准差异化及其对来自复杂系统的杂时间序列数据的应用
Siew Ann Cheong1, Zheng Tien Kang2, Peter Tsung-Wen Yen2
1Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, 637371, Singapore, Republic of Singapore. cheongsa@ntu.edu.sg.
Scientific reports
|November 7, 2025
概括
这项研究引入了准差异化,这是一种新的无模型方法,可以从杂的时间序列数据中提取稳定状态. 该技术成功地发现了市场崩,并提取了关键的金融数据特征.
科学领域:
- 复杂系统分析 复杂系统分析
- 时间序列分析时间序列分析.
- 金融计量经济学 金融计量经济学
背景情况:
- 分析具有多个稳定状态和状态依赖噪声的复杂系统是具有挑战性的.
- 识别稳定状态和过渡需要从噪音数据中构建缓慢变化的顺序参数.
研究的目的:
- 从杂的时间序列数据中提取缓慢变化的组件的无模型方法.
- 应用这种方法来识别市场崩和描述金融时间序列动态.
主要方法:
- 拟议的准差异化:一种使用在移动时间窗口中集成信息之间的差异的方法.
- 开发了集成的准差异化来近似时间序列的缓慢变化的部分.
- 适用于道斯工业平均线 (DJIA) 每日收益率 (2003-2023) 的方法.
主要成果:
- 在DJIA数据中成功识别了2008年10月的雷曼兄弟和2020年3月的COVID-19市场崩.
- 提取DJIA的缓慢变化的平均值和方差.
- 证明了估计赫斯特指数和线性交叉相关性的潜力.
结论:
- 准差异化和集成准差异化是分析杂时间序列的有效无模型工具.
- 这些方法对金融市场以外的各种复杂系统具有广泛的适用性.
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