  Data mining research into time series classification (TSC) has focussed on
alternative distance measures for nearest neighbour classifiers. It is standard
practice to use 1-NN with Euclidean or dynamic time warping (DTW) distance as a
straw man for comparison. As part of a wider investigation into elastic
distance measures for TSC~\cite{lines14elastic}, we perform a series of
experiments to test whether this standard practice is valid.
  Specifically, we compare 1-NN classifiers with Euclidean and DTW distance to
standard classifiers, examine whether the performance of 1-NN Euclidean
approaches that of 1-NN DTW as the number of cases increases, assess whether
there is any benefit of setting $k$ for $k$-NN through cross validation whether
it is worth setting the warping path for DTW through cross validation and
finally is it better to use a window or weighting for DTW. Based on experiments
on 77 problems, we conclude that 1-NN with Euclidean distance is fairly easy to
beat but 1-NN with DTW is not, if window size is set through cross validation.
