Temporal data, representing chronological observations of complex systems, has always
been a typical data structure that can be widely generated by many domains, such as …

Inflation-poverty causal nexus in sub-Saharan African countries: an asymmetric panel
causality approach | Emerald Insight Books and journals Case studies Expert Briefings Open …

Inferring causal relationships in observational time series data is an important task when
interventions cannot be performed. Granger causality is a popular framework to infer …

Estimating causal relations is vital in understanding the complex interactions in multivariate
time series. Non-linear coupling of variables is one of the major challenges in accurate …

Cause-effect analysis is crucial to understand the underlying mechanism of a system. We
propose to exploit model invariance through interventions on the predictors to infer causality …

Causal inference in dynamical systems is a challenge for different research areas. So far it is
mostly about understanding to what extent the underlying causal mechanisms can be …

Latent variables often mask cause-effect relationships in observational data which provokes
spurious links that may be misinterpreted as causal. This problem sparks great interest in the …

Data is central in almost all scientific disciplines nowadays. Furthermore, intelligent systems
have developed rapidly in recent years, so that in many disciplines the expectation is …

This thesis investigates the use of neural network models for time series forecasting with an
emphasis on modelling latent dynamics (unobservable time series). Time series forecasting …

State-of-the-art machine learning methods, especially deep neural networks, have reached
impressive results in many prediction and classification tasks. Rising complexity and …