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

Buda Bajić, Srđan Milićević, Aleksandar Antić, Slobodan Marković, Nemanja Tomić

arXiv 7 Apr 2024 · Econometrics · 1 citations (OpenAlex)

arXiv:2404.04974 · PDF · DOI · OpenAlex · Extracted main text

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.

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86
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Oskar Triebe, Hansika Hewamalage, Polina Pilyugina, Nikolay Laptev,… (2021) Neuralprophet: Explainable forecasting at scale0.73732100%
2Douglas Frechtling (2012) Forecasting tourism demand0.51121100%
3Dimitrios Buhalis (2000) Tourism and information technologies: Past, present and future0.51121100%
4V. Chiarini, Duckeck, J., and J. De Waele (2022) A global perspective on sustainable show cave tourism0.51121100%
5Corrina Cortes and Vladimir Vapnik (1995) Support-vector machine0.40511100%
6Hui Li and Kannan Srinivasan (2019) Competitive dynamics in the sharing economy: An analysis in the context of Airbnb and hotels0.40511100%
7Graziano Abrate, Juan Luis Nicolau, and Giampaolo Viglia (2019) The impact of dynamic price variability on revenue maximization0.40511100%
8Haiyan Song and Stephen F Witt (2012) Tourism demand modelling and forecasting0.40511100%
9J Christopher Holloway (2004) Marketing for tourism0.40511100%
10Marianna Sigala, Evangelos Christou, and Ulrike Gretzel (2012) Social media in travel, tourism and hospitality: Theory, practice and cases0.40511100%

Showing the top 10 of 86 scored citations.