Pruning hybrid time series classifiers including the new Hydrant combination can reduce energy consumption by up to 80% while keeping accuracy loss below 5%.
arXiv preprint arXiv:2203.03445 (2022)
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SelF-Rocket dynamically selects input representations and pooling operators within random convolution kernel methods for TSC and reports SOTA accuracy on UCR datasets.
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Pruning Extensions and Efficiency Trade-Offs for Sustainable Time Series Classification
Pruning hybrid time series classifiers including the new Hydrant combination can reduce energy consumption by up to 80% while keeping accuracy loss below 5%.
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Time series classification with random convolution kernels: pooling operators and input representations matter
SelF-Rocket dynamically selects input representations and pooling operators within random convolution kernel methods for TSC and reports SOTA accuracy on UCR datasets.