In the digital era, social network service (SNS) significantly influences travel behavior. Understanding SNS spillover effects is crucial for accurate tourism demand prediction. This study introduces SNS co-occurrence similarity (SNS-COS) to capture the spillover effects and demonstrates a comprehensive tourism demand forecasting framework that combines econometric and AI models to handle spatial and temporal data. By classifying attractions into different ranks, it effectively captures varying spillover effects. Empirical verification shows significant improvements in forecasting accuracy, especially for low-ranked attractions, providing valuable insights for enhancing tourism demand forecasting and informed decision-making in the tourism industry.

中文翻译：

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旅游需求每日预测：社交网络服务共现相似性的 ST-LSTM 模型


在数字时代，社交网络服务 （SNS） 对旅行行为有重大影响。了解 SNS 溢出效应对于准确预测旅游需求至关重要。本研究引入了 SNS 共现相似性 （SNS-COS） 来捕捉溢出效应，并展示了一个全面的旅游需求预测框架，该框架结合了计量经济学和 AI 模型来处理空间和时间数据。通过将景点分类为不同的等级，它可以有效地捕捉不同的溢出效应。实证验证表明，预测准确性显著提高，尤其是对于排名较低的景点，为加强旅游业的旅游需求预测和明智的决策提供了有价值的见解。