Dynamic Weighted Canonical Correlation Analysis for Auto-Regressive Modeling

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

1 Scopus Citations
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Original languageEnglish
Pages (from-to)200-205
Journal / PublicationIFAC-PapersOnLine
Issue number2
Publication statusPublished - Nov 2020


Title21st IFAC World Congress 2020
Period12 - 17 July 2020



Canonical correlation analysis (CCA) is widely used as a supervised learning method to extract correlations between process and quality datasets. When used to extract relations between current data and historical data, CCA can also be regarded as an auto-regressive modeling method to capture dynamics. Various dynamic CCA algorithms were developed in the literature. However, these algorithms do not consider strong dependence existing in adjacent samples, which may lead to unnecessarily large time lags and inaccurate estimation of current values from historical data. In this paper, a dynamic weighted CCA (DWCCA) algorithm is proposed to address this issue with a series of polynomial basis functions. DWCCA extracts dynamic relations by maximizing correlations between current data and a weighted representation of past data, and the weights rely only on a limited number of polynomial functions, which removes the negative effect caused by strongly collinear neighboring samples. After all the dynamics are exploited, static principal component analysis is then employed to further explore the cross-correlations in the dataset. The Tennessee Eastman process is utilized to demonstrate the effectiveness of the proposed DWCCA method in terms of prediction efficiency and collinearity handling.

Research Area(s)

  • Auto-regressive modeling, Dynamic weighted canonical correlation analysis, Principal component analysis

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