AI RESEARCH
Soft-MSM: Differentiable Context-Aware Elastic Alignment for Time Series
arXiv CS.LG
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ArXi:2605.00069v1 Announce Type: new Elastic distances like dynamic time warping (DTW) are central to time series machine learning because they compare sequences under local temporal misalignment. Soft-DTW is an adaptation of DTW that can be used as a gradient-based loss by replacing the hard minimum in its dynamic-programming recursion with a smooth relaxation. However, this approach does not directly extend to elastic distances whose transition costs depend on the local alignment context.