Latent State Space Modeling of High-Dimensional Time Series with a Canonical Correlation Objective

Jiaxin YU, S. Joe QIN*

*Corresponding author for this work

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

5 Citations (Scopus)


High-dimensional time series are commonly encountered in modern control systems, especially in autonomous systems. In this letter, a novel parsimonious latent state space (LaSS) model is proposed to characterize the latent dynamics, achieving general latent dynamic modeling with dimension reduction. The LaSS model is optimized by alternating estimations of the dimension reduction projection and the latent state space model. Specifically, the latent state dynamics are estimated by stochastic subspace identification methods. Furthermore, the canonical correlation analysis (CCA) objective is employed to acquire the optimal predictability for the extracted latent variables. The proposed LaSS-CCA algorithm is tested on a real industrial case for its effectiveness.

Original languageEnglish
Pages (from-to)3469-3474
Number of pages6
JournalIEEE Control Systems Letters
Early online date16 Jun 2022
Publication statusPublished - 2022
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported by the National Natural Science Foundation of China under Grants 62075160, 61735011, and U2006216.

Publisher Copyright:
© 2017 IEEE.


  • Dynamic factor models
  • Dynamic latent variable models
  • Latent state space models
  • Reduced-dimensional dynamics


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