМЫ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>TRANSPORTATION SYSTEMS</subject></subj-group></article-categories><title-group><article-title>Гибридная модель прогнозирования нерегулярных пассажирских авиаперевозок</article-title><trans-title-group xml:lang="en"><trans-title>Hybrid forecasting model of non-scheduled passenger air transportation</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Агаев</surname><given-names>Н. Б.</given-names></name><name name-style="western" xml:lang="en"><surname>Aghayev</surname><given-names>N. B.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Агаев Надир Бафадин оглы, доктор технических наук, профессор кафедры компьютерных систем и программирования Национальной авиационной академии, Институт информационных технологий</p><p>Баку</p></bio><bio xml:lang="en"><p>Nadir B. Aghayev, Doctor of Technical Sciences, Professor, Department of Computer Systems and Programming, National Aviation Academy and Institute of Information Technology</p><p>Baku</p></bio><email xlink:type="simple">nadir_avia@yahoo.com</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Назарли</surname><given-names>Д. Ш.</given-names></name><name name-style="western" xml:lang="en"><surname>Nazarli</surname><given-names>D. Sh.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Назарли Дашгин, аспирант кафедры авиатранспортного производства воздушного транспорта </p><p>Баку</p></bio><bio xml:lang="en"><p>Dashqin Sh. Nazarli, Postgraduate Student, Department of Air Transport Production</p><p>Baku</p></bio><email xlink:type="simple">dnazarli.32073@naa.edu.az</email><xref ref-type="aff" rid="aff-2"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru">Национальная авиационная академия;&#13;
Институт информационных технологий Министерства науки и образования Азербайджанской Республики<country>Азербайджан</country></aff><aff xml:lang="en">National Aviation Academy;&#13;
Ministry of Science and Education of the Republic of Azerbaijan Institute of Information Technology<country>Azerbaijan</country></aff></aff-alternatives><aff-alternatives id="aff-2"><aff xml:lang="ru">Национальная авиационная академия<country>Азербайджан</country></aff><aff xml:lang="en">National Aviation Academy<country>Azerbaijan</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2025</year></pub-date><pub-date pub-type="epub"><day>08</day><month>05</month><year>2025</year></pub-date><volume>28</volume><issue>2</issue><fpage>8</fpage><lpage>21</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Агаев Н.Б., Назарли Д.Ш., 2025</copyright-statement><copyright-year>2025</copyright-year><copyright-holder xml:lang="ru">Агаев Н.Б., Назарли Д.Ш.</copyright-holder><copyright-holder xml:lang="en">Aghayev N.B., Nazarli D.S.</copyright-holder><license license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://avia.mstuca.ru/jour/article/view/2543">https://avia.mstuca.ru/jour/article/view/2543</self-uri><abstract><p>В статье предлагается гибридная модель на основе ARIMA-Fuzzy для прогнозирования временных рядов нерегулярных пассажирских авиаперевозок. Как известно, модель ARIMA применяется для выявления линейных тенденций и закономерностей в данных временных рядов, а также для прогнозирования. Изучение научной литературы показывает, что модель ARIMA имеет свои ограничения в управлении нелинейностью и случайными изменениями во время прогнозирования. Поскольку процесс нерегулярных авиаперевозок как стохастический процесс зависит от случайных изменений, указанная модель не позволяет описывать весь процесс. По этой причине модель ARIMA не дает достаточно эффективных результатов для моделирования нелинейных и случайных изменений данных в процессе нерегулярных авиаперевозок. В связи с этим для повышения точности прогноза в исследовании применяется гибридная модель, основанная на модели авторегрессии ARIMA вместе с нечеткой моделью случайных отклонений. Апробация разработанной гибридной модели осуществлена на примере прогнозирования пассажиропотоков нерегулярных рейсов в Азербайджане. Полученные результаты показывают, что модель в таком виде обеспечивает более надежные и эффективные прогнозы по сравнению с применением независимых моделей.