“...•The constructed networks improve the accuracy of the clustering results.•The network features reflect the information and reduce the dimension of MTS.•The...”

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

“...•A new method transforms time series into network.•An improved DTW calculates the distance between each component.•The proposed clustering method with good...”

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

“...Marketers distinguish three types of media: paid (e.g., advertising), owned (e.g., company website), and earned (e.g., publicity). The effects of paid media on...”

“...Tourism research is replete with applications of univariate time-series decomposition techniques: multivariate frameworks have been largely ignored. In this...”

“...Entropy, 19, 408 (2017) Exploiting the theory of state space models, we derive the exact expressions of the information transfer, as well as redundant and...”

“...Forecasting in geophysical time series is a challenging problem with numerous applications. The presence of correlation (i.e. spatial correlation across...”

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

“...The deep learning model has been used to predict the variation rule of the target series of multivariate time series data. Based on the attention mechanism,...”