Robust Prediction of Large Spatio-Temporal Datasets

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dc.contributor.authorChen, Yangen
dc.contributor.committeechairChen, Ing-Rayen
dc.contributor.committeechairClancy, Thomas Charles IIIen
dc.contributor.committeememberYu, Guoqiangen
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2013-05-25T08:00:39Zen
dc.date.available2013-05-25T08:00:39Zen
dc.date.issued2013-05-24en
dc.description.abstractThis thesis describes a robust and efficient design of Student-t based Robust Spatio-Temporal Prediction, namely, St-RSTP, to provide estimation based on observations over spatio-temporal neighbors. It is crucial to many applications in geographical information systems, medical imaging, urban planning, economy study, and climate forecasting. 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 statistical model with linear order complexity for processing large scale spatiotemporal data. However, STRE has been shown sensitive to outliers or anomaly observations. In our design, the St-RSTP model assumes that the measurement error follows Student's t-distribution, instead of a traditional Gaussian distribution. To handle the analytical intractable inference of Student's t model, we propose an approximate inference algorithm in the framework of Expectation Propagation (EP). Extensive experimental evaluations, based on both simulation and real-life data sets, demonstrated the robustness and the efficiency of our Student-t prediction model compared with the STRE model.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:1079en
dc.identifier.urihttp://hdl.handle.net/10919/23098en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectRobust Predictionen
dc.subjectExpectation Propagationen
dc.subjectStudent's t Modelen
dc.subjectBayesian Hierarchical Modelen
dc.subjectSpatio-Temporal Processen
dc.titleRobust Prediction of Large Spatio-Temporal Datasetsen
dc.typeThesisen
thesis.degree.disciplineComputer Science and Applicationsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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