2026-04-17T06:04:02Zhttps://recercat.cat/oai/request

oai:recercat.cat:2445/1058292025-12-05T05:47:47Zcom_2072_1057col_2072_478808col_2072_478917

00925njm 22002777a 4500 dc Clavería González, Óscar author Monte Moreno, Enric author Torra Porras, Salvador author 2017-01-19T10:05:25Z 2017-01-19T10:05:25Z 2016 2017-01-19T10:05:25Z This study assesses the influence of the forecast horizon on the forecasting performance of several machine learning (ML) techniques. We compare the forecast accuracy of Support Vector Regression (SVR) to Neural Network (NN) models, using a linear model as a baseline. 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 ML methods improve their forecasting accuracy with respect to linear models as forecast horizons increase. This results shows the suitability of SVR for medium and long term forecasting. Previsió econòmica Turisme Desenvolupament econòmic Xarxes neuronals (Informàtica) Economic forecasting Tourism Economic development Neural networks (Computer science) Modelling tourism demand to Spain with machine learning techniques. The impact of forecast horizon on model selection