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 Citation (Scopus)

Abstract

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 - IEEE International Conference on Data Mining, ICDM
Pages151-160
Number of pages10
DOIs
StatePublished - 2012
Externally publishedYes
Event12th IEEE International Conference on Data Mining, ICDM 2012 - Brussels, Belgium
Duration: Dec 10 2012Dec 13 2012

Other

Other12th IEEE International Conference on Data Mining, ICDM 2012
CountryBelgium
CityBrussels
Period12/10/1212/13/12

Fingerprint

Students
Gaussian distribution
Measurement errors
Random processes
Processing
Statistical Models

Keywords

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

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Chen, Y., Chen, F., Dai, J., Clancy, T. C., & Wu, Y. (2012). Student-t based Robust Spatio-Temporal Prediction. In Proceedings - IEEE International Conference on Data Mining, ICDM (pp. 151-160). [6413907] https://doi.org/10.1109/ICDM.2012.135

Student-t based Robust Spatio-Temporal Prediction. / Chen, Yang; Chen, Feng; Dai, Jing; Clancy, T. Charles; Wu, Yao-jan.

Proceedings - IEEE International Conference on Data Mining, ICDM. 2012. p. 151-160 6413907.

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

Chen, Y, Chen, F, Dai, J, Clancy, TC & Wu, Y 2012, Student-t based Robust Spatio-Temporal Prediction. in Proceedings - IEEE International Conference on Data Mining, ICDM., 6413907, pp. 151-160, 12th IEEE International Conference on Data Mining, ICDM 2012, Brussels, Belgium, 12/10/12. https://doi.org/10.1109/ICDM.2012.135
Chen Y, Chen F, Dai J, Clancy TC, Wu Y. Student-t based Robust Spatio-Temporal Prediction. In Proceedings - IEEE International Conference on Data Mining, ICDM. 2012. p. 151-160. 6413907 https://doi.org/10.1109/ICDM.2012.135
Chen, Yang ; Chen, Feng ; Dai, Jing ; Clancy, T. Charles ; Wu, Yao-jan. / Student-t based Robust Spatio-Temporal Prediction. Proceedings - IEEE International Conference on Data Mining, ICDM. 2012. pp. 151-160
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