Abstract
k Nearest Neighbour classification techniques, where \(k=1\), coupled with Dynamic Time Warping (DTW) are the most effective and most frequently used approaches for time series classification. However, because of the quadratic complexity of DTW, research efforts have been directed at methods and techniques to make the DTW process more efficient. This paper presents a new approach to efficient DTW, the Sub-Sequence-Based DTW approach. Two variations are considered, fixed length sub-sequence segmentation and fixed number sub-sequence segmentation. The reported experiments indicate that the technique improvs efficiency, compared to standard DTW, without adversely affecting effectiveness.
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