基于休伯支向量回归的非线性条件异构时间序列的强有力的控制图.
Chang Kyeom Kim1, Min Hyeok Yoon1, Sangyeol Lee1
1Department of Statistics, Seoul National University, Seoul, South Korea.
PloS one
|February 23, 2024
概括
这项研究引入了一个新的时间序列控制图,集成Huber支向量回归 (HSVR) 和一类分类 (OCC). 这种方法可对复杂而杂的数据进行可靠的监控,提高了财务时间序列分析的准确性.
科学领域:
- 统计 统计 统计 统计
- 时间序列分析时间序列分析
- 机器学习 机器学习
背景情况:
- 有条件的异种时间序列对传统的监测方法提出了挑战.
- 现有的控制图表往往需要复杂的残留修改.
- 对于准确的时间序列分析来说,对条件波动的可靠估计至关重要.
研究的目的:
- 提出一个高级的控制图表,用于监测条件异时间序列.
- 为了加强监控,将Huber支持向量回归 (HSVR) 与一个类分类 (OCC) 集成在一起.
- 开发一种可靠的方法来估计复杂时间序列中的条件波动性.
主要方法:
- 开发HSVR-GARCH模型,以纳入非线性,并对有条件波动性进行可靠估计.
- 基于一个类别分类 (OCC) 的控制图的构建,使用平方余量.
- 应用蒙特卡洛模拟来评估控制图的性能.
- 在现实世界的金融数据中使用引导式方法来构建控制图表.
主要成果:
- HSVR-GARCH模型提供了可靠的条件波动性估计,特别是在复杂的,杂的时间序列中.
- 基于OCC的控制图使用平方余量消除了后续余量修改的需要.
- 蒙特卡洛模拟表明,对于复杂和杂的模型,拟议的方法具有显著的好处.
- 对纳斯达克和KOSPI指数的真实数据分析验证了引导方法的有效性.
结论:
- 拟议的HSVR-GARCH集成OCC控制图为监测条件异种时间序列提供了一个强大的和有效的解决方案.
- 这种方法对于杂音污染的复杂时间序列数据特别有利.
- 该研究证实了启动式方法在金融时间序列控制图表中的实际适用性和有效性.
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