Fault diagnosis is essential for troubleshooting and maintenance of industrial processes that
operate dynamically. Traditional reconstruction-based fault diagnosis methods, however …

Instrumental variable (IV) regression relies on instruments to infer causal effects from
observational data with unobserved confounding. We consider IV regression in time series …

In this paper, dynamic predictive monitoring schemes based on dynamic latent variable
models are proposed. We consider the most typical case in industrial data where dynamics …

Food warehouses and cold rooms have a significant potential for Demand Response (DR)
application (stopping or reducing the power of fans and compressors of the refrigeration …

Abstract Recently, Graph Neural Network-based approaches (GNNs) have been widely
studied in Multivariate Time Series (MTS) prediction, which could extract information from …

This book gives a brief survey of the theory of multidimensional (multivariate), weakly
stationary time series, with emphasis on dimension reduction and prediction. Understanding …

Any developed port plays a dominant role both in domestic and international trade reflecting
economic prosperity of the port and nearby regions in terms of its cargo throughput and port …

We propose a new parametrization for the estimation and identification of the impulse-
response functions (IRFs) of dynamic factor models (DFMs). The theoretical contribution of …

Climate change is a pressing global issue that profoundly impacts ecosystems, economies,
and societies. Accurate climate trend prediction is crucial for informed decision-making and …

Rank-deficient stationary stochastic vector processes are present in many problems in
network theory and dynamic factor analysis. In this article, we study the hidden dynamical …