# A Benchmark Study on Time Series Clustering

arXiv: https://arxiv.org/abs/2004.09546 · HTML: https://mlsanity.com/paper/2004.09546/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2020.

## Built on by (most cited first, 25 of 25)

- [TSGBench: Time Series Generation Benchmark](https://mlsanity.com/paper/2309.03755/) (2023)
- [A Review and Evaluation of Elastic Distance Functions for Time Series Clustering](https://mlsanity.com/paper/2205.15181/) (2022)
- [Unsupervised Deep Learning for IoT Time Series](https://mlsanity.com/paper/2302.03284/) (2023)
- [Pathways through Conspiracy: The Evolution of Conspiracy Radicalization through Engagement in Online Conspiracy Discussions](https://mlsanity.com/paper/2204.10729/) (2022)
- [Time Series Clustering With Random Convolutional Kernels](https://mlsanity.com/paper/2305.10457/) (2023)
- [Detecting CAN Masquerade Attacks with Signal Clustering Similarity](https://mlsanity.com/paper/2201.02665/) (2022)
- [FCPCA: Fuzzy clustering of high-dimensional time series based on common principal component analysis](https://mlsanity.com/paper/2505.07276/) (2025)
- [When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development](https://mlsanity.com/paper/2209.01083/) (2022)
- [LoCoMotif: Discovering time-warped motifs in time series](https://mlsanity.com/paper/2311.17582/) (2023)
- [SOMTimeS: Self Organizing Maps for Time Series Clustering and its Application to Serious Illness Conversations](https://mlsanity.com/paper/2108.11523/) (2021)
- [On the Use of Relative Validity Indices for Comparing Clustering Approaches](https://mlsanity.com/paper/2404.10351/) (2024)
- [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)
- [Time Series Clustering for Grouping Products Based on Price and Sales Patterns](https://mlsanity.com/paper/2204.08334/) (2022)
- [Dimension Reduction for time series with Variational AutoEncoders](https://mlsanity.com/paper/2204.11060/) (2022)
- [Clustering individuals based on multivariate EMA time-series data](https://mlsanity.com/paper/2212.01159/) (2022)
- [Quantitative Evaluation of Motif Sets in Time Series](https://mlsanity.com/paper/2412.09346/) (2024)
- [CSTS: A Benchmark for the Discovery of Correlation Structures in Time Series Clustering](https://mlsanity.com/paper/2505.14596/) (2025)
- [CLUBench: A Clustering Benchmark](https://mlsanity.com/paper/2605.29933/) (2026)
- [Applications of Machine Learning in Pharmacogenomics: Clustering Plasma Concentration-Time Curves](https://mlsanity.com/paper/2210.13310/) (2022)
- [TCGAN: Convolutional Generative Adversarial Network for Time Series Classification and Clustering](https://mlsanity.com/paper/2309.04732/) (2023)
- [A Spatiotemporal Deep Neural Network for Fine-Grained Multi-Horizon Wind Prediction](https://mlsanity.com/paper/2309.04733/) (2023)
- [Can time series forecasting be automated? A benchmark and analysis](https://mlsanity.com/paper/2407.16445/) (2024)
- [CROCS: A Two-Stage Clustering Framework for Behaviour-Centric Consumer Segmentation with Smart Meter Data](https://mlsanity.com/paper/2601.10494/) (2026)
- [VLM4Cluster: Benchmarking Deep Clustering In the Era of Vision-Language Pre-training](https://mlsanity.com/paper/2609.36648/) (2026)

## Abstract (arXiv)

This paper presents the first time series clustering benchmark utilizing all time series datasets currently available in the University of California Riverside (UCR) archive -- the state of the art repository of time series data. Specifically, the benchmark examines eight popular clustering methods representing three categories of clustering algorithms (partitional, hierarchical and density-based) and three types of distance measures (Euclidean, dynamic time warping, and shape-based). We lay out six restrictions with special attention to making the benchmark as unbiased as possible. A phased evaluation approach was then designed for summarizing dataset-level assessment metrics and discussing the results. The benchmark study presented can be a useful reference for the research community on its own; and the dataset-level assessment metrics reported may be used for designing evaluation frameworks to answer different research questions.