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 t o 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 attribut es 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 fo recasts. 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 logisti cs, 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.