The appraisal of machine learning techniques for tourism demand forecasting
Document typePart of book or chapter of book
PublisherNova Science Publishers, Inc.
Rights accessRestricted access - publisher's policy
Machine learning (ML) methods are being increasingly used with forecasting purposes. This study assesses the predictive performance of several ML models in a multiple-input multiple-output (MIMO) setting that allows incorporating the cross-correlations between the inputs. We compare the forecast accuracy of a Gaussian process regression (GPR) model to that of different neural network architectures in a multi-step-ahead time series prediction experiment. 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.
CitationClaveria, O., Monte, E., Torra Porras, S. The appraisal of machine learning techniques for tourism demand forecasting. A: "Machine learning: advances in research and applications". 400 Oser Ave Suite 1600 Hauppauge NY 11788-3619: Nova Science Publishers, Inc., 2017, p. 59-90.