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Found 2 papers, 1 papers with code
Pattern Sampling for Shapelet-based Time Series Classification
The asymptotic time complexity of subsequence-based algorithms remains a higher-order polynomial, because these algorithms are based on exhaustive search for highly discriminative subsequences.
Ensembles of Randomized Time Series Shapelets Provide Improved Accuracy while Reducing Computational Costs
Using random sampling reduces the number of evaluated candidates and consequently the required computational cost, while the classification accuracy of the resulting models is also not significantly different than that of the exact algorithm.
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