Latent Vector Autoregressive Modeling for Reduced Dimensional Dynamic Feature Extraction and Prediction

S. Joe QIN*

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Referred Conference Paperpeer-review

4 Citations (Scopus)


In this paper, we propose a novel latent vector autoregressive (LaVAR) modeling algorithm with a canonical correlation analysis (CCA) objective 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 non-interacting. The dynamic latent variable scores of the proposed algorithm are enforced to be orthogonal or contemporaneously independent, similar to those of DiCCA. An application case study on an industrial dataset is given to illustrate the superiority of the proposed algorithm. The reduced-dimensional latent dynamic model has potential applications for prediction, control, and diagnosis of systems with rich sensors, such as industrial internet of things.
Original languageEnglish
Title of host publication2021 60th IEEE Conference on Decision and Control (CDC)
PublisherInstitute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781665436595, 9781665436588
ISBN (Print)9781665436601
Publication statusPublished - Dec 2021
Externally publishedYes
Event60th IEEE Conference on Decision and Control (CDC 2021) - Fairmont Hotel (Virtual), Austin, United States
Duration: 13 Dec 202117 Dec 2021

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Conference60th IEEE Conference on Decision and Control (CDC 2021)
Country/TerritoryUnited States

Bibliographical note

Financial support for this work from the Natural Science Foundation of China grant, Big data-driven abnormal situation intelligent diagnosis and self-healing control for process industries (U20A201398), and the City University of Hong Kong under Project, SGP: Bridging between Systems Theory and Dynamic Data Learning towards Industrial Intelligence and Industry 4.0 (9380123), is gratefully acknowledged. The author acknowledges discussions and input from Dr. Jicong Fan of the Chinese University of Hong Kong, Shenzhen.


Dive into the research topics of 'Latent Vector Autoregressive Modeling for Reduced Dimensional Dynamic Feature Extraction and Prediction'. Together they form a unique fingerprint.

Cite this