Measuring information transfer by dispersion transfer entropy

Abstract

In this paper, we propose a novel technique, called dispersion transfer entropy(DTE), to determine the information transfer and causal relation in the analysis of complex systems. Symbolization is used to solve the computational burden and noise sensitivity. To deal with the two major issues in symbolization, generating partition and information loss, we use the Ragwitz criterion to dynamically select parameters and utilize dispersion pattern to keep influential information. Moreover, we extend DTE into the multivariate system and propose dispersion multivariate transfer entropy(DMTE), dispersion multivariate transfer entropy curve(DMTEC) and dispersion partial transfer entropy(DPTE). DMTE greatly weakens the influence of synchronicity and similarity in data on information transfer detection, which is a breakthrough in solving the limitation of transfer entropy. DMTEC shows the evolution of causal relation over time and DPTE measures the direct causal effect between systems. These statistics can be combined to obtain a more comprehensive and accurate measurement of causality for multivariable systems. Also, we apply these methods to simulation data as well as stock markets to verify the effectiveness of our methods.