</p></abstract><trans-abstract xml:lang="en"><p> In the article, an ARIMA-Fuzzy-based hybrid model is proposed for forecasting time series of non-scheduled passenger air transportation. As it is known, the ARIMA model is applied to identify linear trends and regularities within time series data as well as for forecasting. The study of scientific research literature shows that the ARIMA model has its own limitations in managing non-linearity and random changes during forecasting. Since the process of non-scheduled air transportation depends on random changes as a stochastic process, the mentioned model does not cover the whole process. For this reason, the ARIMA model does not provide effective enough results outcome strong enough to model non-linear and random changes in the data in the process of non-scheduled air transportation. In this regard, the ARIMA model was applied together with the fuzzy model. The hybrid model, based on ARIMA’s autoregression model, is applied together with the random deviation fuzzy model to further increase the accuracy of the forecast. The results obtained as a result of the application of the hybrid model show that the model in this form provides more reliable and efficient forecasts compared to independent models.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>нерегулярные авиаперевозки</kwd><kwd>гибридная модель</kwd><kwd>статистический анализ</kwd><kwd>нечеткая модель</kwd><kwd>анализ временных рядов</kwd><kwd>оптимальная модель</kwd><kwd>прогнозирование</kwd><kwd>авторегрессионная модель</kwd></kwd-group><kwd-group xml:lang="en"><kwd>non-scheduled air transportation/transport/services</kwd><kwd>hybrid model</kwd><kwd>statistical analysis</kwd><kwd>fuzzy model</kwd><kwd>time series analysis</kwd><kwd>optimal model</kwd><kwd>forecasting</kwd><kwd>autoregressive model</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Aghayev N., Nazarli D. Support vector machines for forecasting non-scheduled passenger air transportation // Problems of Information Technology. 2024. Vol. 15, no. 1. Pp. 3–9. DOI: 10.25045/jpit.v15.i1.01</mixed-citation><mixed-citation xml:lang="en">Aghayev, N., Nazarli, D. (2024). Support vector machines for forecasting non-scheduled passenger air transportation. Problems of Information Technology, vol. 15, no. 1, pp. 3–9. DOI: 10.25045/jpit.v15.i1.01</mixed-citation></citation-alternatives></ref><ref id="cit2"><label>2</label><citation-alternatives><mixed-citation xml:lang="ru">Li C. Combined forecasting of civil aviation passenger volume based on ARIMA-REGRESSION // International Journal of System Assurance Engineering and Management. 2019. Vol. 10. Pp. 945–952. DOI: 10.1007/s13198-019-00825-6</mixed-citation><mixed-citation xml:lang="en">Li, C. (2019). Combined forecasting of civil aviation passenger volume based on ARIMA-REGRESSION. International Journal of System Assurance Engineering and Management, vol. 10, pp. 945–952. DOI: 10.1007/s13198-019-00825-6</mixed-citation></citation-alternatives></ref><ref id="cit3"><label>3</label><citation-alternatives><mixed-citation xml:lang="ru">Ramadhani S., Dhini A., Laoh E. Airline passenger forecasting using ARIMA and artificial neural networks approaches [Электронный ресурс] // Proceedings of the 7th International Conference on ICT Smart Society (ICISS 2020). Pp. 1–5. DOI: 10.1109/ICISS50791.2020.9307571 (дата обращения: 18.12.2024).</mixed-citation><mixed-citation xml:lang="en">Ramadhani, S., Dhini, A., Laoh, E. (2020). Airline passenger forecasting using ARIMA and artificial neural networks approaches. In: Proceedings of the 7th International Conference on ICT Smart Society (ICISS 2020), pp. 1–5. DOI: 10.1109/ICISS50791.2020.9307571 (accessed: 18.12.2024).</mixed-citation></citation-alternatives></ref><ref id="cit4"><label>4</label><citation-alternatives><mixed-citation xml:lang="ru">Asrah N. Time series forecasting of the number of Malaysia airlines and AirAsia passengers / N. Asrah, M. Nor, S. Rahim, W. Leng [Электронный ресурс] // Journal of Physics: Conference Series. 2018. Vol. 995. ID: 012006. DOI: 10.1088/1742-6596/995/1/012006 (дата обращения: 18.12.2024).