Multivariate permutation entropy via the Cartesian graph product to analyse two-phase flow

Activity: Academic talk or presentation typesOral presentation


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, MPEG, using a graph-based approach.

Given a multivariate signal, the algorithm MPEG 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.
Period10 Nov 2022
Event titleThe 11th International Conference on Complex Networks and their Applications 2022
Event typeConference
Conference number11
LocationPalermo, ItalyShow on map
Degree of RecognitionInternational