Transfer entropy (TE) is a powerful tool for analyzing causality between time series and
complex systems. However, it faces two key challenges. First, TE is often used to quantify the …

Time series forecasting is of fundamental importance for financial market prediction and,
consequently, for portfolio allocation strategies. However, non-stationarity and non-linearity …

In long-term time series forecasting (LTSF) tasks, existing deep learning models overlook
the crucial characteristic that discrete time series originate from underlying continuous …

Recent advances in brain network analysis are largely based on graph theory methods to
assess brain network organization, function, and malfunction. Although, functional magnetic …

The basic characteristic of a chaotic system is its sensitivity to the infinitesimal changes in its
initial conditions. A limit to predictability in chaotic system arises mainly due to this sensitivity …

Purpose The aim of this article was to introduce an important tool, cross-recurrence analysis,
to speech production applications by showing how it can be adapted to evaluate the …

This paper describes a comparison of three types of feature sets. The feature sets were
intended to classify 13 faults in a centrifugal pump (CP) and 17 valve faults in a reciprocating …

Bernstein Series in Computational Neuroscience reflects the Bernstein Network's broad
research and teaching activities, including models of neural circuits and higher brain …

Phase space reconstruction is crucial for predicting chaotic hydrological time series.
However, traditional multivariate phase space reconstruction methods, such as high …

Random telegraph noise (RTN) owns its very name to its assumed stochastic nature. In this
paper, we follow up previous works that questioned this stochastic nature, and we …