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Forecasting air passengers at São Paulo International Airport using a mixture of local experts model

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  • Scarpel, Rodrigo Arnaldo

Abstract

An integrated mixture of local experts model is employed to forecast air passengers at São Paulo International Airport. Such approach is normally used in rapidly changing situations, i.e., when the time series presents turning points or any kind of structural change and allows the development of forecasting models that takes into account the heterogeneity of the mapping structure into different regions of the input space. The model is validated using out-of-sample data, and the accuracy of the generated predictions proves to be satisfactory. An assessment of uncertainty in the predictions is made, as well as long lead-time forecasts employing the built model, considering different scenarios.

Suggested Citation

  • Scarpel, Rodrigo Arnaldo, 2013. "Forecasting air passengers at São Paulo International Airport using a mixture of local experts model," Journal of Air Transport Management, Elsevier, vol. 26(C), pages 35-39.
    • Handle: RePEc:eee:jaitra:v:26:y:2013:i:c:p:35-39
    DOI: 10.1016/j.jairtraman.2012.10.001
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    References listed on IDEAS

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    1. Profillidis, V.A, 2000. "Econometric and fuzzy models for the forecast of demand in the airport of Rhodes," Journal of Air Transport Management, Elsevier, vol. 6(2), pages 95-100.
    2. Nenad Njegovan, 2005. "A leading indicator approach to predicting short-term shifts in demand for business travel by air to and from the UK," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(6), pages 421-432.
    3. Grubb, Howard & Mason, Alexina, 2001. "Long lead-time forecasting of UK air passengers by Holt-Winters methods with damped trend," International Journal of Forecasting, Elsevier, vol. 17(1), pages 71-82.
    4. Samagaio, António & Wolters, Mark, 2010. "Comparative analysis of government forecasts for the Lisbon Airport," Journal of Air Transport Management, Elsevier, vol. 16(4), pages 213-217.
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    Cited by:

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    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. Guo Rui & Zhong Zhaowei, 2017. "Forecasting the Air Passenger Volume in Singapore: An Evaluation of TimeSeries Models," International Journal of Technology and Engineering Studies, PROF.IR.DR.Mohid Jailani Mohd Nor, vol. 3(3), pages 117-123.
    4. Scarpel, Rodrigo Arnaldo, 2014. "A demand trend change early warning forecast model for the city of São Paulo multi-airport system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 65(C), pages 23-32.
    5. Scarpel, Rodrigo Arnaldo, 2015. "An integrated mixture of local experts model for demand forecasting," International Journal of Production Economics, Elsevier, vol. 164(C), pages 35-42.
    6. Chen, Jieh-Haur & Wei, Hsi-Hsien & Chen, Chih-Lin & Wei, Hsin-Yi & Chen, Yi-Ping & Ye, Zhongnan, 2020. "A practical approach to determining critical macroeconomic factors in air-traffic volume based on K-means clustering and decision-tree classification," Journal of Air Transport Management, Elsevier, vol. 82(C).
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    8. Wang, Sen & Gao, Yi, 2021. "A literature review and citation analyses of air travel demand studies published between 2010 and 2020," Journal of Air Transport Management, Elsevier, vol. 97(C).
    9. Gunter, Ulrich & Zekan, Bozana, 2021. "Forecasting air passenger numbers with a GVAR model," Annals of Tourism Research, Elsevier, vol. 89(C).

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