Graphical models for multivariate time series based on conditional mutual information
基于条件互信息的多维时间序列图模型

The information theory is introduced for graphical models of multivariate time series. A method for testing direct linearity between two components is proposed. A class of graphical models, called linear conditional mutual information graph, is defined. The vertex set denotes the components of the series and the edges denote the direct linear dependence structure of the components. The presence of the edges is tested by a statistics based on linear conditional mutual information. The permutation procedure is used to determine the significance of the test statistics. Finally, the method is applied to a real series, and the results show that the method can efficiently capture the direct linear dependence between the components.