“...Multivariate time series forecasting has typically been a relevant and interesting topic in many fields, including economics, electricity consumption, solar...”

“...This paper deals with the factor modeling for high-dimensional time series based on a dimension-reduction viewpoint. Under stationary settings, the inference...”

“...Emerging at the end of 2019, COVID-19 has become a public health threat to people worldwide. Apart from deaths with a positive COVID-19 test, many others have...”

“...Clustering of multivariate time series is a central problem in data mining with applications in many fields. Frequently, the clustering target is to identify...”

“...•A time–frequency deep metric learning model is proposed for MTS classification.•A consistency regularizer is designed to capture the correlations among...”

“...The personalized approach to psychopathology conceptualizes mental disorder as a complex system of contextualized dynamic processes that is nontrivially...”

“...The analysis of multivariate time series is a common problem in areas like finance and economics. The classical tools for this purpose are vector...”

“...Many of the data sets extracted from real-world industrial environments are time series that describe dynamic processes with characteristics that change over...”

“...This paper introduces a novel algorithm for big data time series forecasting. Its main novelty lies in its ability to deal with multivariate data, i.e. to...”

“...Many studies on the prediction of manufacturing results using sensor signals have been conducted in the field of fault detection and classification (FDC) for...”