</mixed-citation><mixed-citation xml:lang="en">Asrah, N., Nor, M., Rahim, S., Leng, W. (2018). Time series forecasting of the number of Malaysia airlines and AirAsia passengers. Journal of Physics: Conference Series, vol. 995, ID: 012006. DOI: 10.1088/1742-6596/995/1/012006 (accessed: 18.12.2024).</mixed-citation></citation-alternatives></ref><ref id="cit5"><label>5</label><citation-alternatives><mixed-citation xml:lang="ru">Jin F. Forecasting air passenger demand with a new hybrid ensemble approach / F. Jin, Y. Li, S. Sun, H. Li [Электронный ресурс] // Journal of Air Transport Management. 2020. Vol. 83. ID: 101744. DOI: 10.1016/j.jairtraman.2019.101744 (дата обращения: 18.12.2024).</mixed-citation><mixed-citation xml:lang="en">Jin, F., Li, Y., Sun, S., Li, H. (2020). Forecasting air passenger demand with a new hybrid ensemble approach. Journal of Air Transport Management, vol. 83, ID: 101744. DOI: 10.1016/j.jairtraman.2019.101744 (accessed: 18.12.2024).</mixed-citation></citation-alternatives></ref><ref id="cit6"><label>6</label><citation-alternatives><mixed-citation xml:lang="ru">Madhavan M. Short-term forecasting for airline industry: the case of Indian air passenger and air cargo / M. Madhavan, M.A. Sharafuddin, P. Piboonrungroj, C.-C. Yang // Global Business Review. 2023. Vol. 24, iss. 6. Pp. 1145–1179. DOI: 10.1177/0972150920923316</mixed-citation><mixed-citation xml:lang="en">Madhavan, M., Sharafuddin, M.A., Piboonrungroj, P., Yang, C.-C. (2023). Short-term forecasting for airline industry: the case of Indian air passenger and air cargo. Global Business Review, vol. 24, issue 6, pp. 1145–1179. DOI: 10.1177/0972150920923316</mixed-citation></citation-alternatives></ref><ref id="cit7"><label>7</label><citation-alternatives><mixed-citation xml:lang="ru">Nguyen Q.H., Tran P.Q., Ngo P.D. (2025). Air cargo traffic forecasting model: An empirical study in Vietnam using the SARIMA-X/(E)GARCH model [Электронный ресурс] // Research in Transportation Business &amp; Management. 2025. Vol. 59. ID: 101268. DOI: 10.1016/j.rtbm.2024.101268 (дата обращения: 18.12.2024).</mixed-citation><mixed-citation xml:lang="en">Nguyen, Q.H., Tran, P.Q., Ngo, P.D. (2025). Air cargo traffic forecasting model: An empirical study in Vietnam using the SARIMA-X/(E)GARCH model. Research in Transportation Business &amp; Management, vol. 59, ID: 101268. DOI: 10.1016/j.rtbm.2024.101268 (accessed: 18.12.2024).</mixed-citation></citation-alternatives></ref><ref id="cit8"><label>8</label><citation-alternatives><mixed-citation xml:lang="ru">Anguita J.G.M., Olariaga O.D. Air cargo transport demand forecasting using ConvLSTM2D, an artificial neural network architecture approach [Электронный ресурс] // Case Studies on Transport Policy. 2023. Vol. 12. ID: 101009. DOI: 10.1016/j.cstp.2023.101009 (дата обращения: 18.12.2024).</mixed-citation><mixed-citation xml:lang="en">Anguita, J.G.M., Olariaga, O.D. (2023). Air cargo transport demand forecasting using ConvLSTM2D, an artificial neural network architecture approach. Case Studies on Transport Policy, vol. 12, ID: 101009. DOI: 10.1016/j.cstp.2023.101009 (accessed: 18.12.2024).</mixed-citation></citation-alternatives></ref><ref id="cit9"><label>9</label><citation-alternatives><mixed-citation xml:lang="ru">Alexander D.W., Merkert R. Appli­cations of gravity models to evaluate and forecast US international air freight markets post-GFC // Transport Policy. 2021. Vol. 104. Pp 52–62. DOI: 10.1016/j.tranpol.2020.04.004</mixed-citation><mixed-citation xml:lang="en">Alexander, D.W., Merkert, R. (2021). Applications of gravity models to evaluate and forecast US international air freight markets post-GFC, Transport Policy, vol. 104, pp. 52–62. DOI: 10.1016/j.tranpol.2020.04.004</mixed-citation></citation-alternatives></ref><ref id="cit10"><label>10</label><citation-alternatives><mixed-citation xml:lang="ru">Nieto M.R., Carmona-Benítez R.B. ARIMA + GARCH + Bootstrap forecasting method applied to the airline industry // Journal of Air Transport Management. 