使用混合人工智能模型进行适应性旅游预测:西安国际游客抵达的案例研究
Shuxin Zheng1, Zhongguo Zhang2
1School of Economics and Business, Changzhou Vocational Institute of Textile and Garment, Changzhou, China.
PeerJ. Computer science
|December 11, 2023
概括
准确的旅游需求预测至关重要. 一种新的混合灰色模型-长期短期记忆 (GM-LSTM) 模型有效地预测了利用有限数据的旅游业,优于传统方法.
科学领域:
- 旅游经济学 旅游经济学
- 人工智能的人工智能
- 时间序列分析时间序列分析
背景情况:
- 准确的旅游需求预测对于经济规划和政策制定至关重要.
- 传统的深度学习模型通常需要大量的数据集,这在旅游预测中很少.
- 现有的模型因样本规模不足而扎,这限制了它们的实际应用.
研究的目的:
- 提出一种新的混合模型 (GM-LSTM) 用于以小样本大小准确预测旅游需求.
- 利用灰色模型和长短期记忆 (LSTM) 网络的优势进行自我适应的预测.
- 通过使用历史的国际游客到达数据来验证模型的有效性.
主要方法:
- 一种混合灰色模型-长期短期记忆 (GM-LSTM) 模型被开发出来.
- 灰色模型 (GM) 捕捉了旅游需求的整体趋势.
- 一个具有滚动机制的长短期记忆 (LSTM) 网络模拟非线性残留.
主要成果:
- 该GM-LSTM模型在预测中国西安每年的国际游客到来时表现出了很高的准确性.
- 该模型实现了11.88%的平均绝对百分比误差 (MAPE).
- 拟议的混合模型的性能优于其他传统的时间序列预测模型.
结论:
- 该GM-LSTM模型为旅游需求预测提供了准确而高效的解决方案,特别是在有限的数据的情况下.
- 这种混合方法有效地结合了趋势分析和残余建模,以改善预测.
- 这些发现支持GM-LSTM模型对旅游从业者和政策制定者的实际应用.
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