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Article · TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES · January 1, 2016

Tourism demand modelling and forecasting using data mining techniques in multivariate time series: a case study in Turkey

Selcuk Cankurt, Abdülhamit Subaşı

Selcuk Cankurt
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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.

Forecasting Techniques and ApplicationsStock Market Forecasting MethodsTime Series Analysis and Forecastingtourism demand forecastingmultivariate time series forecastingsupport vector regression

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