Abstract
It is well known that Dynamic Time Warping (DTW) is superior to Euclidean distance as a similarity measure in time series analyses. Use of DTW with the recently introduced warping window constraints and lower bounding measures has significantly increased the accuracy of time series classification while reducing the computational expense required. The warping window technique learns arbitrary constraints on the warping path while performing time series alignment. This work utilizes genetic algorithms to find the optimal warping window constraints which provide a better classification accuracy. Performance of the proposed methodology has been investigated on two problems from diverse domains with favorable results.
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