在丹麦旅游业中采用的通用线性回归模型
Hongxuan Yan1, Xingyu Yan2,3, Luoyi Sun2
1School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, China.
PloS one
|August 22, 2025
概括
这项研究使用先进的统计模型揭示了旅游数据的季节性模式. 一般线性回归GARMA (GLRGARMA) 模型最好地捕捉长期记忆特征,以改善旅游预测.
科学领域:
- 时间序列分析
- 旅游经济学
- 统计模型
背景情况:
- 旅游数据经常表现出复杂的季节性和长期记忆效应.
- 了解这些模式对于准确的预测和资源管理至关重要.
- 现有的模型可能无法完全捕捉旅游业的细微时间动态.
研究的目的:
- 调查旅游时间序列数据中的季节性特征.
- 提出和评估用于捕获长期记忆和季节性特征的先进统计模型.
- 确定旅游数据分析和预测的最佳模式.
主要方法:
- 分析丹麦旅游数据,重点是酒店房间租.
- 使用自相关函数 (ACF) 和周期图来识别长期记忆.
- 开发和比较通用线性回归GARMA (GLRGARMA) 和SARMA (GLRSARMA) 模型.
- 纳入通用Poisson (GP) 分布以提高模型的灵活性.
- 采用贝叶斯方法进行样本内和样本外预测.
主要成果:
- 在旅游数据中清楚地发现了Gegenbauer的长期记忆和季节性特征.
- GLRGARMA模型在处理Gegenbauer长内存的时间序列方面表现出卓越的性能.
- 包含具有周期性海绵效应的解释变量显著改善了模型性能.
- 模型选择标准证实了GLRGARMA模型的优势.
结论:
- 在分析旅游时间序列数据时,GLRGARMA模型具有很高的效率.
- 准确的季节性模型和长期记忆对于稳健的旅游预测至关重要.
- 具有周期性影响的解释变量可以大大提高旅游模型的预测能力.
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