{"url":"/sota/change-point-detection-on-tep","task":{"name":"Change Point Detection","url":"/task/change-point-detection","note":null},"dataset":{"name":"TEP","url":"/dataset/tep"},"category":"Time Series","categories":["Time Series"],"category_note":null,"description":"**Change Point Detection** is concerned with the accurate detection of abrupt and significant changes in the behavior of a time series.\r\n\r\nChange point detection is the task of finding changes in the underlying model of a signal or time series. They are two main methods: \r\n\r\n1) Online methods, that aim to detect changes as soon as they occur in a real-time setting\r\n\r\n2) Offline methods that retrospectively detect changes when all samples are received.\r\n\r\nSource: [Selective review of offline change point detection methods](https://arxiv.org/pdf/1801.00718.pdf)","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["NAB (standard)","NAB (lowFP)","NAB (LowFN)"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"NAB (standard)":null,"NAB (lowFP)":null,"NAB (LowFN)":null}},"counts":{"rows":6,"rows_with_code":6,"rows_with_paper_page":6,"rows_dated":6,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"OptEnsemble CPDE algorithm (Min+MinMax/Rank)","metrics":{"NAB (LowFN)":"42.16","NAB (lowFP)":"41","NAB (standard)":"41.81"},"uses_additional_data":false,"paper_date":"2021-05-09","paper":"/paper/unsupervised-offline-changepoint-detection","paper_url":"https://www.mdpi.com/2076-3417/11/9/4280?utm_source=TrendMD&utm_medium=cpc&utm_campaign=Appl_Sci_TrendMD_0","paper_title":"Unsupervised Offline Changepoint Detection Ensembles","code":"https://github.com/YKatser/CPDE","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"BinSegEnsemble CPDE algorithm (Min+MinMax/Rank)","metrics":{"NAB (LowFN)":"42.16","NAB (lowFP)":"41","NAB (standard)":"41.81"},"uses_additional_data":false,"paper_date":"2021-05-09","paper":"/paper/unsupervised-offline-changepoint-detection","paper_url":"https://www.mdpi.com/2076-3417/11/9/4280?utm_source=TrendMD&utm_medium=cpc&utm_campaign=Appl_Sci_TrendMD_0","paper_title":"Unsupervised Offline Changepoint Detection Ensembles","code":"https://github.com/YKatser/CPDE","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"BinSeg CPD algorithm (Mahalanobis metric)","metrics":{"NAB (LowFN)":"37.29","NAB (lowFP)":"35.82","NAB (standard)":"36.88"},"uses_additional_data":false,"paper_date":"2021-05-09","paper":"/paper/unsupervised-offline-changepoint-detection","paper_url":"https://www.mdpi.com/2076-3417/11/9/4280?utm_source=TrendMD&utm_medium=cpc&utm_campaign=Appl_Sci_TrendMD_0","paper_title":"Unsupervised Offline Changepoint Detection Ensembles","code":"https://github.com/YKatser/CPDE","n_code_links":1,"syntology":null},{"rank_in_archive_order":4,"model":"Opt CPD algorithm (Mahalanobis metric)","metrics":{"NAB (LowFN)":"37.29","NAB (lowFP)":"35.82","NAB (standard)":"36.88"},"uses_additional_data":false,"paper_date":"2021-05-09","paper":"/paper/unsupervised-offline-changepoint-detection","paper_url":"https://www.mdpi.com/2076-3417/11/9/4280?utm_source=TrendMD&utm_medium=cpc&utm_campaign=Appl_Sci_TrendMD_0","paper_title":"Unsupervised Offline Changepoint Detection Ensembles","code":"https://github.com/YKatser/CPDE","n_code_links":1,"syntology":null},{"rank_in_archive_order":5,"model":"Win CPD algorithm (Mahalanobis metric)","metrics":{"NAB (LowFN)":"28.05","NAB (lowFP)":"27","NAB (standard)":"27.79"},"uses_additional_data":false,"paper_date":"2021-05-09","paper":"/paper/unsupervised-offline-changepoint-detection","paper_url":"https://www.mdpi.com/2076-3417/11/9/4280?utm_source=TrendMD&utm_medium=cpc&utm_campaign=Appl_Sci_TrendMD_0","paper_title":"Unsupervised Offline Changepoint Detection Ensembles","code":"https://github.com/YKatser/CPDE","n_code_links":1,"syntology":null},{"rank_in_archive_order":6,"model":"WinEnsemble CPDE algorithm (WeightedSum+MinAbs)","metrics":{"NAB (LowFN)":"26.29","NAB (lowFP)":"24.33","NAB (standard)":"25.14"},"uses_additional_data":false,"paper_date":"2021-05-09","paper":"/paper/unsupervised-offline-changepoint-detection","paper_url":"https://www.mdpi.com/2076-3417/11/9/4280?utm_source=TrendMD&utm_medium=cpc&utm_campaign=Appl_Sci_TrendMD_0","paper_title":"Unsupervised Offline Changepoint Detection Ensembles","code":"https://github.com/YKatser/CPDE","n_code_links":1,"syntology":null}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. Not reviewed by the paper's authors or by the archive's editors, and not ranked against the archive rows.","extraction_file_present":true,"measurement":{"test_papers":883,"papers_with_output":881,"judged_true":108,"judged":110,"wilson95_lower":0.9361,"measured_on":"2026-09-24","frozen_commit":"0e3de0df94"},"measurement_note":"blind adjudication of accepted entries on a held-out split of archive papers, rules frozen before the test","coverage":{"sentence":"Syntology has checked 6,885 of the 9,623 papers on this site that are newer than the archive; results from the others appear after they are checked.","complete":false,"papers_newer_than_archive":9623,"papers_checked":6885,"papers_extracted_not_yet_verified":0,"boards_without_verdict":2,"papers_not_yet_extracted":2737},"order":"newest first by month (arXiv date, else the arXiv-id month), then arXiv id descending","columns":[],"entries":[]},"syntology":{"read_at":"2026-09-25T09:33:49+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}