The human nervous system is one of the most complicated systems in nature. Complex
nonlinear behaviours have been shown from the single neuron level to the system level. For …

Inferring nonlinear and asymmetric causal relationships between multivariate longitudinal
data is a challenging task with wide-ranging application areas including clinical medicine …

A key challenge to gaining insight into complex systems is inferring nonlinear causal
directional relations from observational time-series data. Specifically, estimating causal …

For data-driven anomaly detection, it is difficult to model a prediction model with high
accuracy and sensitivity to anomalous states. In order to solve the above problems, this …

Covering the latest cutting-edge techniques in biomedical signal processing while
presenting a coherent treatment of various signal processing methods and applications, this …

Identifying, from time series analysis, reliable indicators of causal relationships is essential
for many disciplines. Main challenges are distinguishing correlation from causality and …

The discovery of structure from time series data is a key problem in fields of study working
with complex systems. Most identifiability results and learning algorithms assume the …

Granger causality is a fundamental technique for causal inference in time series data,
commonly used in the social and biological sciences. Typical operationalizations of Granger …

This paper presents a new methodology for characterising the evolving behaviour of the
time-varying causality between multivariate time series, from the perspective of change in …

The number of complex infrastructures in an industrial setting is growing and is not immune
to unexplained recurring events such as breakdowns or failure that can have an economic …