Neural Network Modeling for Forecasting Tourism Demand in Stopića Cave: A Serbian Cave Tourism Study

2024/04/07 by Buda Bajić, Bajić, Buda, Srđan Milićević +7
Economics, Econometrics and Finance · #Artificial Intelligence (cs.AI) #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Regional Development and Management Studies

paper · pdf · doi:10.48550/arxiv.2404.04974

openalex publication_date 2024/04/07 · openalex created_date 2024/04/11 · openalex updated_date 2026/07/28

Abstract

For modeling the number of visits in Stopića 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 Stopića cave and preliminary data for addressing the issues of carrying capacity within the most visited cave in Serbia.

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