Reconstructing complex network for characterizing the time-varying causality evolution behavior of multivariate time series.

ABSTRACT: In order to explore the characteristics of the evolution behavior of the time-varying relationships between multivariate time series, this paper proposes an algorithm to transfer this evolution process to a complex network. We take the causality patterns as nodes and the succeeding sequence relations between patterns as edges. We used four time series as sample data. The results of the analysis reveal some statistical evidences that the causalities between time series is in a dynamic process. It implicates that stationary long-term causalities are not suitable for some special situations. Some short-term causalities that our model recognized can be referenced to the dynamic adjustment of the decisions. The results also show that weighted degree of the nodes obeys power law distribution. Th

SUBMITTER: Jiang M 

PROVIDER: S-EPMC5585247 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature