What is it about?
This study evaluates the forecasting performance of neural networks compared to time series models. We use in inbound international tourism demand form all visitor markets to Catalonia.
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Why is it important?
Seasonality and volatility are important features of tourism data. In this context we compare the forecasting performance of linear models to that of nonlinear for different time horizons. We find that ARIMA models outperform SETAR and ANN models, especially for shorter forecasting horizons. Neural network models are more suitable in the presence of nonlinearity in the data. The results suggest that the there is a trade-off between the degree of pre-processing and the accuracy of the predictions. Forecasts of tourist arrivals are more accurate than forecasts of overnight stays.
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This page is a summary of: Forecasting tourism demand to Catalonia: Neural networks vs. time series models, Economic Modelling, January 2014, Elsevier, DOI: 10.1016/j.econmod.2013.09.024.
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