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“Regional tourism demand forecasting with machine learning models: Gaussian process regression vs. neural network models in a multiple-input multiple-output setting"

Author

Listed:
  • Oscar Claveria

    (AQR Research Group-IREA. University of Barcelona. Av.Diagonal 696; 08034 Barcelona, Spain.)

  • Enric Monte

    (Department of Signal Theory and Communications, Polytechnic University of Catalunya (UPC).)

  • Salvador Torra

    (Riskcenter-IREA, University of Barcelona, Av. Diagonal 690, 08034 Barcelona, Spain.)

Abstract
This study presents a multiple-input multiple-output (MIMO) approach for multi-step-ahead time series prediction with a Gaussian process regression (GPR) model. We assess the forecasting performance of the GPR model with respect to several neural network architectures. The MIMO setting allows modelling the cross-correlations between all regions simultaneously. We find that the radial basis function (RBF) network outperforms the GPR model, especially for long-term forecast horizons. As the memory of the models increases, the forecasting performance of the GPR improves, suggesting the convenience of designing a model selection criteria in order to estimate the optimal number of lags used for concatenation.

Suggested Citation

  • Oscar Claveria & Enric Monte & Salvador Torra, 2017. "“Regional tourism demand forecasting with machine learning models: Gaussian process regression vs. neural network models in a multiple-input multiple-output setting"," IREA Working Papers 201701, University of Barcelona, Research Institute of Applied Economics, revised Jan 2017.
    • Handle: RePEc:ira:wpaper:201701
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    References listed on IDEAS

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    Keywords

    Regional forecasting; tourism demand; multiple-input multiple-output (MIMO); Gaussian process regression; neural networks; machine learning. JEL classification:;
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