After introducing process data analytics using latent variable methods and machine learning, this paper briefly review the essence and objectives of latent variable methods to distill desirable components from a set of measured variables. These latent variable methods are then extended to modeling high dimensional time series data to extract the most dynamic latent time series, of which the current values are best predicted from the past values of the extracted latent variables. We show with an industrial case study how real process data are efficiently and effectively modeled using these dynamic methods. The extracted features reveal hidden information in the data that is valuable for understanding process variability. (C) 2017 Elsevier Ltd. All rights reserved.