An altered kernel transformation for time series classification

Abstract

Motivated by the great efficiency of dynamic time warping (DTW) for time series similarity measure, a Gaussian DTW (GDTW) kernel has been developed for time series classification. This paper proposes an altered Gaussian DTW (AGDTW) kernel function, which takes into consideration each of warping path between time series. Time series can be mapped into a special kernel space where the homogeneous data gather together and the heterogeneous data separate from each other. Classification results on transformed time series combined with different classifiers demonstrate that the AGDTW kernel is more powerful to represent and classify time series than the Gaussian radius basis function (RBF) and GDTW kernels.