论文标题
多元置换熵，笛卡尔图产品方法
Multivariate permutation entropy, a Cartesian graph product approach
论文作者
论文摘要
熵指标是量化时间序列复杂性的非线性度量。其中，由于其稳健性和快速计算，置换熵是通用度量的。需要多元熵指标技术来分析由一个以上时间序列组成的数据。为此，我们使用基于图的方法提出了一个多元置换熵$ MPE_G $。 Given a multivariate signal, the algorithm $MPE_G$ involves two main steps: 1) we construct an underlying graph G as the Cartesian product of two graphs G1 and G2, where G1 preserves temporal information of each times series together with G2 that models the relations between different channels, and 2) we consider the multivariate signal as samples defined on the regular graph G and apply the recently introduced permutation entropy for graphs. 我们基于图的方法具有考虑各种类型的跨通道关系和信号的灵活性，并且它克服了当前多元置换熵的局限性。
Entropy metrics are nonlinear measures to quantify the complexity of time series. Among them, permutation entropy is a common metric due to its robustness and fast computation. Multivariate entropy metrics techniques are needed to analyse data consisting of more than one time series. To this end, we present a multivariate permutation entropy, $MPE_G$, using a graph-based approach. Given a multivariate signal, the algorithm $MPE_G$ involves two main steps: 1) we construct an underlying graph G as the Cartesian product of two graphs G1 and G2, where G1 preserves temporal information of each times series together with G2 that models the relations between different channels, and 2) we consider the multivariate signal as samples defined on the regular graph G and apply the recently introduced permutation entropy for graphs. Our graph-based approach gives the flexibility to consider diverse types of cross channel relationships and signals, and it overcomes with the limitations of current multivariate permutation entropy.
