Soft-MSM is a smooth, gradient-enabled version of the context-aware MSM distance for time series alignment that outperforms Soft-DTW alternatives in clustering and nearest-centroid classification.
arXiv preprint arXiv:2305.11921 (2023)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2verdicts
UNVERDICTED 2representative citing papers
SelF-Rocket dynamically selects input representations and pooling operators within random convolution kernel methods for TSC and reports SOTA accuracy on UCR datasets.
citing papers explorer
-
Soft-MSM: Differentiable Context-Aware Elastic Alignment for Time Series
Soft-MSM is a smooth, gradient-enabled version of the context-aware MSM distance for time series alignment that outperforms Soft-DTW alternatives in clustering and nearest-centroid classification.
-
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.