کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1013625 | 1482662 | 2015 | 7 صفحه PDF | دانلود رایگان |
• We incorporate existing common trends in tourist arrivals from all visitor markets to a specific destination in NN models.
• We assess three NN models in a multiple-input multiple-output (MIMO) setting.
• We find that hybrid models such as RBF NN outperform MLP and the Elman networks.
• MIMO settings prove useful when the evolution of tourist arrivals from visitor markets share a common trend.
• We find no significant differences when additional lags are incorporated in the models.
This study evaluates whether modelling the existing common trends in tourist arrivals from all visitor markets to a specific destination can improve tourism predictions. 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 multivariate multiple-output 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.
Journal: Tourism Management Perspectives - Volume 16, October 2015, Pages 116–122