Latent vector autoregressive modeling and feature analysis of high dimensional and noisy data from dynamic systems

S. Joe QIN*

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

7 Citations (Scopus)

Abstract

In this article, a novel latent vector autoregressive (LaVAR) modeling algorithm with a canonical correlation analysis (CCA) objective is proposed to estimate a fully-interacting reduced-dimensional dynamic model. This algorithm is an advancement of the dynamic inner canonical correlation analysis (DiCCA) algorithm, which builds univariate latent autoregressive models that are noninteracting. The dynamic latent variable scores of the proposed algorithm are guaranteed to be orthogonal with a descending order of predictability, retaining the properties of DiCCA. Further, the LaVAR-CCA algorithm solves multiple latent variables simultaneously with a statistical interpretation of the profile likelihood. The Lorenz oscillator with noisy measurements and an application case study on an industrial dataset are used to illustrate the superiority of the proposed algorithm. The reduced-dimensional latent dynamic model has numerous potential applications for prediction, feature analysis, and diagnosis of systems with rich measurements.
Original languageEnglish
Article numbere17703
Number of pages14
JournalAICHE Journal
Volume68
Issue number6
Early online date30 Mar 2022
DOIs
Publication statusPublished - Jun 2022
Externally publishedYes

Bibliographical note

Financial support for this work from a General Research Fund by RGC of Hong Kong (No. 11303421), Dimension reduction modeling methods for high dimensional dynamic data in smart manufacturing and operations, the Natural Science Foundation of China grant (U20A20189), Big data-driven abnormal situation intelligent diagnosis and self-healing control for process industries, and the City University of Hong Kong Project (9380123), SGP: Bridging between systems theory and dynamic data learning toward industrial intelligence and industry 4.0 is gratefully acknowledged. The author appreciates the helpful discussions with Prof. Alain Bensoussan at the City University of Hong Kong and Prof. Yingying Fan at the University of Southern California.

Keywords

  • dynamic latent variable learning
  • latent system modeling
  • plant-wide feature analysis
  • predictable latent time series
  • profile likelihood

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