Detecting causality from nonlinear dynamics with short-term time series.
Detecting causality from nonlinear dynamics with short-term time series.
复制标题
用短期时间序列检测非线性动力学的因果关系
DOI：
10.1038/srep07464
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发表时间：
2014-12-12
影响因子：
4.6
通讯作者：
Chen L
中科院分区：
文献类型：
--
作者：
Ma H;Aihara K;Chen L
从观测的时间序列数据中量化变量之间的因果关系在各个学科中都非常重要，但也是一项具有挑战性的任务，特别是当观测数据很短时。与传统的方法不同，我们发现它可以检测因果关系只有很短的时间序列数据，基于嵌入理论的非线性动力学的吸引子。具体来说，我们首先表明，测量两个观测变量之间的交叉映射的平滑度可以用来检测因果关系。然后，我们提供了一个非常有效的算法来计算评估的平滑度的交叉地图，或“交叉地图平滑度”（CMS），从而推断的因果关系，它可以达到很高的精度，即使在很短的时间序列数据。从各种基准和生物系统的真实的数据的数学模型的分析验证了我们的方法。
Quantifying causality between variables from observed time series data is of great importance in various disciplines but also a challenging task, especially when the observed data are short. Unlike the conventional methods, we find it possible to detect causality only with very short time series data, based on embedding theory of an attractor for nonlinear dynamics. Specifically, we first show that measuring the smoothness of a cross map between two observed variables can be used to detect a causal relation. Then, we provide a very effective algorithm to computationally evaluate the smoothness of the cross map, or “Cross Map Smoothness” (CMS) and thus to infer the causality, which can achieve high accuracy even with very short time series data. Analysis of both mathematical models from various benchmarks and real data from biological systems validates our method.