Common trends in international tourism demand: Are they useful to improve tourism predictions?

• 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.