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.
Data Mining and Knowledge Discovery , year =
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Framework for dataset subset selection via clustering, A/D-optimality, and FAFI with bootstrap intervals to preserve model rankings, showing high Spearman correlation (0.95 with 5 datasets) in TSC but limited gains in recommender systems.
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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.
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Benchmarking on Tasks That Matter: Dataset Selection for Preserving Model Rankings
Framework for dataset subset selection via clustering, A/D-optimality, and FAFI with bootstrap intervals to preserve model rankings, showing high Spearman correlation (0.95 with 5 datasets) in TSC but limited gains in recommender systems.