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

Marcos Álvarez-Díaz, Manuel González-Gómez and María Soledad Otero-Giráldez
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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
Authors registered in the RePEc Author Service: Mª Soledad Otero Giráldez

Forecasting, 2018, vol. 1, issue 1, 1-17

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.

Keywords: international tourism demand forecasting; artificial neural networks; genetic programming; SARIMA; Spain (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jforec:v:1:y:2018:i:1:p:7-106:d:169666

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