# Rock the KASBA: Blazingly Fast and Accurate Time Series Clustering

arXiv: https://arxiv.org/abs/2411.17838 · HTML: https://mlsanity.com/paper/2411.17838/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 86 references that are in the map.
- First posted on arXiv 26 Nov 2024.

## Builds on (most cited first, 25 of 25)

- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
- [ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels](https://mlsanity.com/paper/1910.13051/) (2019)
- [Soft-DTW: a Differentiable Loss Function for Time-Series](https://mlsanity.com/paper/1703.01541/) (2017)
- [MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification](https://mlsanity.com/paper/2012.08791/) (2020)
- [Should we really use post-hoc tests based on mean-ranks?](https://mlsanity.com/paper/1505.02288/) (2015)
- [Bake off redux: a review and experimental evaluation of recent time series classification algorithms](https://mlsanity.com/paper/2304.13029/) (2023)
- [aeon: a Python toolkit for learning from time series](https://mlsanity.com/paper/2406.14231/) (2024)
- [A review on distance based time series classification](https://mlsanity.com/paper/1806.04509/) (2018)
- [shapeDTW: shape Dynamic Time Warping](https://mlsanity.com/paper/1606.01601/) (2016)
- [An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Set](https://mlsanity.com/paper/2305.11921/) (2023)
- [A Benchmark Study on Time Series Clustering](https://mlsanity.com/paper/2004.09546/) (2020)
- [A Review and Evaluation of Elastic Distance Functions for Time Series Clustering](https://mlsanity.com/paper/2205.15181/) (2022)
- [Nonsmooth Analysis and Subgradient Methods for Averaging in Dynamic Time Warping Spaces](https://mlsanity.com/paper/1701.06393/) (2017)
- [Elastic Similarity and Distance Measures for Multivariate Time Series](https://mlsanity.com/paper/2102.10231/) (2021)
- [Scalable Classifier-Agnostic Channel Selection for Multivariate Time Series Classification](https://mlsanity.com/paper/2206.09274/) (2022)
- [Time Series Clustering With Random Convolutional Kernels](https://mlsanity.com/paper/2305.10457/) (2023)
- [ShapeDBA: Generating Effective Time Series Prototypes using ShapeDTW Barycenter Averaging](https://mlsanity.com/paper/2309.16353/) (2023)
- [Exact Mean Computation in Dynamic Time Warping Spaces](https://mlsanity.com/paper/1710.08937/) (2017)
- [Understanding partition comparison indices based on counting object pairs](https://mlsanity.com/paper/1901.01777/) (2019)
- [Discovering patterns of online popularity from time series](https://mlsanity.com/paper/1904.04994/) (2019)
- [A General Framework for Density Based Time Series Clustering Exploiting a Novel Admissible Pruning Strategy](https://mlsanity.com/paper/1612.00637/) (2016)
- [SOMTimeS: Self Organizing Maps for Time Series Clustering and its Application to Serious Illness Conversations](https://mlsanity.com/paper/2108.11523/) (2021)
- [On time series clustering with k-means](https://mlsanity.com/paper/2410.14269/) (2024)
- [Agglomerative Hierarchical Clustering with Dynamic Time Warping for Household Load Curve Clustering](https://mlsanity.com/paper/2210.09523/) (2022)
- [Towards Personalised Patient Risk Prediction Using Temporal Hospital Data Trajectories](https://mlsanity.com/paper/2407.09373/) (2024)

## Built on by (most cited first, 2 of 2)

- [Soft-MSM: Differentiable Context-Aware Elastic Alignment for Time Series](https://mlsanity.com/paper/2605.00069/) (2026)
- [CLUES-WEASEL: No additional clues required to choose your time series clustering algorithm](https://mlsanity.com/paper/2609.07606/) (2026)

## Abstract (arXiv)

Time series data has become increasingly prevalent across numerous domains, driving a growing demand for time series machine learning techniques. Among these, time series clustering (TSCL) stands out as one of the most popular machine learning tasks. TSCL serves as a powerful exploratory analysis tool and is also employed as a preprocessing step or subroutine for various tasks, including anomaly detection, segmentation, and classification. The most popular TSCL algorithms are either fast (in terms of run time) but perform poorly on benchmark problems, or perform well on benchmarks but scale poorly. We present a new TSCL algorithm, the k-means (K) accelerated (A) Stochastic subgradient (S) Barycentre (B) Average (A) (KASBA) clustering algorithm. KASBA is a k-means clustering algorithm that uses the Move-Split-Merge (MSM) elastic distance at all stages of clustering, applies a randomised stochastic subgradient gradient descent to find barycentre centroids, links each stage of clustering to accelerate convergence and exploits the metric property of MSM distance to avoid a large proportion of distance calculations. It is a versatile and scalable clusterer designed for real-world TSCL applications. It allows practitioners to balance run time and clustering performance. We demonstrate through extensive experimentation that KASBA produces significantly better clustering than the faster state of the art clusterers and is offers orders of magnitude improvement in run time over the most performant k-means alternatives.