通过机器学习对时间序列的功能分解和不可逆性的估计
Michele Vodret1, Cristiano Pacini1, Christian Bongiorno1
1CentraleSupélec, Université Paris-Saclay, Laboratoire de Mathématiques et Informatique pour la Complexité et les Systèmes, 91192 Gif-sur-Yvette, France.
Physical review. E
|February 7, 2025
概括
本研究提出了一种新的方法,用于估计复杂时间序列数据的不可逆性,使用梯度增强. 调查结果表明,金融市场的不可逆转性在不稳定的时期从短期转向长期模式.
科学领域:
- 复杂系统分析 复杂系统分析
- 时间序列预测时间序列预测
- 计算金融是指计算金融.
背景情况:
- 在多变量时间序列中估计不可逆性对于理解系统动态至关重要.
- 现有的方法可能缺乏灵活性或需要广泛的校准.
- 确定可变相互作用对不可逆转性的贡献是具有挑战性的.
研究的目的:
- 引入一种新的,无模型的方法来估计多变量时间序列中的不可逆性.
- 为了使可变相互作用对不同时间尺度的不可逆转性贡献的剖析.
- 将该方法应用于金融市场,并分析不可逆转模式的变化.
主要方法:
- 将时间序列不可逆性估计映射到二进制分类问题.
- 使用梯度提升作为非线性,多变量分类器.
- 一个三个阶段的管道:轨迹编码,马科维序列识别和基于分类器的不可逆性估计.
主要成果:
- 提出的方法提供了一个无模型,非线性和多变量分析,最小的校准.
- 该方法允许详细分析可变相互作用对不可逆转性的贡献.
- 金融市场分析显示,不可逆转性来源在稳定 (短期) 和不稳定 (长期) 期间之间发生了转变.
结论:
- 梯度增强方法提供了一个有效的工具来量化复杂系统中的不可逆性.
- 该方法对贡献进行剖析的能力提高了对系统动态的理解,特别是在金融领域.
- 了解这些不可逆转性转变对于金融市场稳定性分析至关重要.
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