Identifying causal relationships and quantifying their strength from observational time series
data are key problems in disciplines dealing with complex dynamical systems such as the …

Cooperation between different data owners may lead to an improvement in forecast quality—
for instance, by benefiting from spatiotemporal dependencies in geographically distributed …

This study presents a comprehensive review of the automated classification in partial
discharge (PD) source identification and probabilistic interpretation of the classification …

The vector autoregressive (VAR) model is a useful tool for economic evaluation and
prediction. This paper develops a leave-subject-out cross-validation model averaging …

Prediction of water quality parameters is a significant aspect of contemporary green
development and ecological restoration. However, the conventional water quality prediction …

A random fuzzy model is proposed to express the probabilistic and possibilistic uncertainties
of wind speed simultaneously. In this model, wind speed is represented by a random …

Vector time series data are widely met in practice. In this paper we propose a multivariate
functional-coefficient regression model with heteroscedasticity for modelling such data. A …

In this article, a semiparametric time‐varying nonlinear vector autoregressive (NVAR) model
is proposed to model nonlinear vector time series data. We consider a combination of …

Scientific research often seeks to understand the causal structure underlying high-level
variables in a system. For example, climate scientists study how phenomena, such as El Ni …

The single-index coefficient model, where the coefficients are functions of an index of a
covariate vector, is a powerful tool for modelling nonlinearity in multivariate estimation. By …