Causality Analysis with Different Probabilistic Distributions Using Transfer Entropy

Abstract

This paper presents the results of an analysis of causality detection in a multi-loop control system. The investigation focuses on application of the Transfer Entropy method, which is not commonly used during the exact construction of information and material flow pathways in the field of automation. Calculations are performed on simulated multi-loop control system data obtained from a system with a structure known a priori. The model incorporates the possibility of freely changing its parameters and of applying noise with different properties. In addition, a method for determining the entropy transfer between process variables is investigated. The fitting of different variants of the probability distribution functions to the data is crucial for effective evaluation of the Transfer Entropy approach. The obtained results allow for suggestions to be formulated with respect to choosing which probability function the transfer entropy should be based upon. Moreover, we provide a proposal for the design of a causality analysis approach that can reliably obtain information relationships.