Abstract:In this study multiple linear regression, multilayer perceptron (MLP) regression, and support vector regression (SVR) are used to make multivariate tourism forecasting for Turkey. This paper is a comparative study of data mining techniques based on multivariate regression modelling with monthly data points to forecast tourism demand; it focuses on Turkey. Both MLP and SVR methods are widely employed in the variety forecasting problems. Most of the previous research on tourism forecasting used univariate time series or a limited number of variables with mostly yearly or quarterly, and rarely monthly frequencies. However, the application of data mining techniques for multivariate forecasting in the context of tourism demand has not been widely explored. This paper differs from earlier research in two ways: 1) it proposes multivariate regression modelling with monthly data points to forecast tourism demand; and 2) it focuses on Turkey by using a dataset with the most recently accumulated (between January 1996 and Dec 2013) 67 time series with respect to Turkey and its top 26 major tourism clients. Comparison of forecasting performances in terms of relative absolute error (RAE) and root relative squared error (RRSE) measurements shows that the SVR model, with RAE = 12.34% and RRSE = 14.02%, gives a better performance. The results obtained in this study provide information for researchers interested in applying data mining techniques to tourism demand forecasting and help policy makers, government bodies, investors, and managers for their regularization, planning, and investments by way of accurate tourism demand forecasting.

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Over the last decades, several soft computing techniques have been applied to tourism demand forecasting. Among these techniques, a neuro-fuzzy model of ANFIS (adaptive neuro-fuzzy inference system) has started to emerge. A conventional ANFIS model cannot deal with the large dimension of a dataset, and cannot work with our dataset, which is composed of a 62 time-series, as well. This study attempts to develop an ensemble model by incorporating neural networks with ANFIS to deal with a large number of input variables for multivariate forecasting. Our proposed approach is a collaboration of two base learners, which are types of the neural network models and a meta-learner of ANFIS in the framework of the stacking ensemble. The results show that the stacking ensemble of ANFIS (meta-learner) and ANN models (base learners) outperforms its stand-alone counterparts of base learners. Numerical results indicate that the proposed ensemble model achieved a MAPE of 7.26% compared to its single-instance ANN models with MAPEs of 8.50 and 9.18%, respectively. Finally, this study which is a novel application of the ensemble systems in the context of tourism demand forecasting has shown better results compared to those of the single expert systems based on the artificial neural networks.

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