English(EN) Tourist arrival forecasting with game-theoretic social influence weighting and deep learning

新AI模型利用社会影响加权预测游客到达量

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-16 04:00

一项新的研究论文提出了一种混合方法来预测游客到达量，将互联网大数据与博弈论算法相结合，以加权社交媒体意见领袖。该方法使用经验小波变换来管理非平稳属性，并使用堆叠双向长短期记忆网络（通过贝叶斯方法优化）来生成预测。与传统方法相比，该方法在均方根误差方面有了显著降低，即使在波动条件下也表现出鲁棒性。 AI

影响这种新颖的方法可以通过提高需求预测的准确性来加强服务系统的规划和后勤。

排序理由研究论文发表在arXiv上，详细介绍了一种新颖的预测方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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Signal score: 17 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 研究论文发表在arXiv上，详细介绍了一种新颖的预测方法。[lever_c_demoted from research: ic=1 ai=1.0]
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完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Ali Nikseresht · 2026-10-16 04:00

基于博弈论社会影响加权与深度学习的游客到达预测

arXiv:2507.03411v2 Announce Type: replace Abstract: In the era of Industry 5.0, data-driven decision-making has become indispensable for planning in service systems such as tourism. This paper proposes a non-linear hybrid approach for forecasting tourist arrivals to Hong Kong fro…

本文如何被排名

报道来源 [1]

基于博弈论社会影响加权与深度学习的游客到达预测

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