改进了基于洛伦兹轨迹分解的因果推理的融合交叉映射方法
Zhuoma Sunu1, Jingru Ma1, Bingliang He1
1School of Mathematics and Computer Science Institute, Northwest Minzu University, Lanzhou, 730030, China.
Scientific reports
|November 3, 2025
概括
一种新的方法,局部动态行为一致的CCM (LdCCM),改善了因果推理. 与传统的融合交叉映射 (CCM) 不同,LdCCM可以准确地检测因果关系,即使在诸如大气数据等复杂系统中也是如此.
科学领域:
- 复杂系统分析 复杂系统分析
- 非线性动力学是一种非线性动力学.
- 因果推理的原因推理.
背景情况:
- 收交叉映射 (CCM) 用于在动态系统中检测因果关系.
- 传统的CCM努力检测因果关系,当重建的多元组不能完全捕捉系统动态时,就像洛伦兹方程所看到的那样.
研究的目的:
- 解决传统CCM在检测因果关系方面的局限性.
- 提出和验证一个改进的CCM算法,LdCCM,用于增强因果推理.
主要方法:
- 开发了局部动态行为一致的CCM (LdCCM) 算法.
- 专注于选择最近的邻居,以确保在重建的分流器上保持一致的局部动态行为.
- 将LdCCM应用于洛伦兹方程和大气观测数据.
主要成果:
- 在洛伦兹方程中,LdCCM成功地检测了变量X和Y对Z的因果影响,而传统的CCM没有做到这一点.
- LdCCM算法在识别因果强度方面表现显著提高.
- 在真实世界的大气观测数据上验证了LdCCM的有效性和可靠性.
结论:
- 通过确保局部动态的一致性,LdCCM克服了传统CCM的局限性.
- 改进的算法为复杂系统中的因果发现提供了更强大的工具.
- 对于分析气候科学等各个科学领域的因果关系,LdCCM显得有前途.
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