论文标题
在多元时间序列中检测直接因果关系：比较研究
Detecting direct causality in multivariate time series: A comparative study
论文作者
论文摘要
Granger因果关系的概念越来越多地应用于不同应用中定向相互作用的表征。为了说明多元时间序列中的所有可用信息，至关重要的是用于估计Granger因果关系的多元框架。但是，包含非信息或不重要的变量会产生与“维度诅咒”有关的估计问题。为了解决这个问题，已经引入了使用可变选择和降低技术的直接因果关系措施。在这项比较工作中，评估了时间域中双变量和多元因果关系措施的表现，重点是减少维度因果关系措施。特别是，使用了不同类型的高维耦合离散系统（最多涉及100个变量），并检查了因果关系度量与时间序列长度和不同噪声类型的鲁棒性。仿真研究的结果突出了降低措施的优越性，尤其是对于高维系统。
The concept of Granger causality is increasingly being applied for the characterization of directional interactions in different applications. A multivariate framework for estimating Granger causality is essential in order to account for all the available information from multivariate time series. However, the inclusion of non-informative or non-significant variables creates estimation problems related to the 'curse of dimensionality'. To deal with this issue, direct causality measures using variable selection and dimension reduction techniques have been introduced. In this comparative work, the performance of an ensemble of bivariate and multivariate causality measures in the time domain is assessed, focusing on dimension reduction causality measures. In particular, different types of high-dimensional coupled discrete systems are used (involving up to 100 variables) and the robustness of the causality measures to time series length and different noise types is examined. The results of the simulation study highlight the superiority of the dimension reduction measures, especially for high-dimensional systems.
