Predictive Mining of Time Series Data in Astronomy

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dc.contributor.authorPerlman, Eric
dc.contributor.authorJava, Akshay
dc.date.accessioned2019-01-28T16:26:04Z
dc.date.available2019-01-28T16:26:04Z
dc.date.issued2003-01-01
dc.descriptionAstronomical Data Analysis Software and Systems XII ASP Conference Seriesen_US
dc.description.abstractWe discuss the development of a Java toolbox for astronomical time series data. Rather than using methods conventional in astronomy (e.g., power spectrum and cross-correlation analysis) we employ rule discovery techniques commonly used in analyzing stock-market data. By clustering patterns found within the data, rule discovery allows one to build pre- dictive models, allowing one to forecast when a given event might occur or whether the occurrence of one event will trigger a second. We have tested the toolbox and accompanying display tool on datasets (represent- ing several classes of objects) from the RXTE All Sky Monitor. We use these datasets to illustrate the methods and functionality of the toolbox. We also discuss issues that can come up in data analysis as well as the possible future development of the packageen_US
dc.description.urihttps://ebiquity.umbc.edu/paper/html/id/292/Predictive-Mining-of-Time-Series-Data-in-Astronomyen_US
dc.format.extent4 pagesen_US
dc.genreconference papers and proceedings preprintsen_US
dc.identifierdoi:10.13016/m2qqpt-xi2g
dc.identifier.urihttp://hdl.handle.net/11603/12624
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Physics Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectpredictive miningen_US
dc.subjecttime seriesen_US
dc.subjectdata in astronomyen_US
dc.subjectUMBC Ebiquity Research Groupen_US
dc.titlePredictive Mining of Time Series Data in Astronomyen_US
dc.typeTexten_US

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