  This study assesses the influence of the forecast horizon on the forecasting
performance of several machine learning techniques. We compare the fo recast
accuracy of Support Vector Regression (SVR) to Neural Network (NN) models,
using a linear model as a benchmark. We focus on international tourism demand
to all seventeen regions of Spain. The SVR with a Gaussian radial basis
function kernel outperforms the rest of the models for the longest forecast
horizons. We also find that machine learning methods improve their forecasting
accuracy with respect to linear models as forecast horizons increase. This
result shows the suitability of SVR for medium and long term forecasting.
