{"url":"/dataset/uea-time-series-datasets","name":"UEA time-series datasets","full_name":"UEA time-series datasets for series-level anomaly detection","description_markdown":"Five datasets used in NeurTraL-AD paper: \\textit{RacketSports (RS).} Accelerometer and gyroscope recording of players playing four different racket sports. Each sport is designated as a different class. \\textit{Epilepsy (EPSY).} Accelerometer recording of healthy actors simulating four different activity classes, one of them being an epileptic shock. \\textit{Naval air training and operating procedures standardization (NAT).} Positions of sensors mounted on different body parts of a person performing activities. There are six different activity classes in the dataset. \\textit{Character trajectories (CT).} Velocity trajectories of a pen on a WACOM tablet. There are $20$ different characters in this dataset.  \\textit{Spoken Arabic Digits (SAD).} MFCC features of ten arabic digits spoken by $88$ different speakers.","description_withheld":null,"homepage":"http://www.timeseriesclassification.com/","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"Anomaly Detection","url":"/task/anomaly-detection","datasets_with_task":"/datasets/task/anomaly-detection"}],"languages":[],"variants":["UEA time-series datasets"],"data_loaders":[{"repo":"https://github.com/NivC/SINBAD","url":"https://github.com/NivC/SINBAD","frameworks":[]}],"num_papers_in_archive":10,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/anomaly-detection-on-uea-time-series-datasets","task":"Anomaly Detection","dataset_variant":"UEA time-series datasets","rows":3,"metrics":["Avg. ROC-AUC"],"first_row_in_archive_order":{"model":"SINBAD","paper":"/paper/set-features-for-fine-grained-anomaly","metrics":{"Avg. ROC-AUC":"96.8"},"code_links":[{"title":"NivC/SINBAD","url":"https://github.com/NivC/SINBAD"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/set-features-for-fine-grained-anomaly","title":"Set Features for Fine-grained Anomaly Detection","date":"2023-02-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/classification-based-anomaly-detection-for-1","title":"Classification-Based Anomaly Detection for General Data","date":"2020-05-05","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":5,"samples_unverified":5,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/drocc-deep-robust-one-class-classification","title":"DROCC: Deep Robust One-Class Classification","date":"2020-02-28","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":11,"samples_ran":6,"samples_unverified":5,"pointer_only_for_licence":5,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}