动态时间增强学习和政策增强的LSTM用于预测酒店预订取消预测
Junhua Xiao1,2, Shahriman Zainal Abidin2, Verly Veto Vermol2
1Gongqing College of Nanchang University, Jiangxi, China.
PeerJ. Computer science
|February 3, 2025
概括
这项研究引入了一种新的深度强化学习模型,用于预测酒店预订取消. 先进的模型准确地预测取消,改善酒店管理和效率.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 全球旅游业的快速扩张需要高效的酒店预订管理.
- 传统的取消预测模型无法考虑季节性和事件等动态时间因素.
- 现有的方法与现实世界取消行为的动态性质作斗争.
研究的目的:
- 开发一种新的深度强化学习框架,用于准确预测酒店预订取消预测.
- 解决静态模型在捕捉时间动态和市场波动方面的局限性.
- 通过提高预测准确度,提高酒店服务效率和运营管理.
主要方法:
- 实施一个新的框架,将动态的时间强化学习与政策增强的长短期记忆 (LSTM) 结合起来.
- 利用多源信息来捕捉复杂的时间动态并提高预测稳定性.
- 使用深度强化学习技术,以适应性和准确地预测取消趋势.
主要成果:
- 拟议的模型实现了超过95.9%的预测准确度,显著超过传统方法.
- 证明了高模型稳定性 (0.98),F1评分接近1和相互信息评分约为0.93.
- 在各种数据源中验证了有效性和概括性,证实了可靠性.
结论:
- 深度强化学习为管理取消酒店预订提供了一种创新和高效的解决方案.
- 拟议的模型有效地处理具有动态时间影响的复杂预测任务.
- 这种方法增强了AI在优化酒店业运营效率方面的潜力.
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