Bridging systems theory and data science: A unifying review of dynamic latent variable analytics and process monitoring

S. Joe QIN*, Yining DONG, Qinqin ZHU, Jin WANG, Qiang LIU

*Corresponding author for this work

Research output: Journal PublicationsReview articleOther Review

92 Citations (Scopus)


This paper is concerned with data science and analytics as applied to data from dynamic systems for the purpose of monitoring, prediction, and inference. Collinearity is inevitable in industrial operation data. Therefore, we focus on latent variable methods that achieve dimension reduction and collinearity removal. We present a new dimension reduction expression of state space framework to unify dynamic latent variable analytics for process data, dynamic factor models for econometrics, subspace identification of multivariate dynamic systems, and machine learning algorithms for dynamic feature analysis. We unify or differentiate them in terms of model structure, objectives with constraints, and parsimony of parameterization. The Kalman filter theory in the latent space is used to give a system theory foundation to some empirical treatments in data analytics. We provide a unifying review of the connections among the dynamic latent variable methods, dynamic factor models, subspace identification methods, dynamic feature extractions, and their uses for prediction and process monitoring. Both unsupervised dynamic latent variable analytics and the supervised counterparts are reviewed. Illustrative examples are presented to show the similarities and differences among the analytics in extracting features for prediction and monitoring.
Original languageEnglish
Pages (from-to)29-48
Number of pages20
JournalAnnual Reviews in Control
Early online date16 Oct 2020
Publication statusPublished - 2020
Externally publishedYes

Bibliographical note

Financial support for this work from the City University of Hong Kong under Project 9380123, SGP: Bridging between Systems Theory and Dynamic Data Learning towards Industrial Intelligence and Industry 4.0, is gratefully acknowledged.


  • Data science
  • Kalman filtering
  • Latent variable analytics
  • Machine learning
  • Multivariate time series
  • Process data analytics


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