IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v35y2016i3p217-223.html
   My bibliography  Save this article

Comparison of Near Neighbour and Neural Network in Travel Forecasting

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Angesh Anupam & Isah A. Lawal, 2024. "Forecasting air passenger travel: A case study of Norwegian aviation industry," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 661-672, April.
    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. 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).
    4. 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.
    5. 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.
    6. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:jforec:v:35:y:2016:i:3:p:217-223. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.