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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

23 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)476-481
Journal / PublicationIFAC-PapersOnLine
Volume51
Issue number18
Online published8 Oct 2018
Publication statusPublished - 2018
Externally publishedYes

Abstract

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.

Research Area(s)

  • dynamic data modeling, Granger causality analysis, latent dynamic model, root cause diagnosis