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“Multiple-input multiple-output vs. single-input single-output neural network forecasting”

Author

Listed:
  • Oscar Claveria

    (Department of Econometrics. University of Barcelona)

  • Enric Monte

    (Department of Signal Theory and Communications. Polytechnic University of Catalunya.)

  • Salvador Torra

    (Department of Econometrics & Riskcenter-IREA. Universitat de Barcelona)

Abstract
This study attempts to improve the forecasting accuracy of tourism demand by using the existing common trends in tourist arrivals form all visitor markets to a specific destination in a multiple-input multiple-output (MIMO) structure. While most tourism forecasting research focuses on univariate methods, we compare the performance of three different Artificial Neural Networks in a multivariate setting that takes into account the correlations in the evolution of inbound international tourism demand to Catalonia (Spain). We find that the MIMO approach does not outperform the forecasting accuracy of the networks when applied country by country, but it significantly improves the forecasting performance for total tourist arrivals. When comparing the forecast accuracy of the different models, we find that radial basis function networks outperform multilayer-perceptron and Elman networks.

Suggested Citation

  • Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Multiple-input multiple-output vs. single-input single-output neural network forecasting”," AQR Working Papers 201502, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2015.
    • Handle: RePEc:aqr:wpaper:201502
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    References listed on IDEAS

    as
    1. Akkemik, K. Ali, 2012. "Assessing the importance of international tourism for the Turkish economy: A social accounting matrix analysis," Tourism Management, Elsevier, vol. 33(4), pages 790-801.
    2. Bermúdez, José D. & Corberán-Vallet, Ana & Vercher, Enriqueta, 2009. "Multivariate exponential smoothing: A Bayesian forecast approach based on simulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(5), pages 1761-1769.
    3. MacKinnon, James G & Haug, Alfred A & Michelis, Leo, 1999. "Numerical Distribution Functions of Likelihood Ratio Tests for Cointegration," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(5), pages 563-577, Sept.-Oct.
    4. Peng, Bo & Song, Haiyan & Crouch, Geoffrey I., 2014. "A meta-analysis of international tourism demand forecasting and implications for practice," Tourism Management, Elsevier, vol. 45(C), pages 181-193.
    5. Chu, Fong-Lin, 2011. "A piecewise linear approach to modeling and forecasting demand for Macau tourism," Tourism Management, Elsevier, vol. 32(6), pages 1414-1420.
    6. Tsang, Eric W. K., 2014. "Old and New," Management and Organization Review, Cambridge University Press, vol. 10(03), pages 390-390, November.
    7. Jacint Balaguer & Manuel Cantavella-Jorda, 2002. "Tourism as a long-run economic growth factor: the Spanish case," Applied Economics, Taylor & Francis Journals, vol. 34(7), pages 877-884.
    8. Medeiros, Marcelo C. & McAleer, Michael & Slottje, Daniel & Ramos, Vicente & Rey-Maquieira, Javier, 2008. "An alternative approach to estimating demand: Neural network regression with conditional volatility for high frequency air passenger arrivals," Journal of Econometrics, Elsevier, vol. 147(2), pages 372-383, December.
    9. Hadavandi, Esmaeil & Ghanbari, Arash & Shahanaghi, Kamran & Abbasian-Naghneh, Salman, 2011. "Tourist arrival forecasting by evolutionary fuzzy systems," Tourism Management, Elsevier, vol. 32(5), pages 1196-1203.
    10. Claveria, Oscar & Datzira, Jordi, 2008. "Tourism Demand in Catalonia: Detecting External Economic Factors," MPRA Paper 25303, University Library of Munich, Germany, revised 12 Apr 2008.
    11. Guizzardi, Andrea & Stacchini, Annalisa, 2015. "Real-time forecasting regional tourism with business sentiment surveys," Tourism Management, Elsevier, vol. 47(C), pages 213-223.
    12. Chen, Kuan-Yu, 2007. "Forecasting systems reliability based on support vector regression with genetic algorithms," Reliability Engineering and System Safety, Elsevier, vol. 92(4), pages 423-432.
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    Keywords

    Tourism demand; forecasting; multivariate; multiple-output; artificial neural networks JEL classification: C22; C45; C63; L83; R11;
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