| Abstract: |
For modeling the number of visits in Stopi\'{c}a cave (Serbia) we consider the
classical Auto-regressive Integrated Moving Average (ARIMA) model, Machine
Learning (ML) method Support Vector Regression (SVR), and hybrid NeuralPropeth
method which combines classical and ML concepts. The most accurate predictions
were obtained with NeuralPropeth which includes the seasonal component and
growing trend of time-series. In addition, non-linearity is modeled by shallow
Neural Network (NN), and Google Trend is incorporated as an exogenous
variable. Modeling tourist demand represents great importance for management
structures and decision-makers due to its applicability in establishing
sustainable tourism utilization strategies in environmentally vulnerable
destinations such as caves. The data provided insights into the tourist demand
in Stopi\'{c}a cave and preliminary data for addressing the issues of carrying
capacity within the most visited cave in Serbia. |