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 …

In 2002, in a seminal article, Bandt and Pompe proposed a new methodology for the
analysis of complex time series, now known as Ordinal Analysis. The ordinal methodology is …

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 …

Biometric systems are playing a key role in the multitude of applications and placed at the
center of debate in the scientific research community. Among the numerous biometric …

While artificial intelligence is widely employed in many applications, it is vulnerable to bias
and unethical use. Therefore, fairness evaluation tools and bias mitigation algorithms have …

Identifying the change point of a system's health status is important. Indeed, a change point
usually signifies an incipient fault under development. The One-Class Support Vector …

In this work we analyze predictability and complexity of monthly rainfall temporal series
recorded from 1950 to 2012, at 133 gauging stations in Pernambuco state, northeastern …

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

We investigated the influence of the construction of cascade dams and reservoirs on the
predictability and complexity of the streamflow of the São Francisco River, Brazil, by using …

In this work, we propose a solution for detecting botnet attacks on the Internet of Things (IoT)
by identifying anomalies in the temporal dynamics of their devices. Given their limited …