Forecasting of a non-seasonal tourism time series with ANN
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The paper present and discusses several alternative architectures of
Artificial Neural Network models used to predict the time series of tourism demand
for Cape Verde. This time series is particularly difficult to predict due to
its non-seasonal characteristic usual in a similar time series for European Tourism
destinations. The time index used as input and other input parameters variations
improved the performance of the prediction over the test set to a relative
error of 7.3% and a Pearson correlation coefficient of 0.92.