2018. Vol. 71 (C). Pp. 1–8. DOI: 10.1016/j.jairtraman.2018.05.007</mixed-citation><mixed-citation xml:lang="en">Nieto, M.R., Carmona-Benítez, R.B. (2018). ARIMA + GARCH + Bootstrap forecasting method applied to the airline industry. Journal of Air Transport Management, vol. 71 (C), pp. 1–8. DOI: 10.1016/j.jairtraman.2018.05.007</mixed-citation></citation-alternatives></ref><ref id="cit11"><label>11</label><citation-alternatives><mixed-citation xml:lang="ru">Tolcha T.D. The state of Africa’s air transport market amid COVID-19, and forecasts for recovery [Электронный ресурс] // Journal of Air Transport Management. 2023. Vol. 108. ID: 102380. DOI: 10.1016/j.jairtraman.2023.102380 (дата обращения: 18.12.2024).</mixed-citation><mixed-citation xml:lang="en">Tolcha, T.D. (2023). The state of Africa’s air transport market amid COVID-19, and forecasts for recovery. Journal of Air Transport Management, vol. 108, ID: 102380. DOI: 10.1016/j.jairtraman.2023.102380 (accessed: 18.12.2024).</mixed-citation></citation-alternatives></ref><ref id="cit12"><label>12</label><citation-alternatives><mixed-citation xml:lang="ru">Aljanabi M.R. SVD-based adaptive fuzzy for generalized transportation / M.R. Aljanabi, K. Borna, Sh. Ghanbari, A.J. Obaid // Alexandria Engineering Journal. 2024. Vol. 94. Pp. 377–396. DOI: 10.1016/j.aej.2024.03.020</mixed-citation><mixed-citation xml:lang="en">Aljanabi, M.R., Borna, K., Ghanbari, Sh., Obaid, A.J. (2024). SVD-based adaptive fuzzy for generalized transportation. Alexandria Engineering Journal, vol. 94, pp. 377–396. DOI: 10.1016/j.aej.2024.03.020</mixed-citation></citation-alternatives></ref><ref id="cit13"><label>13</label><citation-alternatives><mixed-citation xml:lang="ru">Tanwar R., Agarwal P.K. Assessing travel time performance of multimodal transportation systems using fuzzy-analytic hierarchy process: A case study of Bhopal City [Электронный ресурс] // Heliyon. 2024. Vol. 10, iss. 17. DOI: 10.1016/j.heliyon.2024.e36844 (дата обращения: 18.12.2024).</mixed-citation><mixed-citation xml:lang="en">Tanwar, R., Agarwal, P.K. (2024). Assessing travel time performance of multimodal transportation systems using fuzzy-analytic hierarchy process: A case study of Bhopal City. Heliyon, vol. 10, issue 17, ID: e36844. DOI: 10.1016/j.heliyon.2024.e36844 (accessed: 18.12.2024).</mixed-citation></citation-alternatives></ref><ref id="cit14"><label>14</label><citation-alternatives><mixed-citation xml:lang="ru">Djimasbe R. Development of an ARIMAX model for forecasting airport electricity consumption in Accra-Ghana: The role of weather and air passenger traffic / R. Djimasbe, S. Gyamfi, C.D. Iweh, B.N. Ribar [Электронный ресурс] // e-Prime – Advances in Electrical Engineering, Electronics and Energy. 2024. Vol. 9. ID: 100691. DOI: 10.1016/j.prime.2024.100691 (дата обращения: 18.12.2024).</mixed-citation><mixed-citation xml:lang="en">Djimasbe, R., Gyamfi, S., Iweh, C.D., Ribar, B.N. (2024). Development of an ARIMAX model for forecasting airport electricity consumption in Accra-Ghana: The role of weather and air passenger traffic. e-Prime – Advances in Electrical Engineering, Electronics and Energy, vol. 9, ID: 100691. DOI: 10.1016/j.prime.2024.100691 (accessed: 18.12.2024).</mixed-citation></citation-alternatives></ref><ref id="cit15"><label>15</label><citation-alternatives><mixed-citation xml:lang="ru">Hopfe D.H., Lee K., Yu C. Short-term forecasting airport passenger flow during periods of volatility: Comparative investigation of time series vs. neural network models [Электронный ресурс] // Journal of Air Transport Management. 2024. Vol. 115. ID: 102525. DOI: 10.1016/j.jairtraman.2023.102525 (дата обращения: 18.12.2024).</mixed-citation><mixed-citation xml:lang="en">Hopfe, D.H., Lee, K., Yu, C. (2024). Short-term forecasting airport passenger flow during periods of volatility: Comparative investigation of time series vs. neural network models. Journal of Air Transport Management, vol. 115, ID: 102525. DOI: 10.1016/j.jairtraman.2023.102525 (accessed: 18.12.2024).</mixed-citation></citation-alternatives></ref><ref id="cit16"><label>16</label><citation-alternatives><mixed-citation xml:lang="ru">Carmona-Benitez R.B., Nieto M.R. SARIMA damp trend grey forecasting model for airline industry [Электронный ресурс] // Journal of Air Transport Management. 