基于波波变换的决策树的时空预测,适用于空气质量和Covid-19预测.
Xin Zhao1,2, Stuart Barber2, Charles C Taylor2
1School of Mathematics, Southeast University, Nanjing, People's Republic of China.
Journal of applied statistics
|June 28, 2023
概括
本研究引入了一种新的决策树和波形变换方法,用于预测具有空间效应的时间序列. 这种方法提高了预测的准确性,并提供了对时间序列机制的明确见解.
科学领域:
- 数据科学数据科学数据科学
- 环境科学 环境科学
- 流行病学 流行病学
背景情况:
- 时间序列预测经常与空间溢出效应作斗争.
- 决策树提供可解释性,但在处理复杂的时间和空间依赖性时可能受到限制.
- 波形变换可以以各种分辨率表示数据,从而有可能增强特征提取.
研究的目的:
- 为时间序列预测开发混合决策树和波形变换方法.
- 为了提高时间序列的预测准确性和可解释性,具有空间溢出效应.
- 在模拟,空气污染和COVID-19数据上应用和验证该方法.
主要方法:
- 一个决策树模型与波量变换集成用于特征提取.
- 哈尔,LA8,D4和D6波段用于时间序列分解的应用.
- 构建空间权重以模拟连接地区的溢出效应.
主要成果:
- 哈尔波段在模拟中表现出卓越的性能.
- 混合模型成功地确定了空气质量指数数据中的自回归性,季节性和空间溢出效应.
- 与原始数据相比,波形转换变量带来了更好的预测性能和可解释性.
结论:
- 开发的方法有效地预测时间序列数据与空间溢出效应.
- 波形变换提高了复杂时间序列的决策树性能和可解释性.
- 对COVID-19数据的分析表明,封锁政策是有效的,正如空间加权变量的非选择所表明的那样.
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