Modelling and Forecasting Inbound Tourism Demand to Croatia using Artificial Neural Networks: A Comparative Study

Abstract

Tourism demand is the basis on which all commercial decisions concerning tourism ultimately depend. Accurate estimation of tourism demand is essential for the tourism industry because it can help reduce risk and uncertainty as well as effectively provide basic information for better tourism planning. The purpose of this study is to develop the optimal forecasting model that yields the highest accuracy when compared to the forecast performances of three different methods, namely Artificial Neural Network (ANN), Exponential Smoothing, and Box-Jenkins methods for forecasting monthly inbound tourist flows to Croatia. Prior studies have been applied to forecast tourism demand to Croatia based on time series models and casual methods. However, the monthly and comparative tourism demand forecasting studies using ANNs are still limited, and this paper aims to fill this gap. The number of monthly foreign tourist arrivals to Croatia covers the period between January 2005-December 2019 data were used to build optimal forecasting models. Forecasting performances of the models were measured by Mean Absolute Percentage Error (MAPE) statistics. As a result of the experiments carried out, when compared to the forecasting performances of various models, 12 lagged ANN models, which have [4-3-1] architecture, were seen to perform best among all models applied in this study. Considering both the empirical findings obtained from this study and previous studies on tourism forecasting, it can be seen that ANN models that do not have any negativities (such as over-training, faulty architecture, etc.) produce successful forecasting results when compared with results generated by conventional statistical methods.

A review of demand forecasting models and methodological developments within tourism and passenger transportation industry

“…food, souvenirs). It must be considered that tourism is a perishable product, and not selling it at the right time will lead to lost sales for all related businesses (Cuhadar et al, 2014). The tourism characteristics that are usually forecasted in most studies on the subject are: the number of trips from the origin country to the tourism destination; the amount of money that is spent by the whole tourists in a year or the average amount of money that each tourist spent in the destination region; and the number of nights that each tourist stays at the destination (Fernandes and Teixeira, 2008).…”

Tourism forecasting: A review of methodological developments over the last decade

“…In Chen et al (2012), before BPN was implemented, empirical mode decomposition (EMD) was used first time to decompose complicated raw data into a set of easier and cleaner intrinsic mode functions and a residue, which makes implementation of BPN more convenient. Multilayer perceptron (MLP) also gains popularity in NN, which can transform linearly combined input variables by non-linear activation functions within three or more nodes (Cang, 2011;Claveria et al, 2015;Cuhadar et al, 2014). Unlike MLP as a supervised learning model, radial basis function (RBF) network is another ANN method that combines supervised and unsupervised learning, with three layers such as input layer, hidden layer consisted of neurons computing a symmetric radial function and output layer consisted of neurons that linearly combine outputs from hidden layers (Claveria et al, 2015).…”

“…Unlike MLP as a supervised learning model, radial basis function (RBF) network is another ANN method that combines supervised and unsupervised learning, with three layers such as input layer, hidden layer consisted of neurons computing a symmetric radial function and output layer consisted of neurons that linearly combine outputs from hidden layers (Claveria et al, 2015). Applications included Claveria et al (2015) and Cuhadar et al (2014). Other ANN methods that have been used in tourism demand forecasting are generalized regression NNs (GRNNs) (Cuhadar et al, 2014) and Elman network (Claveria et al, 2015).…”

“…Applications included Claveria et al (2015) and Cuhadar et al (2014). Other ANN methods that have been used in tourism demand forecasting are generalized regression NNs (GRNNs) (Cuhadar et al, 2014) and Elman network (Claveria et al, 2015). Elman NN also contained three layers, but besides it contains a set of context units connected from hidden layers, which allows feedback on the internal activation of the network.…”

Future projection and the sales of industrial wood in Turkey: artificial neural networks

“…In recent years, ANNs have been widely used for making forecasts regarding the future and there are many studies in the literature about forecasting modeling with ANNs in various fields such as tourism (Palmer et al, 2006;Cuhadar et al, 2014), finance (Erilli et al, 2010), agriculture (Küçükönder, 2011;Güler et al, 2017), and energy (Tesha and Kichonge, 2015). Although studies on the forecasting of wood and wood products using ANNs are available (Kazemi et al, 2011;Anandhi et al, 2012;Tigas et al, 2013;Sivaram, 2014), there are not many studies that have been conducted in Turkey regarding the forecasting modeling of exports, imports, production, and sales of wood and wood products.…”