论文标题
转移熵和部分转移熵的缺点：扩展它们以逃避维度的诅咒
Shortcomings of transfer entropy and partial transfer entropy: Extending them to escape the curse of dimensionality
论文作者
论文摘要
转移熵（TE）捕获了两个变量之间的定向关系。部分转移熵（PTE）解释了多元系统的所有混杂变量的存在，并且仅在直接因果关系中渗透。但是，PTE的计算涉及高维分布，因此在许多变量的情况下可能不健壮。在这项工作中，通过基于不同方案的相互关系与驾驶或响应变量建立了减少的混杂变量，从而引入了PTE的不同变体。基于连通性的PTE变体和利用随机森林（RF）方法学对合成时间序列进行了评估。经验发现表明所建议的变体优于TE和PTE，尤其是在高维系统的情况下。
Transfer entropy (TE) captures the directed relationships between two variables. Partial transfer entropy (PTE) accounts for the presence of all confounding variables of a multivariate system and infers only about direct causality. However, the computation of PTE involves high dimensional distributions and thus may not be robust in case of many variables. In this work, different variants of PTE are introduced, by building a reduced number of confounding variables based on different scenarios in terms of their interrelationships with the driving or response variable. Connectivity-based PTE variants and utilizing the random forests (RF) methodology are evaluated on synthetic time series. The empirical findings indicate the superiority of the suggested variants over TE and PTE, especially in case of high dimensional systems.
