来自混合嵌入 (PMIME) 的部分相互信息的因果关系测量重新审视
Akylas Fotiadis1, Ioannis Vlachos1,2,3, Dimitris Kugiumtzis1
1Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece.
Chaos (Woodbury, N.Y.)
|March 6, 2024
概括
这项研究增强了混合嵌入 (PMIME) 措施的部分相互信息,以提高在复杂系统中识别因果关系的准确性. 修改减少了假阳性,并使协同相互作用的检测成为可能.
科学领域:
- 信息理论是信息理论.
- 复杂系统分析 复杂系统分析
- 时间序列分析时间序列分析.
背景情况:
- 混合嵌入的部分相互信息 (PMIME) 是一种基于信息理论的测量方法,用于识别高维复杂系统中的因果关系.
- 原来的PMIME方法虽然对非线性相互作用有效,但在随机系统中存在错误的阳性检测,无法识别纯粹的协同关系.
研究的目的:
- 解决原始PMIME措施的局限性,特别是错误检测和无法捕捉协同关系.
- 提高PMIME算法的准确性和适用于更广泛的复杂系统,包括具有强烈随机性和协同效应的系统.
主要方法:
- 引入了改进的重新抽样显著性测试和向后修订程序,以减轻错误阳性检测.
- 通过结合检查候选驱动器对的方法来增强PMIME,从而能够检测出协同关系.
- 在具有不同尺寸 (3-30),随机性和协同效应的设计系统上进行了系统模拟.
主要成果:
- 修改后的PMIME通过减少错误检测和成功识别协同相互作用,显著提高了性能.
- 改进的算法在具有强大的随机性特征的复杂系统中表现出更好的准确性.
- 为优化实际应用的精度和计算效率之间的平衡提供了指导方针.
结论:
- 这些修改大大提高了PMIME算法的可靠性和分析复杂动态系统中因果关系的范围.
- 增强的PMIME是调查因果关系的更强有力的工具,特别是在金融市场和其他现实世界的应用中.
- 这项研究为应用高级因果关系测量方法对高维时间序列数据提供了实用见解.
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