论文标题
通过跨距离向量检测方向耦合检测
Directional coupling detection through cross-distance vectors
论文作者
论文摘要
从测量的复杂系统的测量时间序列中推断耦合方向是具有挑战性的。我们提出了一种新的基于状态空间的因果关系措施，该因素是从跨距离向量获得的，以量化相互作用强度。这是一种无模型的噪声方法，仅需要几个参数。该方法适用于双变量时间序列，并且对人工制品和缺失值有弹性。结果是两个耦合指数，它们比已经建立的状态空间度量更准确地量化了每个方向的耦合强度。我们在不同的动态系统上测试了提出的方法，并分析了数值稳定性。结果，提出了最佳参数选择的过程，从而规避确定最佳嵌入参数的挑战。我们表明，在较短的时间序列中，它对噪声和可靠是可靠的。此外，我们表明它可以检测到测量数据中的心肺相互作用。可以在https://repo.ijs.si/mbresar/cd-vec上获得数值高效的实现。
Inferring the coupling direction from measured time series of complex systems is challenging. We propose a new state space based causality measure obtained from cross-distance vectors for quantifying interaction strength. It is a model-free noise-robust approach that requires only a few parameters. The approach is applicable to bivariate time series and is resilient to artefacts and missing values. The result is two coupling indices that quantify coupling strength in each direction more accurately than the already established state space measures. We test the proposed method on different dynamical systems and analyse numerical stability. As a result, a procedure for optimal parameter selection is proposed, circumventing the challenge of determining the optimal embedding parameters. We show it is robust to noise and reliable in shorter time series. Moreover, we show that it can detect cardiorespiratory interaction in measured data. A numerically efficient implementation is available at https://repo.ijs.si/mbresar/cd-vec.
