Soft-MSM: Differentiable Context-Aware Elastic Alignment for Time Series

This research introduces Soft-MSM, a novel differentiable elastic alignment loss for time series, building upon the Move-Split-Merge (MSM) distance. Soft-MSM addresses the limitation of Soft-DTW by incorporating context-aware transition costs, making it suitable for gradient-based optimization in machine learning tasks like classification and clustering.