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

Created by W.Langdon from gp-bibliography.bib Revision:1.7642

@Article{Alvarez-Diaz:2019:Forecasting,

• author = "Marcos Alvarez-Diaz and Manuel Gonzalez-Gomez and Maria Soledad Otero-Giraldez",
• title = "Forecasting International Tourism Demand Using a Non-Linear Autoregressive Neural Network and Genetic Programming",
• journal = "Forecasting",
• year = "2019",
• volume = "1",
• number = "1",
• pages = "90--106",
• note = "Special Issue Applications of Forecasting by Hybrid Artificial Intelligent Technologies",
• keywords = "genetic algorithms, genetic programming, ANN, international tourism demand forecasting, artificial neural networks, SARIMA, spain",
• ISSN = "2571-9394",
• bibsource = "OAI-PMH server at oai.repec.org",
• identifier = "RePEc:gam:jforec:v:1:y:2018:i:1:p:7-106:d:169666",
• oai = "oai:RePEc:gam:jforec:v:1:y:2018:i:1:p:7-106:d:169666",
• URL = "https://www.mdpi.com/2571-9394/1/1/7/",
• URL = "https://www.mdpi.com/2571-9394/1/1/7.pdf",
• DOI = "doi:10.3390/forecast1010007",
• 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.",

}

Genetic Programming entries for Marcos Alvarez-Diaz Manuel Gonzalez Gomez Maria Soledad Otero-Giraldez

Citations