Transfer Entropy: where Shannon meets Turing

04/19/2019

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by   David Sigtermans, et al.

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Transfer Entropy is capable of capturing non-linear source-destination relations between multivariate time-series. It is a measure of association between source data that are transformed into destination data via a set of linear transformations between their probability mass functions. The resulting tensor formalism is used to show that in specific cases, e.g. in the case the system consists of three stochastic processes, bivariate analysis suffices to distinguish true relations from false relations. This allows us to determine the causal structure as far as encoded in the probability mass functions of the data.