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Modelling tourism demand to Spain with machine learning techniques. The impact of forecast horizon on model selection

Author
Claveria, O.; Torra Porras, Salvador; Monte, E.
Type of activity
Journal article
Journal
Revista de economía aplicada
Date of publication
2016-12-01
Volume
24
Number
72
First page
109
Last page
132
Repository
http://hdl.handle.net/2117/100218 Open in new window
URL
http://www.revecap.com/revista/ Open in new window
Abstract
This study assesses the influence of the forecast horizon on the forecasting performance of several machine learning techniques. We compare the fo recastaccuracy 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 m...
Citation
Claveria, O., Torra Porras, S., Monte, E. Modelling tourism demand to Spain with machine learning techniques. The impact of forecast horizon on model selection. "Revista de economía aplicada", 1 Desembre 2016, vol. 24, núm. 72, p. 109-132.
Keywords
Forecasting, Machine learning, Neural networks, Spain, Support vector regression, Tourism demand
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
TALP - Centre for Language and Speech Technologies and Applications
VEU - Speech Processing Group

Participants

  • Claveria González, Oscar  (author)
  • Torra Porras, Salvador  (author)
  • Monte Moreno, Enrique  (author)