通过估计单变量和多变量时间序列之间的转移来确定随机动态系统中的关键驱动因素
1Soongsil University, Department of Bioinformatics and Life Science, 06978 Seoul, Korea.
Physical review. E
|March 19, 2025
概括
本研究介绍了输出转移 (OutTE) 和输入转移 (InTE),以测量变量对系统或来自系统的因果影响. 一种新的估计方法提高了复杂系统的准确性,成功识别了口腔微生物群中的关键驱动因素.
科学领域:
- 复杂系统分析 复杂系统分析
- 信息理论 信息理论
- 因果推理因果推理
背景情况:
- 转移 (TE) 传统上量化了动态系统中的双对因果关系.
- 识别关键驱动因素需要评估一个组件对整个系统的影响,而不仅仅是对对相互作用.
研究的目的:
- 引入输出转移 (OutTE) 和输入转移 (InTE) 来量化整个系统的因果影响.
- 为OutTE和InTE开发一种新的估计方案,以提高复杂的高维系统的精度.
主要方法:
- 定义OutTE是从一个变量转移到系统中的所有其他变量的.
- 定义InTE是从所有其他变量转移到特定组件的.
- 开发了一个估计方案,重点关注显著交互的合作伙伴,以减少高维数据中的错误.
主要成果:
- 拟议的OutTE和InTE方法准确量化了全系统的因果影响.
- 与天真方法相比,新型估计方案显著减少了错误,特别是在有限的样本中.
- 在口腔微生物群数据集中成功识别出已知的关键细菌物种,验证了该方法.
结论:
- OutTE和InTE为理解复杂动态系统中的因果驱动因素提供了有价值的指标.
- 这种新的估计方法提高了TE用于分析大型互连系统的适用性.
- 这种方法有可能在各种领域发现因果关系,包括微生物生态学.
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