-جلیلی، م. و منطقی، م.، (1397)، “تحلیل پیش بینی تقاضای مسافر و بار در صنعت هوایی ایران”، فصلنامه مطالعات پژوهشی، سال دهم، شماره اول، ص. 101-75.
-کفایی، س.م.ع. و کبیریراد، س.، (1390)، “برآورد تابع تقاضای حملونقل هوایی مسافر در پروازهای داخلی یکسر تهران”، پژوهشنامه حمل و نقل، سال هشتم، شماره دوم،
ص. 182-169.
-Domingos, Domingos S., João F.L. de Oliveira, and Paulo S.G. de Mattos Neto., (2019), “An Intelligent Hybridization of ARIMA with Machine Learning Models for Time Series Forecasting”. Knowledge-Based Systems 175, pp. 72–86.
https://doi.org/10.1016/j.knosys.2019.03.011.
-Fausett, Laurene, (1969), “Fundamentals Of Neural Networks”, IEEE Transactions on Computers C–18(6), pp.572.
-Jin, Feng, Yongwu Li, Shaolong Sun, and Hongtao Li., (2020), “Forecasting Air Passenger Demand with a New Hybrid Ensemble Approach”, Journal of Air Transport Management 83 (October 2019): 101744. https://doi.org/10.1016/j.jairtraman.2019.101744.
-Kanavos, Andreas, Fotios Kounelis, Lazaros Iliadis, and Christos Makris, (2021), “Deep Learning Models for Forecasting Aviation Demand Time Series”, Neural Computing and Applications 33(23), pp. 16329–43.
https://doi.org/10.1007/s00521-021-06232-y.
-Kim, Jungin et al., (2020), “Model Calibration and Forecasts of Air Travel Demand with Categorized Household Socioeconomic Attributes”, Transportation Research Record 2674(6), pp. 363–71.
-Kumar, Amir, and Abhisek Bhandari, (2021), “Modeling and Forecasting Passenger Demand for a New Domestic Airport with Limited Data”, Transportation Research Record (2214), pp. 59–68.
-Li, Cheng, (2019), “Combined Forecasting of Civil Aviation Passenger Volume Based on Arima-Regression”, International Journal of Systems Assurance Engineering and Management 10(5), pp. 945–52.
https://doi.org/10.1007/s13198-019-00825-6.
-Likas, Aristidis, Nikos Vlassis, and Jakob J. Verbeek, (2003), “The Global K-Means Clustering Algorithm.” Pattern Recognition 36(2), pp.451–61.
-Ming, Wei, Yukun Bao, Zhongyi Hu, and Tao Xiong., (2014), “Multistep-Ahead Air Passengers Traffic Prediction with Hybrid ARIMA-SVMs Models”, The Scientific World Journal.
-Naghawi, Hana, Ala’ Alobeidyeen, and Mu’Tasim Abdel-Jaber, (2019), “Econometric Modeling for International Passenger Air Travel Demand in Jordan”, Jordan Journal of Civil Engineering 13(3), pp.377–85.
-Qiao, Meiying, Shuhao Yan, Xiaxia Tang, and Chengkuan Xu., (2020), “Deep Convolutional and LSTM Recurrent Neural Networks for Rolling Bearing Fault Diagnosis under Strong Noises and Variable Loads”, IEEE Access 8, pp. 66257–69.
-Saâdaoui, Foued, Hayet Saadaoui, and Hana Rabbouch, (2020), “Hybrid Feedforward ANN with NLS-Based Regression Curve Fitting for US Air Traffic Forecasting”, Neural Computing and Applications 32(14),
pp. 10073–85.
-Samli, Ruya, Murat Firat, and Derya Yiltas-Kaplan, (2021), “Forecasting Air Travel Demand for Selected Destinations Using Machine Learning Methods”, Journal of Universal Computer Science 27(6),
pp. 564–81.
-Srisaeng, Panarat, Glenn S. Baxter, and Graham Wild, (2015), “Forecasting Demand for Low Cost Carriers in Australia Using an Artificial Neural Network Approach”, Aviation 19(2), pp. 90–103.
-Suh, Daniel Y., and Megan S. Ryerson, (2019), “Forecast to Grow: Aviation Demand Forecasting in an Era of Demand Uncertainty and Optimism Bias”, Transportation Research Part E: Logistics and Transportation Review 128 (December 2018), pp. 400–416.
https://doi.org/10.1016/j.tre.2019.06.016.
)