{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/the-uea-multivariate-time-series","title":"The UEA multivariate time series classification archive, 2018","arxiv_id":"1811.00075","date":"2018-10-31","proceeding":null,"authors":["Anthony Bagnall","Hoang Anh Dau","Jason Lines","Michael Flynn","James Large","Aaron Bostrom","Paul Southam","Eamonn Keogh"],"abstract":"In 2002, the UCR time series classification archive was first released with\nsixteen datasets. It gradually expanded, until 2015 when it increased in size\nfrom 45 datasets to 85 datasets. In October 2018 more datasets were added,\nbringing the total to 128. The new archive contains a wide range of problems,\nincluding variable length series, but it still only contains univariate time\nseries classification problems. One of the motivations for introducing the\narchive was to encourage researchers to perform a more rigorous evaluation of\nnewly proposed time series classification (TSC) algorithms. It has worked: most\nrecent research into TSC uses all 85 datasets to evaluate algorithmic advances.\nResearch into multivariate time series classification, where more than one\nseries are associated with each class label, is in a position where univariate\nTSC research was a decade ago. Algorithms are evaluated using very few datasets\nand claims of improvement are not based on statistical comparisons. We aim to\naddress this problem by forming the first iteration of the MTSC archive, to be\nhosted at the website www.timeseriesclassification.com. Like the univariate\narchive, this formulation was a collaborative effort between researchers at the\nUniversity of East Anglia (UEA) and the University of California, Riverside\n(UCR). The 2018 vintage consists of 30 datasets with a wide range of cases,\ndimensions and series lengths. For this first iteration of the archive we\nformat all data to be of equal length, include no series with missing data and\nprovide train/test splits.","url_abs":"http://arxiv.org/abs/1811.00075v1","url_pdf":"http://arxiv.org/pdf/1811.00075v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"the-uea-multivariate-time-series","repo_url":"https://github.com/FlorentF9/DeepTemporalClustering","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"the-uea-multivariate-time-series","repo_url":"https://github.com/raneeny/mts2graph","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"the-uea-multivariate-time-series","repo_url":"https://github.com/yuhao-3/ACTLL","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-classification","task_name":"Time Series Classification"}],"methods":[],"datasets_introduced":[{"slug":"eigenworms","name":"EigenWorms","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1811.00075","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.00075"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/raneeny/mts2graph","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/FlorentF9/DeepTemporalClustering","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/yuhao-3/ACTLL","reach":{"status":"ok"}}],"summary":{"unverified":5},"by_repo_kind":{"listed":{"samples":5,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"80173d4125e85944","entry":"cid","repo":"FlorentF9/DeepTemporalClustering","repo_kind":"listed","path":"tsdistances.py","file_url":"https://github.com/FlorentF9/DeepTemporalClustering/blob/HEAD/tsdistances.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"80173d4125e85944"}},{"code_sha256_prefix":"124a21dc643bc4f4","entry":"cluster_acc","repo":"FlorentF9/DeepTemporalClustering","repo_kind":"listed","path":"metrics.py","file_url":"https://github.com/FlorentF9/DeepTemporalClustering/blob/HEAD/metrics.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"124a21dc643bc4f4"}},{"code_sha256_prefix":"96d1d4e0ee4553cd","entry":"cluster_purity","repo":"FlorentF9/DeepTemporalClustering","repo_kind":"listed","path":"metrics.py","file_url":"https://github.com/FlorentF9/DeepTemporalClustering/blob/HEAD/metrics.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"96d1d4e0ee4553cd"}},{"code_sha256_prefix":"f6969e433d15fb2a","entry":"eucl","repo":"FlorentF9/DeepTemporalClustering","repo_kind":"listed","path":"tsdistances.py","file_url":"https://github.com/FlorentF9/DeepTemporalClustering/blob/HEAD/tsdistances.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"f6969e433d15fb2a"}},{"code_sha256_prefix":"31af0d834e8268cb","entry":"roc_auc","repo":"FlorentF9/DeepTemporalClustering","repo_kind":"listed","path":"metrics.py","file_url":"https://github.com/FlorentF9/DeepTemporalClustering/blob/HEAD/metrics.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"31af0d834e8268cb"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}