Please use this identifier to cite or link to this item:
Title: Modelling tourism demand to Spain with machine learning techniques. The impact of forecast horizon on model selection
Author: Clavería González, Óscar
Monte Moreno, Enric
Torra Porras, Salvador
Keywords: Previsió econòmica
Desenvolupament econòmic
Xarxes neuronals (Informàtica)
Economic forecasting
Economic development
Neural networks (Computer science)
Issue Date: 2016
Publisher: Universidad de Zaragoza
Abstract: 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.
Note: Reproducció del document publicat a:
It is part of: Revista de Economia Aplicada, 2016, vol. XXIV, num. 72, p. 109-132
ISSN: 1133-455X
Appears in Collections:Articles publicats en revistes (Econometria, Estadística i Economia Aplicada)

Files in This Item:
File Description SizeFormat 
659290.pdf378.66 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.