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A multivariate neural forecasting modeling for air transport – Preprocessed by decomposition: A Brazilian application

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  • Alekseev, K.P.G.
  • Seixas, J.M.

Abstract

An artificial neural forecasting model is developed for air transport passenger analysis. It uses a preprocessing method that decomposes information to reveal relevant features from the data. It is found that neural processing outperforms the traditional econometric approach and offers generalization on time series behavior, even where there are only small samples.

Suggested Citation

  • Alekseev, K.P.G. & Seixas, J.M., 2009. "A multivariate neural forecasting modeling for air transport – Preprocessed by decomposition: A Brazilian application," Journal of Air Transport Management, Elsevier, vol. 15(5), pages 212-216.
    • Handle: RePEc:eee:jaitra:v:15:y:2009:i:5:p:212-216
    DOI: 10.1016/j.jairtraman.2008.08.008
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    References listed on IDEAS

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    1. Tim Hill & Marcus O'Connor & William Remus, 1996. "Neural Network Models for Time Series Forecasts," Management Science, INFORMS, vol. 42(7), pages 1082-1092, July.
    2. Rech, Gianluigi, 2002. "Forecasting with artificial neural network models," SSE/EFI Working Paper Series in Economics and Finance 491, Stockholm School of Economics.
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    Cited by:

    1. Xiao, Yi & Liu, John J. & Hu, Yi & Wang, Yingfeng & Lai, Kin Keung & Wang, Shouyang, 2014. "A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting," Journal of Air Transport Management, Elsevier, vol. 39(C), pages 1-11.
    2. Ari, Didem & Mizrak Ozfirat, Pinar, 2024. "Comparison of artificial neural networks and regression analysis for airway passenger estimation," Journal of Air Transport Management, Elsevier, vol. 115(C).
    3. Hanson, Daniel & Toru Delibasi, Tuba & Gatti, Matteo & Cohen, Shamai, 2022. "How do changes in economic activity affect air passenger traffic? The use of state-dependent income elasticities to improve aviation forecasts," Journal of Air Transport Management, Elsevier, vol. 98(C).
    4. Tillmann, Andreas M. & Joormann, Imke & Ammann, Sabrina C.L., 2023. "Reproducible air passenger demand estimation," Journal of Air Transport Management, Elsevier, vol. 112(C).
    5. Plakandaras, Vasilios & Papadimitriou, Theophilos & Gogas, Periklis, 2019. "Forecasting transportation demand for the U.S. market," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 195-214.
    6. Hu, Yi & Xiao, Jin & Deng, Ying & Xiao, Yi & Wang, Shouyang, 2015. "Domestic air passenger traffic and economic growth in China: Evidence from heterogeneous panel models," Journal of Air Transport Management, Elsevier, vol. 42(C), pages 95-100.
    7. Phimphorn Sowawattanakul & Winai Wongsurawat, 2013. "Domestic airline networks and passenger demand in Thailand after deregulation," International Journal of Aviation Management, Inderscience Enterprises Ltd, vol. 2(1/2), pages 35-53.
    8. Dantas, Tiago Mendes & Cyrino Oliveira, Fernando Luiz & Varela Repolho, Hugo Miguel, 2017. "Air transportation demand forecast through Bagging Holt Winters methods," Journal of Air Transport Management, Elsevier, vol. 59(C), pages 116-123.
    9. Ryazanov Vlas, 2013. "Air mobility of people and airport growth potential in regions of Russia," Bulletin of Geography. Socio-economic Series, Sciendo, vol. 22(22), pages 97-110, December.
    10. Xu, Shuojiang & Chan, Hing Kai & Zhang, Tiantian, 2019. "Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 169-180.
    11. Gizem Kaya & Umut Aydın & Burç Ülengin, 2023. "A Comparison of Forecasting Performance of PPML and OLS estimators: The Gravity Model in the Air Cargo Market," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 0(39), pages 112-128, December.
    12. Gunter, Ulrich & Zekan, Bozana, 2021. "Forecasting air passenger numbers with a GVAR model," Annals of Tourism Research, Elsevier, vol. 89(C).
    13. Mueller, Falko, 2023. "Link and edge weight prediction in air transport networks — An RNN approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 613(C).
    14. Abdelghany, Ahmed & Guzhva, Vitaly S., 2022. "Exploratory analysis of air travel demand stimulation in first-time served markets," Journal of Air Transport Management, Elsevier, vol. 98(C).

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