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• W2078804823 abstract "Similarity measures are of fundamental importance in time series data mining. Dynamic Time Warping (DTW) is a quite popular measure because it handles time distortions well. However, DTW has an inherent shortcoming in that DTW can lead to pathological alignments between time series where a single point maps onto a large subsection of another time series. To overcome this problem, we propose a novel variant of DTW named SC-DTW. SC-DTW employs shape context, a rich local shape descriptor, to replace the raw observed values considered by conventional DTW. The main novelties of SC-DTW are (1) it deeply explores both the numerical nature and shape nature of time series; and (2) neighborhood information for each point is taken into account. SC-DTW can generate a more feature-to-feature alignment between time series and thus serves as a robust similarity measure. We test the performance of SC-DTW on UCR time series datasets using the one nearest neighbor (1NN) classifier. Compared with other well-established methods, SC-DTW provides better accuracy on 24 of 34 datasets." @default.
• W2078804823 created "2016-06-24" @default.
• W2078804823 creator A5051151162 @default.
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• W2078804823 date "2015-09-01" @default.
• W2078804823 modified "2023-10-16" @default.
• W2078804823 title "Dynamic time warping under pointwise shape context" @default.
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• W2078804823 doi "https://doi.org/10.1016/j.ins.2015.04.007" @default.
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