Methods for quantifying the causal structure of bivariate time series
Methods for quantifying the causal structure of bivariate time series
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DOI：
10.1142/s0218127407017628
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发表时间：
2007-03-01
影响因子：
2.2
通讯作者：
Otsu, N.
中科院分区：
文献类型：
--
作者：
Lungarella, M.;Ishiguro, K.;Otsu, N.
在复杂系统的研究中，主要关注的问题之一是不同子系统之间的因果关系和耦合的检测和表征。这种依赖关系的性质通常不仅是非线性的，而且是不对称的，因此使得对称和线性方法的使用无效。此外，从现实世界系统中采样的信号是有噪声的和短的，对潜在耦合的估计提出了额外的约束。在本文中，我们比较了最近引入的六种方法，用于量化从具有复杂动态行为的系统中提取的二元时间序列的因果结构。我们讨论了在单向和双向耦合确定性混沌系统中检测非对称耦合和信息流的方法的有用性。
In the study of complex systems one of the major concerns is the detection and characterization of causal interdependencies and couplings between different subsystems. The nature of such dependencies is typically not only nonlinear but also asymmetric and thus makes the use of symmetric and linear methods ineffective. Moreover, signals sampled from real world systems are noisy and short, posing additional constraints on the estimation of the underlying couplings. In this article, we compare a set of six recently introduced methods for quantifying the causal structure of bivariate time series extracted from systems with complex dynamical behavior. We discuss the usefulness of the methods for detecting asymmetric couplings and directional flow of information in the context of uni- and bidirectionally coupled deterministic chaotic systems.