Resumen de Some recent methods for analyzing high dimensional time series
This article analyzes six recent advances in the analyses of high dimensional time series.
The first two procedures have the objective of understanding the structure of the set of series: dynamic quantiles for data visualization and clustering by dependency to split the series into homogeneous groups. The other four methods are oriented to modeling and forecasting large sets of time series by dynamic factor models (DFM): procedures for determining the number of factors, for estimating DFM with cluster structure, for forecasting generalized dynamic factor models and for modeling matrices of time series are described. Some comments about the future evolution of the field of dependent high dimensional data are included in the conclusions.
