Multivariate Gaussian and Student-t process regression for multi-output prediction

Zexun Chen*, Bo Wang, Alexander N. Gorban

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Gaussian process model for vector-valued function has been shown to be useful for multi-output prediction. The existing method for this model is to reformulate the matrix-variate Gaussian distribution as a multivariate normal distribution. Although it is effective in many cases, reformulation is not always workable and is difficult to apply to other distributions because not all matrix-variate distributions can be transformed to respective multivariate distributions, such as the case for matrix-variate Student-t distribution. In this paper, we propose a unified framework which is used not only to introduce a novel multivariate Student-t process regression model (MV-TPR) for multi-output prediction, but also to reformulate the multivariate Gaussian process regression (MV-GPR) that overcomes some limitations of the existing methods. Both MV-GPR and MV-TPR have closed-form expressions for the marginal likelihoods and predictive distributions under this unified framework and thus can adopt the same optimization approaches as used in the conventional GPR. The usefulness of the proposed methods is illustrated through several simulated and real-data examples. In particular, we verify empirically that MV-TPR has superiority for the datasets considered, including air quality prediction and bike rent prediction. At last, the proposed methods are shown to produce profitable investment strategies in the stock markets.

Original languageEnglish
Pages (from-to)3005-3028
Number of pages24
JournalNeural Computing and Applications
Issue number8
Early online date31 Dec 2019
Publication statusPublished - 1 Apr 2020

Keywords / Materials (for Non-textual outputs)

  • Gaussian process regression
  • industrial sector
  • multi-output prediction
  • multivariate Gaussian process
  • multivariate Student-t process
  • stock investment strategy
  • Student-t process regression
  • time series prediction


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