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Forecasting International Tourism Demand Using a Non-Linear Autoregressive Neural Network and Genetic Programming

Author

Listed:
  • Marcos Álvarez-Díaz

    (Department of Economics, Universidade de Vigo, 36310 Vigo, Spain)

  • Manuel González-Gómez

    (Departament of Applied Economics, Universidade de Vigo, 36310 Vigo, Spain)

  • María Soledad Otero-Giráldez

    (Departament of Applied Economics, Universidade de Vigo, 36310 Vigo, Spain)

Abstract

This study explores the forecasting ability of two powerful non-linear computational methods: artificial neural networks and genetic programming. We use as a case of study the monthly international tourism demand in Spain, approximated by the number of tourist arrivals and of overnight stays. The forecasting results reveal that non-linear methods achieve slightly better predictions than those obtained by a traditional forecasting technique, the seasonal autoregressive integrated moving average (SARIMA) approach. This slight forecasting improvement was close to being statistically significant. Forecasters must judge whether the high cost of implementing these computational methods is worthwhile.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jforec:v:1:y:2018:i:1:p:7-106:d:169666
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    References listed on IDEAS

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