Please use this identifier to cite or link to this item:
http://hdl.handle.net/2445/105829
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 Turisme Desenvolupament econòmic Xarxes neuronals (Informàtica) Economic forecasting Tourism 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: http://www.revecap.com/revista/numeros/72/72_inv06.html |
It is part of: | Revista de Economia Aplicada, 2016, vol. XXIV, num. 72, p. 109-132 |
URI: | http://hdl.handle.net/2445/105829 |
Related resource: | http://www.revecap.com/revista/numeros/72/72_inv06.html |
ISSN: | 1133-455X |
Appears in Collections: | Articles publicats en revistes (Econometria, Estadística i Economia Aplicada) |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
659290.pdf | 378.66 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.