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 …

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 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 …

Complex network approaches have been recently emerging as novel and complementary
concepts of nonlinear time series analysis that are able to unveil many features that are …

We introduce a representation space to contrast chaotic with stochastic dynamics. Following
the complex network representation of a time series through ordinal pattern transitions, we …

This article proposes TSCLAS, a time series classification strategy for the Internet of Things
(IoT) data, based on the class separability analysis of their temporal dynamics. Given the …

Analyzing people's mobility and identifying the transportation mode is essential for cities to
create travel diaries. It can help develop essential technologies to reduce traffic jams and …

The use of Bandt–Pompe probability distributions and descriptors of information theory has
been presenting satisfactory results with low computational cost in the time series analysis …

Prediction in natural environments is a challenging task, and there is a lack of clarity around
how a myopic organism can make short-term predictions given limited data availability and …

Human interlocutors may use emotions as an important signaling device for coordinating an
interaction. In this context, predicting a significant change in a speaker's emotion may be …