Abstract
Transfer Entropy (TE) is one of the most commonly used methods to detect the causal relationship between a pair of time series. However, the computational complexity of the TE is very hign, because its calculation needs to estimate the probability distribution of the variables. In order to solve this problem, we propose a new version of the TE based on the concept of Matrix Entropy (MT), called Matrix Transfer Entropy (MTE). MTE can be used for two variables with linear or non-linear causal relationships. Compared with the traditional TE, the new approach can achieve more robust results. Bypassing the estimation of the probability density functions (PDFs) of the variables, the computational complexity of the MTE is not high. Experimental results on two toy examples are provided to demonstrate the performance of the MTE. Additionally, the new method is applied to a real clinical dataset to analyze the cardiorespiratory causality.
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