# Beyond Point Matching: Evaluating Multiscale Dubuc Distance for Time Series Similarity

arXiv: https://arxiv.org/abs/2510.21824 · HTML: https://mlsanity.com/paper/2510.21824/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 21 Oct 2025.

## Builds on (most cited first, 7 of 7)

- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
- [Time-Series Representation Learning via Temporal and Contextual Contrasting](https://mlsanity.com/paper/2106.14112/) (2021)
- [Soft-DTW: a Differentiable Loss Function for Time-Series](https://mlsanity.com/paper/1703.01541/) (2017)
- [Unsupervised Scalable Representation Learning for Multivariate Time Series](https://mlsanity.com/paper/1901.10738/) (2019)
- [How to Train Your Flare Prediction Model: Revisiting Robust Sampling of Rare Events](https://mlsanity.com/paper/2103.07542/) (2021)
- [Feature Selection on a Flare Forecasting Testbed: A Comparative Study of 24 Methods](https://mlsanity.com/paper/2109.14770/) (2021)
- [Multiscale Dubuc: A New Similarity Measure for Time Series](https://mlsanity.com/paper/2411.10418/) (2024)

## Abstract (arXiv)

Time series are high-dimensional and complex data objects, making their efficient search and indexing a longstanding challenge in data mining. Building on a recently introduced similarity measure, namely Multiscale Dubuc Distance (MDD), this paper investigates its comparative strengths and limitations relative to the widely used Dynamic Time Warping (DTW). MDD is novel in two key ways: it evaluates time series similarity across multiple temporal scales and avoids point-to-point alignment. We demonstrate that in many scenarios where MDD outperforms DTW, the gains are substantial, and we provide a detailed analysis of the specific performance gaps it addresses. We provide simulations, in addition to the 95 datasets from the UCR archive, to test our hypotheses. Finally, we apply both methods to a challenging real-world classification task and show that MDD yields a significant improvement over DTW, underscoring its practical utility.