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Kathryn Dover

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Pattern Recognition in Stock Data

Weiqing Gu
Second Reader(s)
Dagan Karp


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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