Tourism time series forecast with artificial neural networks

The modulation of tourism time series was used in this work for forecast purposes. The Tourism Revenue and Total Overnights registered in the hotels of the North region of Por- tugal were used for the experimented models. Several feed-forward Artificial Neural Networks (ANN) models using different input features and number of hidden nodes were experimented to forecast the Tourism time series. Empirical results indicate that the Dedicated ANN models perform better than models with several outputs. Generally the usage of previous 12 values of the same time series is very important to a good quality forecast. For the prediction of Tourism Revenue the Foreign Overnights and GDP of contributing countries are relevant. This time series was predicted with an error of 4.7% and a Pearson correlation of 0.98. The forecast of Total Overnights had an error of 6.0% and Pearson correlation of 0.98. Domestic Overnights are more predictable than Foreign Overnights.