| dc.contributor.author | Roy, Arighna | |
| dc.description.abstract | One of the popular methods for quantifying the relationship between two time series data
sets is canonical correlations; however, it is linear and cannot accommodate more complex scenarios,
such as time series data for which distance relationships are best characterized through dynamic
time warping. I introduce a nearest-neighbor-overlap method that resolves both problems and
allows a reliable determination of signi cant relationships. The nearest neighbor algorithm also
does not depend on the normal distribution of the variables, unlike canonical correlations. Also,
it is not sensitive to singularity (when one variable is derivable from another) of the data. I
demonstrate that our method substantially outperforms canonical correlation analysis for time
series data sets from the UCR repository as well as the environmental data of Red River Valley
region. | en_US |
| dc.publisher | North Dakota State University | en_US |
| dc.rights | NDSU Policy 190.6.2 | |
| dc.title | Quantifying Relationships Between Two Time Series Data Sets | en_US |
| dc.type | Thesis | en_US |
| dc.date.accessioned | 2018-04-23T19:22:05Z | |
| dc.date.available | 2018-04-23T19:22:05Z | |
| dc.date.issued | 2016 | en_US |
| dc.identifier.uri | https://hdl.handle.net/10365/28017 | |
| dc.description.sponsorship | National Science Foundation through grants PFI-1114363 and IIA-1355466 | en_US |
| dc.rights.uri | https://www.ndsu.edu/fileadmin/policy/190.pdf | |
| ndsu.degree | Master of Science (MS) | en_US |
| ndsu.college | Engineering | en_US |
| ndsu.department | Computer Science | en_US |
| ndsu.program | Computer Science | en_US |
| ndsu.advisor | Denton, Anne M. | |
