{"url":"/task/time-series-clustering","name":"Time Series Clustering","slug":"time-series-clustering","description_markdown":"**Time Series Clustering** is an unsupervised data mining technique for organizing data points into groups based on their similarity. The objective is to maximize data similarity within clusters and minimize it across clusters. Time-series clustering is often used as a subroutine of other more complex algorithms and is employed as a standard tool in data science for anomaly detection, character recognition, pattern discovery, visualization of time series.\r\n\r\n\r\n<span class=\"description-source\">Source: [Comprehensive Process Drift Detection with Visual Analytics ](https://arxiv.org/abs/1907.06386)</span>","categories":[{"name":"Time Series","url":"/area/time-series"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":113,"papers_with_code":36,"benchmarks":1,"benchmark_tables_in_archive":1,"benchmark_tables_shown":1,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":5,"subtasks":0,"parent_tasks":1},"benchmarks":[{"leaderboard":"/sota/time-series-clustering-on-eicu-collaborative","slug":"time-series-clustering-on-eicu-collaborative","dataset":"eICU Collaborative Research Database","dataset_url":"/dataset/eicu-crd","rows_in_archive":3,"metrics":["NMI (physiology_6_hours)","NMI (physiology_12_hours)","NMI (physiology_24_hours)"],"first_row_in_archive_order":{"model":"SOM-VAE-prob","paper_title":"SOM-VAE: Interpretable Discrete Representation Learning on Time Series","paper_url":"/paper/som-vae-interpretable-discrete-representation","paper_date":"2018-06-06","arxiv_id":"1806.02199","code_links":[{"title":"ratschlab/SOM-VAE","url":"https://github.com/ratschlab/SOM-VAE"},{"title":"KurochkinAlexey/SOM-VAE","url":"https://github.com/KurochkinAlexey/SOM-VAE"},{"title":"ai-how/TIme-series-clustering","url":"https://github.com/ai-how/TIme-series-clustering"},{"title":"alexwndm/state-detection-somvae","url":"https://github.com/alexwndm/state-detection-somvae"},{"title":"merchen911/SOM-VAE","url":"https://github.com/merchen911/SOM-VAE"},{"title":"shrra/minisom","url":"https://github.com/shrra/minisom"}],"syntology":{"n":6,"n_ran":0,"n_unverified":6,"n_pointer_only":0}}}],"datasets":[{"url":"/dataset/ucr-time-series-classification-archive","name":"UCR Time Series Classification Archive","full_name":"UCR Time Series Classification Archive","num_papers_in_archive":42},{"url":"/dataset/edeniss2020","name":"edeniss2020","full_name":"EDEN ISS 2020 Telemetry Dataset","num_papers_in_archive":2},{"url":"/dataset/bosch-cnc-machining-dataset","name":"Bosch CNC Machining Dataset","full_name":"","num_papers_in_archive":1},{"url":"/dataset/csts","name":"CSTS","full_name":"Correlation Structures in Time Series","num_papers_in_archive":1},{"url":"/dataset/drosophila-immunity-time-course-data","name":"Drosophila Immunity Time-Course Data","full_name":"","num_papers_in_archive":1}],"subtasks":[],"parent_tasks":[{"url":"/task/time-series","name":"Time Series Analysis"}],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":30,"of":36,"tagged_in_all":113,"items":[{"url":"/paper/som-vae-interpretable-discrete-representation","title":"SOM-VAE: Interpretable Discrete Representation Learning on Time Series","date":"2018-06-06","arxiv_id":"1806.02199","repositories_listed":6,"syntology":{"n":6,"n_ran":0,"n_unverified":6,"n_pointer_only":0}},{"url":"/paper/pypots-a-python-toolbox-for-data-mining-on","title":"PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series","date":"2023-05-30","arxiv_id":"2305.18811","repositories_listed":5,"syntology":{"n":21,"n_ran":0,"n_unverified":21,"n_pointer_only":0}},{"url":"/paper/n2dnot-too-deep-clustering-via-clustering-the","title":"N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding","date":"2019-08-16","arxiv_id":"1908.05968","repositories_listed":5,"syntology":{"n":4,"n_ran":2,"n_unverified":2,"n_pointer_only":2}},{"url":"/paper/forecasting-across-time-series-databases","title":"Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering Approach","date":"2017-10-09","arxiv_id":"1710.03222","repositories_listed":3,"syntology":null},{"url":"/paper/learning-representations-for-time-series","title":"Learning Representations for Time Series Clustering","date":"2019-12-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/deep-learning-for-clustering-of-multivariate","title":"Deep learning for clustering of multivariate clinical patient trajectories with missing values","date":"2019-11-15","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/variational-psom-deep-probabilistic","title":"DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps","date":"2019-10-03","arxiv_id":"1910.01590","repositories_listed":2,"syntology":null},{"url":"/paper/csts-a-benchmark-for-the-discovery-of","title":"CSTS: A Benchmark for the Discovery of Correlation Structures in Time Series Clustering","date":"2025-05-20","arxiv_id":"2505.14596","repositories_listed":1,"syntology":null},{"url":"/paper/k-graph-a-graph-embedding-for-interpretable","title":"$k$-Graph: A Graph Embedding for Interpretable Time Series Clustering","date":"2025-02-18","arxiv_id":"2502.13049","repositories_listed":1,"syntology":null},{"url":"/paper/time-series-clustering-with-general-state","title":"Time Series Clustering with General State Space Models via Stochastic Variational Inference","date":"2024-06-29","arxiv_id":"2407.00429","repositories_listed":1,"syntology":null},{"url":"/paper/unraveling-anomalies-in-time-unsupervised","title":"Unraveling Anomalies in Time: Unsupervised Discovery and Isolation of Anomalous Behavior in Bio-regenerative Life Support System Telemetry","date":"2024-06-14","arxiv_id":"2406.09825","repositories_listed":1,"syntology":null},{"url":"/paper/shapedba-generating-effective-time-series","title":"ShapeDBA: Generating Effective Time Series Prototypes using ShapeDTW Barycenter Averaging","date":"2023-09-28","arxiv_id":"2309.16353","repositories_listed":1,"syntology":null},{"url":"/paper/graph-based-time-series-clustering-for-end-to","title":"Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting","date":"2023-05-30","arxiv_id":"2305.19183","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_unverified":2,"n_pointer_only":0}},{"url":"/paper/time-series-clustering-with-random","title":"Time Series Clustering With Random Convolutional Kernels","date":"2023-05-17","arxiv_id":"2305.10457","repositories_listed":1,"syntology":null},{"url":"/paper/the-conditional-cauchy-schwarz-divergence","title":"The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making","date":"2023-01-21","arxiv_id":"2301.08970","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_unverified":1,"n_pointer_only":2}},{"url":"/paper/unsupervised-4d-lidar-moving-object","title":"Unsupervised 4D LiDAR Moving Object Segmentation in Stationary Settings with Multivariate Occupancy Time Series","date":"2022-12-30","arxiv_id":"2212.14750","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-dtw-for-time-series-and-sequences","title":"Uncertainty-DTW for Time Series and Sequences","date":"2022-10-30","arxiv_id":"2211.00005","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_unverified":3,"n_pointer_only":7}},{"url":"/paper/time-series-clustering-with-an-em-algorithm","title":"Time Series Clustering with an EM algorithm for Mixtures of Linear Gaussian State Space Models","date":"2022-08-25","arxiv_id":"2208.11907","repositories_listed":1,"syntology":null},{"url":"/paper/smart-data-collection-system-for-brownfield","title":"Smart Data Collection System for Brownfield CNC Milling Machines: A New Benchmark Dataset for Data-Driven Machine Monitoring","date":"2022-06-29","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/tnn7-a-custom-macro-suite-for-implementing","title":"TNN7: A Custom Macro Suite for Implementing Highly Optimized Designs of Neuromorphic TNNs","date":"2022-05-16","arxiv_id":"2205.07410","repositories_listed":1,"syntology":null},{"url":"/paper/novel-features-for-time-series-analysis-a","title":"Novel Features for Time Series Analysis: A Complex Networks Approach","date":"2021-10-11","arxiv_id":"2110.09888","repositories_listed":1,"syntology":null},{"url":"/paper/learning-representations-for-incomplete-time","title":"Learning Representations for Incomplete Time Series Clustering","date":"2021-05-18","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/algorithms-for-learning-graphs-in-financial","title":"Algorithms for Learning Graphs in Financial Markets","date":"2020-12-31","arxiv_id":"2012.15410","repositories_listed":1,"syntology":null},{"url":"/paper/k-means-on-a-log-cholesky-manifold-with","title":"$k$-means on Positive Definite Matrices, and an Application to Clustering in Radar Image Sequences","date":"2020-08-08","arxiv_id":"2008.03454","repositories_listed":1,"syntology":null},{"url":"/paper/conditional-latent-block-model-a-multivariate","title":"Conditional Latent Block Model: a Multivariate Time Series Clustering Approach for Autonomous Driving Validation","date":"2020-08-03","arxiv_id":"2008.00946","repositories_listed":1,"syntology":null},{"url":"/paper/temporal-phenotyping-using-deep-predictive","title":"Temporal Phenotyping using Deep Predictive Clustering of Disease Progression","date":"2020-06-15","arxiv_id":"2006.08600","repositories_listed":1,"syntology":null},{"url":"/paper/automating-cluster-analysis-to-generate","title":"Clustering Residential Electricity Consumption Data to Create Archetypes that Capture Household Behaviour in South Africa","date":"2020-06-11","arxiv_id":"2006.07197","repositories_listed":1,"syntology":null},{"url":"/paper/deep-markov-spatio-temporal-factorization","title":"Deep Markov Spatio-Temporal Factorization","date":"2020-03-22","arxiv_id":"2003.09779","repositories_listed":1,"syntology":null},{"url":"/paper/interpreting-lstm-prediction-on-solar-flare","title":"Interpreting LSTM Prediction on Solar Flare Eruption with Time-series Clustering","date":"2019-12-27","arxiv_id":"1912.12360","repositories_listed":1,"syntology":null},{"url":"/paper/a-time-resolved-clustering-method-revealing","title":"A time resolved clustering method revealing longterm structures and their short-term internal dynamics","date":"2019-12-09","arxiv_id":"1912.04261","repositories_listed":1,"syntology":null}],"syntology_records":6,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}