旅游需求预测的时间序列变压器
Siyuan Yi1, Xing Chen2, Chuanming Tang3
1Chengdu University of Technology, Chengdu, 610059, Sichuan, China.
Scientific reports
|August 12, 2025
概括
本研究介绍了一个时间序列转换器 (Tsformer) 用于旅游需求预测,提高准确性和可解释性. 该Tsformer有效地捕捉了长期和短期的依赖关系,优于现有的AI方法.
科学领域:
- 人工智能的人工智能
- 时间序列分析时间序列分析
- 旅游管理 旅游管理
背景情况:
- 人工智能方法在旅游需求预测中普遍存在.
- 现有的人工智能模型在长期依赖性和缺乏解释性方面扎.
- 准确的预测对旅游业至关重要.
研究的目的:
- 为旅游需求预测提出一种基于人工智能的新型模型.
- 解决捕捉长期依赖和当前方法的解释性方面的局限性.
- 为了提高短期和长期旅游需求预测的准确性.
主要方法:
- 时间序列变压器 (Tsformer) 模型的开发.
- 在Tsformer中使用编码器-解码器架构.
- 整合日历功能,在预测时间范围内合并数据点.
主要成果:
- 在九种基线方法中,Tsformer表现出优越的性能.
- 该模型在短期和长期预测中实现了高准确性.
- 随着日历功能的加入,预测性能得到了改善.
- 该模型在COVID-19爆发之前和之后都显示出有效性.
结论:
- 拟议的Tsformer为旅游需求预测提供了一个更准确,更易于解释的替代方案.
- 编码-解码架构有效地捕获时间依赖.
- 整合日历信息可以提高预测能力.
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