We present a new decomposition of transfer entropy to characterize the degree of synergy- and redundancy-dominated influence a time series has upon the interaction between other time series. We prove the existence of a class of time series, where the early past of the conditioning time series yields a synergistic effect upon the interaction, whereas the late past has a redundancy-dominated effect. In general, different parts of the past can have different effects. Our information theoretic quantities are easy to compute in practice, and we demonstrate their usage on real-world brain data.

翻译：我们提出了一种新的传递熵分解方法，用于表征时间序列对其他时间序列间交互作用的协同主导与冗余主导影响程度。我们证明了存在一类时间序列，其中条件时间序列的早期过去对交互作用产生协同效应，而晚期过去则产生冗余主导效应。一般而言，过去的不同部分可能产生不同影响。我们提出的信息论量在实际应用中易于计算，并展示了其在真实脑数据上的应用。