2026-04-13T13:14:03Zhttps://recercat.cat/oai/requestoai:recercat.cat:2445/1058292025-12-05T05:47:47Zcom_2072_1057col_2072_478808col_2072_478917
RECERCAT
author
Clavería González, Óscar
author
Monte Moreno, Enric
author
Torra Porras, Salvador
2017-01-19T10:05:25Z2017-01-19T10:05:25Z20162017-01-19T10:05:25Z
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.
(c) Clavería González, Óscar et al., 2016 info:eu-repo/semantics/openAccess
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
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion