Student-t based Robust Spatio-Temporal Prediction

Yang Chen, Feng Chen, Jing Dai, T. Charles Clancy, Yao Jan Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


This paper describes an efficient and effective design of Robust Spatio-Temporal Prediction based on Student's t distribution, namely, St-RSTP, to provide estimations based on observations over spatio-temporal neighbors. The proposed St-RSTP is more resilient to outliers or other small departures from model assumptions than its ancestor, the Spatio-Temporal Random Effects (STRE) model. STRE is a state-of-the-art statistical model with linear order complexity for large scale processing. However, it assumes Gaussian observations, which has the well-known limitation of non-robustness. In our St-RSTP design, the measurement error follows Student's t distribution, instead of a traditional Gaussian distribution. This design reduces the influence of outliers, improves prediction quality, and keeps the problem analytically intractable. We propose a novel approximate inference approach, which approximates the model into the form that separates the high dimensional latent variables into groups, and then estimates the posterior distributions of different groups of variables separately in the framework of Expectation Propagation. As a good property, our approximate approach degeneralizes to the standard STRE based prediction, when the degree of freedom of the Student's t distribution is set to infinite. Extensive experimental evaluations based on both simulation and real-life data sets demonstrated the robustness and the efficiency of our Student-t prediction model. The proposed approach provides critical functionality for stochastic processes on spatio-temporal data.

Original languageEnglish (US)
Title of host publicationProceedings - 12th IEEE International Conference on Data Mining, ICDM 2012
Number of pages10
StatePublished - 2012
Event12th IEEE International Conference on Data Mining, ICDM 2012 - Brussels, Belgium
Duration: Dec 10 2012Dec 13 2012

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Other12th IEEE International Conference on Data Mining, ICDM 2012


  • Expectation propagation
  • Spatio-temporal process
  • Student's t distribution

ASJC Scopus subject areas

  • General Engineering


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