Dynamic-Inner Canonical Correlation and Causality Analysis for High Dimensional Time Series Data

In this paper, a novel dynamic-inner canonical correlation analysis (DiCCA) algorithm is proposed to extract dynamic components from high dimensional dynamic data. DiCCA extracts latent variables with descending dynamics, which are referred to as principal time series. Since DiCCA enables the principal time series to have maximal predictability, the most important dynamic features in the data are guaranteed to be extracted first. Therefore, usually a lower dimensional principal time series are able to provide good representation of the dynamic features, leading to the ease of interpretation and visualization. A case study on the Eastman plant-wide oscillating dataset demonstrates the effectiveness of the proposed method. Combined with Granger causality analysis, major oscillatory latent dynamics are analyzed, identified, and localized to equipment malfunctions. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.