Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1013625 | Tourism Management Perspectives | 2015 | 7 Pages |
•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.