Master's Thesis

This is the final submission of my master thesis that is hosted on Concordia’s Spectrum repository here. It explores online kernel change point detection methods across a variety of synthetic datasets. Real market liquidity data is also analyzed using a kernel change point method.

To cite this thesis use the following bibtex snippet:

@unpublished{library987140,
          year = {2020},
          month = {August},
          author = {Tyler Manning-Dahan},
          title = {Applying Kernel Change Point Detection To Financial Markets},
          school = {Concordia University},
          url = {https://spectrum.library.concordia.ca/987140/}
}