Trend Detection and Pattern Recognition in Financial Time Series

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dc.contributor.advisorGentle, James E
dc.contributor.authorWilson, Seunghye Jung
dc.creatorWilson, Seunghye Jung
dc.date.accessioned2016-09-28T10:23:06Z
dc.date.available2016-09-28T10:23:06Z
dc.date.issued2016
dc.description.abstractOne major interest of financial time series analysis is to identify changepoints of trends and recognize patterns that can be used for classification and clustering of time series. Because of the large amounts of data, nonlinear relationship of the data elements, and the presence of random noise, some method of data reduction is necessary. The data re- duction, however, must preserve the important characteristics of the original data. Many representation methods in the time domain or frequency domain have been suggested to accomplish efficient extraction of information. These include, for example, piecewise lin- ear approximation, symbolic representation, and discrete wavelet transformation (DWT). However, most of the existing methods do not take into consideration time information of trends and/or depend on user-defined parameters, for example the number of segments for piecewise approximation.
dc.format.extent126 pages
dc.identifier.urihttps://hdl.handle.net/1920/10463
dc.language.isoen
dc.rightsCopyright 2016 Seunghye Jung Wilson
dc.subjectStatistics
dc.subjectDetecting trend changepoints
dc.subjectFinancial time series
dc.subjectSimilarity measures
dc.subjectTime series data representation
dc.titleTrend Detection and Pattern Recognition in Financial Time Series
dc.typeDissertation
thesis.degree.disciplineStatistical Science
thesis.degree.grantorGeorge Mason University
thesis.degree.levelPh.D.

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