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.