Analyzing relationships among ARMA processes based on non-Gaussianity of external influences
Analyzing relationships among ARMA processes based on non-Gaussianity of external influences
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DOI：
10.1016/j.neucom.2011.02.008
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发表时间：
2011-06
期刊：
影响因子：
6
通讯作者：
Yoshinobu Kawahara;Shohei Shimizu;T. Washio
中科院分区：
文献类型：
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作者：
Yoshinobu Kawahara;Shohei Shimizu;T. Washio
数据生成系统中变量间关系的分析是机器学习中的重要问题之一。本文基于外部影响的非高斯性和自回归滑动平均(ARMA)模型，提出了一种估计数据生成过程中变量的图形表示的方法。该模型由两部分组成，即用于瞬时效应的经典结构方程模型和用于过程滞后效应的ARMA模型，并通过对残差过程的非高斯性分析来估计。与最近提出的基于非高斯性的Lingam分析方法一样，该方法的估计具有可辨识性和一致性。我们还讨论了我们的方法估计的结构与格兰杰因果关系的关系。最后，我们使用我们提出的方法对同时包含瞬时因果关系和Granger(时间)因果关系的数据进行了演示，其中演示的数据集涵盖了人工和真实的物理系统。
The analysis of a relationship among variables in data generating systems is one of the important problems in machine learning. In this paper, we propose an approach for estimating a graphical representation of variables in data generating processes, based on the non-Gaussianity of external influences and an autoregressive moving-average (ARMA) model. The presented model consists of two parts,i.e., a classical structural-equation model for instantaneous effects and an ARMA model for lagged effects in processes, and is estimated through the analysis using the non-Gaussianity on the residual processes. As well as the recently proposed non-Gaussianity based method named LiNGAM analysis, the estimation by the proposed method has identifiability and consistency. We also address the relation of the estimated structure by our method to the Granger causality. Finally, we demonstrate analyses on the data containing both of the instantaneous causality and the Granger (temporal) causality by using our proposed method where the datasets for the demonstration cover both artificial and real physical systems.