# ShapeDBA: Generating Effective Time Series Prototypes using ShapeDTW Barycenter Averaging

arXiv: https://arxiv.org/abs/2309.16353 · HTML: https://mlsanity.com/paper/2309.16353/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 28 Sep 2023.

## Builds on (most cited first, 9 of 9)

- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
- [Soft-DTW: a Differentiable Loss Function for Time-Series](https://mlsanity.com/paper/1703.01541/) (2017)
- [Should we really use post-hoc tests based on mean-ranks?](https://mlsanity.com/paper/1505.02288/) (2015)
- [Data augmentation using synthetic data for time series classification with deep residual networks](https://mlsanity.com/paper/1808.02455/) (2018)
- [shapeDTW: shape Dynamic Time Warping](https://mlsanity.com/paper/1606.01601/) (2016)
- [Explaining Deep Classification of Time-Series Data with Learned Prototypes](https://mlsanity.com/paper/1904.08935/) (2019)
- [An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Set](https://mlsanity.com/paper/2305.11921/) (2023)
- [A Review and Evaluation of Elastic Distance Functions for Time Series Clustering](https://mlsanity.com/paper/2205.15181/) (2022)
- [Time series classification for varying length series](https://mlsanity.com/paper/1910.04341/) (2019)

## Built on by (most cited first, 6 of 6)

- [aeon: a Python toolkit for learning from time series](https://mlsanity.com/paper/2406.14231/) (2024)
- [Finding Foundation Models for Time Series Classification with a PreText Task](https://mlsanity.com/paper/2311.14534/) (2023)
- [On time series clustering with k-means](https://mlsanity.com/paper/2410.14269/) (2024)
- [Rock the KASBA: Blazingly Fast and Accurate Time Series Clustering](https://mlsanity.com/paper/2411.17838/) (2024)
- [An Attentive Representative Sample Selection Strategy Combined with Balanced Batch Training for Skin Lesion Segmentation](https://mlsanity.com/paper/2503.17034/) (2025)
- [Soft-MSM: Differentiable Context-Aware Elastic Alignment for Time Series](https://mlsanity.com/paper/2605.00069/) (2026)

## Abstract (arXiv)

Time series data can be found in almost every domain, ranging from the medical field to manufacturing and wireless communication. Generating realistic and useful exemplars and prototypes is a fundamental data analysis task. In this paper, we investigate a novel approach to generating realistic and useful exemplars and prototypes for time series data. Our approach uses a new form of time series average, the ShapeDTW Barycentric Average. We therefore turn our attention to accurately generating time series prototypes with a novel approach. The existing time series prototyping approaches rely on the Dynamic Time Warping (DTW) similarity measure such as DTW Barycentering Average (DBA) and SoftDBA. These last approaches suffer from a common problem of generating out-of-distribution artifacts in their prototypes. This is mostly caused by the DTW variant used and its incapability of detecting neighborhood similarities, instead it detects absolute similarities. Our proposed method, ShapeDBA, uses the ShapeDTW variant of DTW, that overcomes this issue. We chose time series clustering, a popular form of time series analysis to evaluate the outcome of ShapeDBA compared to the other prototyping approaches. Coupled with the k-means clustering algorithm, and evaluated on a total of 123 datasets from the UCR archive, our proposed averaging approach is able to achieve new state-of-the-art results in terms of Adjusted Rand Index.