343 citations · 3.3k across the 63 of their papers we have counts for
17 papers · 1 filter
Characterizing unstructured data with the nearest neighbor permutation entropy
Leonardo G. J. M. Voltarelli, Arthur A. B. Pessa, Luciano Zunino +4
Permutation entropy and its associated frameworks are remarkable examples of physics-inspired techniques adept at processing complex and extensive datasets. Despite substantial pro…
Clustering free-falling paper motion with complexity and entropy
Arthur A. B. Pessa, Matjaz Perc, Haroldo V. Ribeiro
Many simple natural phenomena are characterized by complex motion that appears random at first glance, but that often displays underlying patterns and behavior that can be clustere…
Permutation Jensen-Shannon distance: A versatile and fast symbolic tool for complex time series analysis
Luciano Zunino, Felipe Olivares, Haroldo V. Ribeiro +1
The main motivation of this paper is to introduce the permutation Jensen-Shannon distance, a symbolic tool able to quantify the degree of similarity between two arbitrary time seri…
Determining liquid crystal properties with ordinal networks and machine learning
Arthur A. B. Pessa, Rafael S. Zola, Matjaz Perc +1
Machine learning methods are becoming increasingly important for the development of materials science. In spite of this, the use of image analysis in the development of these syste…
ordpy: A Python package for data analysis with permutation entropy and ordinal network methods
Arthur A. B. Pessa, Haroldo V. Ribeiro
Since Bandt and Pompe's seminal work, permutation entropy has been used in several applications and is now an essential tool for time series analysis. Beyond becoming a popular and…
Characterizing stochastic time series with ordinal networks
Arthur A. B. Pessa, Haroldo V. Ribeiro
Approaches for mapping time series to networks have become essential tools for dealing with the increasing challenges of characterizing data from complex systems. Among the differe…