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- W2016094720 abstract "In time series mining, the Dynamic Time Warping (DTW) distance is a commonly and widely used similarity measure. Since the computational complexity of the DTW distance is quadratic, various kinds of warping constraints, lower bounds and abstractions have been developed to speed up time series mining under DTW distance. In this contribution, we propose a novel Lucky Time Warping (LTW) distance, with linear time and space complexity, which uses a greedy algorithm to accelerate distance calculations for nearest neighbor classification. The results show that, compared to the Euclidean distance (ED) and (un)constrained DTW distance, our LTW distance trades classification accuracy against computational cost reasonably well, and therefore can be used as a fast alternative for nearest neighbor time series classification." @default.
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- W2016094720 date "2014-03-24" @default.
- W2016094720 modified "2023-10-03" @default.
- W2016094720 title "Fast time series classification under lucky time warping distance" @default.
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- W2016094720 doi "https://doi.org/10.1145/2554850.2554885" @default.
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