在对称的动态系统中因果发现与收交叉映射
Yiting Duan1, Yi Guo1, Jack Yang2
1School of Computer, Data and Mathematical Sciences, Western Sydney University, Parramatta, NSW 2150, Australia.
融合交叉映射可以错误地识别时间序列数据中的因果关系,这是由于对称的混乱吸引力. 一种新的k-means集群方法纠正了这些错误,准确地揭示了双向因果关系.
科学领域:
- 复杂的系统复杂的系统.
- 时间序列分析时间序列分析
- 非线性动力学是一种非线性动力学.
背景情况:
- 收交叉映射 (CCM) 是一种在时间序列中推断因果关系的方法.
- 对称的混乱吸引子可以导致CCM错误地识别单向因果关系.
研究的目的:
- 解决CCM在检测混乱吸引器表现出对称性时的双向因果关系方面的局限性.
- 提出和验证一种新的方法,以在这种情况下准确地推断因果关系.
主要方法:
- 提出了一种基于k-means集群的新方法.
- 该方法旨在恢复潜伏的混乱吸引力的对称性.
- 它在没有外部变量信息的情况下发现因果关系.
主要成果:
- 提出的方法成功地恢复了潜在的混乱吸引力对称性.
- 确定了准确的双向因果关系,纠正了CCM错误.
- 验证是在模拟和真实世界时间序列数据上进行的.
结论:
- 新的基于k-means集群的方法有效地克服了由对称混乱吸引器引起的CCM限制.
- 这种方法提高了从时间序列数据的因果发现的可靠性.
- 它为识别复杂系统中真正的因果关系提供了一个强大的解决方案.
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