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Claremont Colleges
Scholarship @ Claremont
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Paern Recognition in Stock Data
Kathryn Dover
Harvey Mudd College
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Pattern Recognition in Stock Data
Kathryn Dover
Weiqing Gu, Advisor
Dagan Karp, Reader
Department of Mathematics
May, 2017
Copyright © 2017 Kathryn Dover.
The author grants Harvey Mudd College and the Claremont Colleges Library the
nonexclusive right to make this work available for noncommercial, educational
purposes, provided that this copyright statement appears on the reproduced
materials and notice is given that the copying is by permission of the author. To
disseminate otherwise or to republish requires written permission from the author.
Abstract
Finding patterns in high dimensional data can be difficult because it cannot
be easily visualized. Many different machine learning methods are able to
fit this high dimensional data in order to predict and classify future data but
there is typically a large expense on having the machine learn the fit for a
certain part of the dataset. This thesis proposes a geometric way of defining
different patterns in data that is invariant under size and rotation so it is not
so dependent on the input data. Using a Gaussian Process, the pattern is
found within stock market data and predictions are made from it.
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