Efficient Dynamic Latent Variable Analysis for High-Dimensional Time Series Data

Yining DONG, Yingxiang LIU, S Joe QIN*

*Corresponding author for this work

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

43 Citations (Scopus)


Dynamic-inner canonical correlation analysis (DiCCA) extracts dynamic latent variables from high-dimensional time series data with a descending order of predictability in terms of R2. The reduced dimensional latent variables with rank-ordered predictability capture the dynamic features in the data, leading to easy interpretation and visualization. In this article, numerically efficient algorithms for DiCCA are developed to extract dynamic latent components from high-dimensional time series data. The numerically improved DiCCA algorithms avoid repeatedly inverting a covariance matrix inside the iteration loop of the numerical DiCCA algorithms. A further improvement using singular value decomposition converts the generalized eigenvector problem into a standard eigenvector problem for the DiCCA solution. Another improvement in model efficiency in this article is the dynamic model compaction of the extracted latent scores using autoregressive integrated moving average (ARIMA) models. Integrating factors, if existed in the latent variable scores, are made explicit in the ARIMA models. Numerical tests on two industrial datasets are provided to illustrate the improvements.
Original languageEnglish
Article number8928945
Pages (from-to)4068-4076
Number of pages9
JournalIEEE Transactions on Industrial Informatics
Issue number6
Publication statusPublished - Jun 2020
Externally publishedYes

Bibliographical note

This work was supported in part by the Fundamental Research Program of the Shenzhen Committee on Science and Innovations under Grant 20160207 and Grant 20170155. Paper no. TII-19-2782.


  • Canonical correlation analysis
  • dynamic feature extraction
  • high-dimensional time series
  • latent dynamic modeling
  • numerical implementation


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