Forecasting the Tourism Demand of Türkiye Using Artificial Neural Network (ANN) Approach

Abstract

Modelling and forecasting the tourism demand is an important research area for both public and private sectors for planning the travel and accommodation facilities in advance. Therefore, modelling the tourism demand in terms of the number of tourists is an active research area for both tourism, economics and financial studies. In this work, the tourism demand of Türkiye is modelled using artificial neural network methods. The tourism demand represented by the number of tourists is taken as the dependent variable while the past values of the number of tourists is considered as the inputs. Furthermore, the tourism revenue is also modelled employing the same autoregressive artificial neural network approach. Monthly data in the period of 2008-2022 which are taken from the official sources are used as the research material. The multilayer perceptron type artificial neural networks with nonlinear neural activation functions is selected for the accurate modelling of the highly seasonal tourism demand data. The modelling and forecasting results show that the developed artificial neural network reaches high accuracy for modelling both the tourism demand and the tourism revenue such that the coefficient of determination of the developed artificial neural network models have the values of R2=0.949 and R2=0.840 for the tourism demand and tourism revenue, respectively, providing an objective assessment of the accuracy of the developed models. It is concluded that the multilayer perceptron based artificial neural network methods can be used to model the tourism demand therefore this type of modelling is expected to be helpful for tourism planners by providing them the tourism demand forecast in advance.