One of the most important aspects of time series is their degree of stochasticity vs. chaoticity.
Since the discovery of chaotic maps, many algorithms have been proposed to discriminate …

Infectious disease outbreaks recapitulate biology: they emerge from the multi-level
interaction of hosts, pathogens, and environment. Therefore, outbreak forecasting requires …

It is unquestionable that time series forecasting is of paramount importance in many fields.
The most used machine learning models to address time series forecasting tasks are …

We investigate a generalised version of the recently proposed ordinal partition time series to
network transformation algorithm. First, we introduce a fixed time lag for the elements of …

A symbolic encoding scheme, based on the ordinal relation between the amplitude of
neighboring values of a given data sequence, should be implemented before estimating the …

Complexity measures are important to understand and analyze systems with one
dimensional data. However, extension of these methods to images (two dimensional data) …

The separation of blood vessels in the retina is a major aspect in detecting ailment and is
carried out by segregating the retinal blood vessels from the fundus images. Moreover, it …

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 series …

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 …

In 2013, Karsten Keller, Jürgen Kurths, and one of us (JMA) guest edited an issue of the
European Physical Journal Special Topics, entitled Recent Progress in Symbolic Dynamics …