Entropy is a powerful tool for the analysis of time series, as it allows describing the
probability distributions of the possible state of a system, and therefore the information …

More than ten years ago Bandt and Pompe introduced a new measure to quantify
complexity in measured time series. During these ten years, this measure has been modified …

A growing number of algorithms have been proposed to map a scalar time series into
ordinal partition transition networks. However, most observable phenomena in the empirical …

This paper examines the populational impact of the COVID-19 vaccinations for Brazil.
Therefore, our analysis takes into account the time series of the daily number of deaths …

A recently proposed methodology called the Horizontal Visibility Graph (HVG)[Luque et al.,
Phys. Rev. E., 80, 046103 (2009)] that constitutes a geometrical simplification of the well …

The search for patterns in time series is a very common task when dealing with complex
systems. This is usually accomplished by employing a complexity measure such as …

Detection and prediction of thermoacoustic instabilities are major challenges in the
development and operation of combustion systems. In this paper, we introduce a tool for …

By appealing to a long list of different nonlinear maps we review the characterization of time
series arising from chaotic maps. The main tool for this characterization is the permutation …

Strategies based on the extraction of measures from ordinal patterns transformation, such as
probability distributions and transition graphs, have reached relevant advancements in …

At issue here is the distinction between noise and chaos. They are different phenomena but
sometimes produce results that resemble each other. From a numerical viewpoint, in …