A study for multiscale information transfer measures based on conditional mutual information.

ABSTRACT: As the big data science develops, efficient methods are demanded for various data analysis. Granger causality provides the prime model for quantifying causal interactions. However, this theoretic model does not meet the requirement for real-world data analysis, because real-world time series are diverse whose models are usually unknown. Therefore, model-free measures such as information transfer measures are strongly desired. Here, we propose the multi-scale extension of conditional mutual information measures using MORLET wavelet, which are named the WM and WPM. The proposed measures are computational efficient and interpret information transfer by multi-scales. We use both synthetic data and real-world examples to demonstrate the efficiency of the new methods. The results of the new meth

SUBMITTER: Wan X 

PROVIDER: S-EPMC6283631 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature