mlsanityData to 7 Oct 2026

A Benchmark Study on Time Series Clustering

arXiv 2004.09546 ↗First posted 20 Apr 2020cs.LG stat.MLcited by 25

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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.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Apr 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 8; 2023: 6; 2024: 5; 2025: 2; 2026: 3.

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  1. 2023TSGBench: Time Series Generation Benchmarkcited by 24
  2. 2022A Review and Evaluation of Elastic Distance Functions for Time Series Clusteringcited by 14
  3. 2023Unsupervised Deep Learning for IoT Time Seriescited by 11
  4. 2022Pathways through Conspiracy: The Evolution of Conspiracy Radicalization through Engagement in Online Conspiracy Discussionscited by 8
  5. 2023Time Series Clustering With Random Convolutional Kernelscited by 6
  6. 2022Detecting CAN Masquerade Attacks with Signal Clustering Similaritycited by 5
  7. 2025FCPCA: Fuzzy clustering of high-dimensional time series based on common principal component analysiscited by 5
  8. 2022When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Developmentcited by 4
  9. 2023LoCoMotif: Discovering time-warped motifs in time seriescited by 3
  10. 2021SOMTimeS: Self Organizing Maps for Time Series Clustering and its Application to Serious Illness Conversationscited by 2
  11. 2024On the Use of Relative Validity Indices for Comparing Clustering Approachescited by 2
  12. 2024On time series clustering with k-meanscited by 2
13 more
  1. 2022Time Series Clustering for Grouping Products Based on Price and Sales Patternscited by 1
  2. 2022Dimension Reduction for time series with Variational AutoEncoderscited by 1
  3. 2022Clustering individuals based on multivariate EMA time-series datacited by 1
  4. 2024Rock the KASBA: Blazingly Fast and Accurate Time Series Clusteringcited by 1
  5. 2024Quantitative Evaluation of Motif Sets in Time Seriescited by 1
  6. 2025CSTS: A Benchmark for the Discovery of Correlation Structures in Time Series Clusteringcited by 1
  7. 2026CLUBench: A Clustering Benchmarkcited by 1
  8. 2022Applications of Machine Learning in Pharmacogenomics: Clustering Plasma Concentration-Time Curvescited by 0
  9. 2023TCGAN: Convolutional Generative Adversarial Network for Time Series Classification and Clusteringcited by 0
  10. 2023A Spatiotemporal Deep Neural Network for Fine-Grained Multi-Horizon Wind Predictioncited by 0
  11. 2024Can time series forecasting be automated? A benchmark and analysiscited by 0
  12. 2026CROCS: A Two-Stage Clustering Framework for Behaviour-Centric Consumer Segmentation with Smart Meter Datacited by 0
  13. 2026VLM4Cluster: Benchmarking Deep Clustering In the Era of Vision-Language Pre-trainingcited by 0