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Title: Combination forecasts of tourism demand with machine learning models
Author: Clavería González, Óscar
Monte Moreno, Enric
Torra Porras, Salvador
Keywords: Previsió
Xarxes neuronals (Informàtica)
Distribució de Gauss
Anàlisi de regressió
Aprenentatge automàtic
Neural networks (Computer science)
Gaussian distribution
Regression analysis
Machine learning
Issue Date: 2016
Publisher: Taylor and Francis
Abstract: The main objective of this study is to analyse whether the combination of regional predictions generated with machine learning (ML) models leads to improved forecast accuracy. With this aim we construct one set of forecasts by estimating models on the aggregate series, another set by using the same models to forecast the individual series prior to aggregation, and then we compare the accuracy of both approaches. We use three ML techniques: Support Vector Regression (SVR), Gaussian Process Regression (GPR) and Neural Network (NN) models. We use an ARMA model as a benchmark. We find that ML methods improve their forecasting performance with respect to the benchmark as forecast horizons increase, suggesting the suitability of these techniques for mid- and long-term forecasting. In spite of the fact that the disaggregated approach yields more accurate predictions, the improvement over the benchmark occurs for shorter forecast horizons with the direct approach.
Note: Versió postprint del document publicat a:
It is part of: Applied Economics Letters, 2016, vol. 23, num. 6, p. 428-431
Related resource:
ISSN: 1350-4851
Appears in Collections:Articles publicats en revistes (Econometria, Estadística i Economia Aplicada)

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