2020. Vol. 82. ID: 101736. DOI: 10.1016/J.JAIRTRAMAN.2019.101736 (дата обращения: 18.12.2024).</mixed-citation><mixed-citation xml:lang="en">Carmona-Benítez, R.B., Nieto, M.R. (2020). SARIMA damp trend grey forecasting model for airline industry. Journal of Air Transport Management, vol. 82, ID: 101736. DOI: 10.1016/J.JAIRTRAMAN.2019.101736 (accessed: 18.12.2024).</mixed-citation></citation-alternatives></ref><ref id="cit17"><label>17</label><citation-alternatives><mixed-citation xml:lang="ru">Sun S. Nonlinear vector auto-regression neural network for forecasting air passenger flow / S. Sun, H. Lu, K. Tsui, S. Wang // Journal of Air Transport Management. 2019. Vol. 78. Pp. 54–62. DOI: 10.1016/j.jairtraman.2019.04.005</mixed-citation><mixed-citation xml:lang="en">Sun, S., Lu, H., Tsui, K., Wang, S. (2019). Nonlinear vector auto-regression neural network for forecasting air passenger flow. Journal of Air Transport Management, vol. 78, pp. 54–62. DOI: 10.1016/j.jairtraman.2019.04.005</mixed-citation></citation-alternatives></ref><ref id="cit18"><label>18</label><citation-alternatives><mixed-citation xml:lang="ru">Shramenko N., Muzylyov D. Forecasting of overloading volumes in transport systems based on the fuzzy-neural model. In book: Advances in Design, Simulation and Manufacturing II. Springer International Publishing, Cham, 2019. Pp. ­311–320. DOI: 10.1007/978-3-030-22365-6_31</mixed-citation><mixed-citation xml:lang="en">Shramenko, N., Muzylyov, D. (2019). Forecasting of overloading volumes in transport systems based on the fuzzy-neural model. In book: Advances in Design, Simulation and Manufacturing II. Springer International Publishing, Cham, pp. 311–320. DOI: 10.1007/978-3-030-22365-6_31</mixed-citation></citation-alternatives></ref><ref id="cit19"><label>19</label><citation-alternatives><mixed-citation xml:lang="ru">Souza J.A.F. A forecasting model based on ARIMA and artificial neural networks for end-of-life vehicles / J.A.F. Souza, M.M. de Silva, S.G. Rodrigues, S.M. Santos [Электронный ресурс] // Journal of Environmental Management. 2022. Vol. 318, ID: 115616. DOI: 10.1016/j.jenvman.2022.115616 (дата обращения: 18.12.2024).</mixed-citation><mixed-citation xml:lang="en">Souza, J.A.F., de Silva, M.M., Rodrigues, S.G., Santos, S.M. (2022). A fore­casting model based on ARIMA and artificial neural networks for end-of-life vehicles. Journal of Environmental Management, vol. 318, ID: 115616. DOI: 10.1016/j.jenvman.2022.115616 (accessed: 18.12.2024).</mixed-citation></citation-alternatives></ref><ref id="cit20"><label>20</label><citation-alternatives><mixed-citation xml:lang="ru">Gu W. Civil Aviation Passenger Traffic Forecasting: Application and Comparative Study of the Seasonal Autoregressive Integrated Moving Average Model and Backpropagation Neural Network / W. Gu, B. Guo, Z. Zhang, H. Lu [Электронный ресурс] // Sustainability. 2024. Vol. 16, iss. 10. ID: 4110. DOI: 10.3390/su16104110 (accessed: 18.12.2024).</mixed-citation><mixed-citation xml:lang="en">Gu, W., Guo, B., Zhang, Z., Lu, H. (2024). Civil Aviation Passenger Traffic Forecasting: Application and Comparative Study of the Seasonal Autoregressive Integrated Moving Average Model and Backpropagation Neural Network. Sustainability, vol. 16, issue 10, ID: 4110. DOI: 10.3390/su16104110 (accessed: 18.12.2024).</mixed-citation></citation-alternatives></ref><ref id="cit21"><label>21</label><citation-alternatives><mixed-citation xml:lang="ru">Aghayev N., Nazarli D. Forecasting models of non-scheduled passenger air transportation through regression analysis // Scientific Journal. 2023. Vol. 25, no. 4. Pp. 13–22. DOI: 10.30546/EMNAA.2023.25.4.2</mixed-citation><mixed-citation xml:lang="en">Aghayev, N., Nazarli, D. (2023). Forecasting models of non-scheduled passenger air transportation through regression analysis. Scientific Journal, vol. 25, no. 4, pp. 13–22. DOI: 10.30546/EMNAA.2023.25.4.2</mixed-citation></citation-alternatives></ref></ref-list><fn-group><fn fn-type="conflict"><p>The authors declare that there are no conflicts of interest present.</p></fn></fn-group></back></article>
