Modelling tourism demand to Spain with machine learning techniques. The impact of forecast horizon on model selection
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hdl:2117/100218
Tipus de documentArticle
Data publicació2016-12-01
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
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 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.
CitacióClaveria, 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.
ISSN1413-8050
Versió de l'editorhttp://www.revecap.com/revista/
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