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

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Department of Economics, Universidade de Vigo, 36310 Vigo, Spain
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Departament of Applied Economics, Universidade de Vigo, 36310 Vigo, Spain
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Author to whom correspondence should be addressed.
Forecasting 2019, 1(1), 90-106; https://doi.org/10.3390/forecast1010007
Received: 17 August 2018 / Revised: 10 September 2018 / Accepted: 10 September 2018 / Published: 13 September 2018
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. View Full-Text
Keywords: international tourism demand forecasting; artificial neural networks; genetic programming; SARIMA; Spain international tourism demand forecasting; artificial neural networks; genetic programming; SARIMA; Spain
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Álvarez-Díaz, M.; González-Gómez, M.; Otero-Giráldez, M.S. Forecasting International Tourism Demand Using a Non-Linear Autoregressive Neural Network and Genetic Programming. Forecasting 2019, 1, 90-106.

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