Fast Gradient-Based Approximation of Continuous Dynamic Time Warping
Dynamic Time Warping (DTW) is a common choice for measuring the discrepancy between time series. It proceeds by finding an optimal warping between timestamps through dynamic programming, which can be slow and is not differentiable. Consequently, several variants have been proposed. Among them, Soft-DTW proposes a smooth formulation of DTW, amenable to gradient computation, while Continuous-DTW (CDTW) introduces a continuous formulation through parametrizable warping functions. Yet, computational costs remain high for both methods and impractical for large datasets in modern machine learning pipelines. This paper revisits CDTW in several ways. After establishing its differentiability under...
| Authors | Pierre Houedry, Romain Tavenard, Nicolas Courty et al. |
|---|---|
| Primary category | stat.ML |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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