A Framework and Algorithms for Multivariate Time Series Analytics (MTSA): Learning, Monitoring, and Recommendation

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dc.contributor.advisorBrodsky, Alexander
dc.contributor.advisorLin, Jessica
dc.contributor.authorNgan, Chun-Kit
dc.creatorNgan, Chun-Kit
dc.date.accessioned2013-08-19T21:16:49Z
dc.date.available2013-08-19T21:16:49Z
dc.date.issued2013-08
dc.description.abstractMaking decisions over multivariate time series is an important topic which has gained significant interest in the past decade. A time series is a sequence of data points which are measured and ordered over uniform time intervals. A multivariate time series is a set of multiple, related time series in a particular domain in which domain experts utilize multivariate time series to make a vital decision. Through studying multivariate time series, specialists are able to understand problems of events from different perspectives within particular domains. Identification and detection of those significant events over multivariate time series can lead to a better decision-making and actionable recommendations.
dc.format.extent177 pages
dc.identifier.urihttps://hdl.handle.net/1920/8370
dc.language.isoen
dc.rightsCopyright 2013 Chun-Kit Ngan
dc.subjectComputer science
dc.subjectInformation science
dc.subjectOperations research
dc.subjectDatabase Models
dc.subjectDecision Support Systems
dc.subjectMultivariate Time Series Analytics
dc.subjectOptimization Model and Algorithms
dc.subjectQuery Language
dc.titleA Framework and Algorithms for Multivariate Time Series Analytics (MTSA): Learning, Monitoring, and Recommendation
dc.typeDissertation
thesis.degree.disciplineComputer Science
thesis.degree.grantorGeorge Mason University
thesis.degree.levelDoctoral

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