A Causality Network Construction Method for High Dimensional Time Series Big Data

How to infer the causal relationship between variables in high-dimensional time series data and construct the causal network have always been a difficulty of research. The two major challenges are dimension reduction and causal inference. This paper proposes a causality network construction method for high-dimensional sequential big data. In this method, the dimensionality of feature variables is reduced by fuzzy C-means clustering twice and feature variables with similar and specific patterns are screened out. Then transfer entropy is used to infer causal relationships among variables and construct the time-series data network of causal relationships among variables. The proposed method was applied to a high-dimensional time-series data set which contains 32884 rats' gene expression values at 480 time points to infer the causal regulatory relationships among the genes, and efficiently construct the regulatory network of rats' rhythm genes.