Update paper.md

joss/paper.md

@@ -49,7 +49,6 @@ The main features of ``DTW-C++`` are as follows:
 
 Consider a time series to be a vector of some arbitrary length. Consider that we have $p$ such vectors in total, each possibly differing in length. To find a subset of $k$ clusters within the set of $p$ vectors we must first make $\frac{(^p C_2)}{2}$ pairwise comparisons between all vectors within the total set and find the `similarity' between each pair. This requires a distance metric to be defined, and dynamic time warping uses local warping (stretching or compressing along the time axis) of the elements within each vector to find optimal alignment (i.e., with minimum cost/distance) between each pair of vectors. 
 
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 Comparing two short time series $x$ and $y$ of differing lengths $n$ and $m$
 
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