2014/06/18 by Anthony Bagnall, Bagnall, Anthony, Jason Lines +1 · 1 citation
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #cs.LG
paper · pdf · doi:10.48550/arxiv.1406.4757
arxiv created 2014/06/18 · arxiv updated 2014/06/19
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~\citelines14elastic, 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.