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A Hybrid Game-Theory and Deep Learning Framework for Predicting Tourist Arrivals via Big Data Analytics and Opinion Leader Detection

In the era of Industry 5.0, data-driven decision-making has become indispensable for optimizing systems across Industrial Engineering. This paper addresses the value of big data analytics by proposing a novel non-linear hybrid approach for forecasting international tourist arrivals in two different contexts: (i) arrivals to Hong Kong from five major source nations (pre-COVID-19), and (ii) arrivals to Sanya in Hainan province, China (post-COVID-19). The method integrates multiple sources of Internet big data and employs an innovative game theory-based algorithm to identify opinion leaders on social media platforms. Subsequently, nonstationary attributes in tourism demand data are managed through Empirical Wavelet Transform (EWT), ensuring refined time-frequency analysis. Finally, a memory-aware Stacked Bi-directional Long Short-Term Memory (Stacked BiLSTM) network is used to generate accurate demand forecasts. Experimental results demonstrate that this approach outperforms existing state-of-the-art techniques and remains robust under dynamic and volatile conditions, highlighting its applicability to broader Industrial Engineering domains, such as logistics, supply chain management, and production planning, where forecasting and resource allocation are key challenges. By merging advanced Deep Learning (DL), time-frequency analysis, and social media insights, the proposed framework showcases how large-scale data can elevate the quality and efficiency of decision-making processes.

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