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Comparison of Near Neighbour and Neural Network in Travel Forecasting

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  • Elena Olmedo

Abstract

In this paper we confirm the existence of nonlinear dynamics in a time series of airport arrivals. We subsequently propose alternative non‐parametric forecasting techniques to be used in a travel forecasting problem, emphasizing the difference between the reconstruction and learning approach. We compare the results achieved in point prediction versus sign prediction. The reconstruction approach offers better results in sign prediction and the learning approach in point prediction. Copyright © 2015 John Wiley & Sons, Ltd.

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  • Elena Olmedo, 2016. "Comparison of Near Neighbour and Neural Network in Travel Forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(3), pages 217-223, April.
    • Handle: RePEc:wly:jforec:v:35:y:2016:i:3:p:217-223
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    Cited by:

    1. Silva, Emmanuel Sirimal & Hassani, Hossein & Heravi, Saeed & Huang, Xu, 2019. "Forecasting tourism demand with denoised neural networks," Annals of Tourism Research, Elsevier, vol. 74(C), pages 134-154.
    2. 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.
    3. Shaolong Suna & Dan Bi & Ju-e Guo & Shouyang Wang, 2020. "Seasonal and Trend Forecasting of Tourist Arrivals: An Adaptive Multiscale Ensemble Learning Approach," Papers 2002.08021, arXiv.org, revised Mar 2020.
    4. Güner, Samet & Cebeci, Halil İbrahim, 2021. "Output targeting and capacity utilization for a new-built airport: Analysis for the new airport in Istanbul," Socio-Economic Planning Sciences, Elsevier, vol. 76(C).
    5. Marcos Álvarez-Díaz & Manuel González-Gómez & María Soledad Otero-Giráldez, 2018. "Forecasting International Tourism Demand Using a Non-Linear Autoregressive Neural Network and Genetic Programming," Forecasting, MDPI, vol. 1(1), pages 1-17, September.

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