{"id":"https://openalex.org/W3109518304","doi":"https://doi.org/10.1145/3442381.3449903","title":"Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding","display_name":"Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding","publication_year":2021,"publication_date":"2021-04-19","ids":{"openalex":"https://openalex.org/W3109518304","doi":"https://doi.org/10.1145/3442381.3449903","mag":"3109518304"},"language":"en","primary_location":{"id":"doi:10.1145/3442381.3449903","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3442381.3449903","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Web Conference 2021","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3442381.3449903","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Shohreh Deldari","orcid":null},"institutions":[{"id":"https://openalex.org/I82951845","display_name":"RMIT University","ror":"https://ror.org/04ttjf776","country_code":"AU","type":"education","lineage":["https://openalex.org/I82951845"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Shohreh Deldari","raw_affiliation_strings":["RMIT University, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RMIT University, Australia","institution_ids":["https://openalex.org/I82951845"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Daniel V. Smith","orcid":null},"institutions":[{"id":"https://openalex.org/I1292875679","display_name":"Commonwealth Scientific and Industrial Research Organisation","ror":"https://ror.org/03qn8fb07","country_code":"AU","type":"government","lineage":["https://openalex.org/I1292875679","https://openalex.org/I2801453606","https://openalex.org/I4387156119"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Daniel V. Smith","raw_affiliation_strings":["CSIRO, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CSIRO, Australia","institution_ids":["https://openalex.org/I1292875679"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Hao Xue","orcid":null},"institutions":[{"id":"https://openalex.org/I82951845","display_name":"RMIT University","ror":"https://ror.org/04ttjf776","country_code":"AU","type":"education","lineage":["https://openalex.org/I82951845"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Hao Xue","raw_affiliation_strings":["RMIT University, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RMIT University, Australia","institution_ids":["https://openalex.org/I82951845"]}]},{"author_position":"last","author":{"id":null,"display_name":"Flora D. Salim","orcid":null},"institutions":[{"id":"https://openalex.org/I82951845","display_name":"RMIT University","ror":"https://ror.org/04ttjf776","country_code":"AU","type":"education","lineage":["https://openalex.org/I82951845"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Flora D. Salim","raw_affiliation_strings":["RMIT University, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RMIT University, Australia","institution_ids":["https://openalex.org/I82951845"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":13.8973,"has_fulltext":false,"cited_by_count":113,"citation_normalized_percentile":{"value":0.99461623,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"3124","last_page":"3135"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13283","display_name":"Mental Health Research Topics","score":0.9897000193595886,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/change-detection","display_name":"Change detection","score":0.7444999814033508},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5379999876022339},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5338000059127808},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.5188999772071838},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5027999877929688},{"id":"https://openalex.org/keywords/predictive-coding","display_name":"Predictive coding","score":0.46860000491142273},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4684000015258789},{"id":"https://openalex.org/keywords/interval","display_name":"Interval (graph theory)","score":0.420199990272522}],"concepts":[{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.7444999814033508},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7084000110626221},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6240000128746033},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5379999876022339},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5338000059127808},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.5188999772071838},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5027999877929688},{"id":"https://openalex.org/C2778061373","wikidata":"https://www.wikidata.org/wiki/Q1315146","display_name":"Predictive coding","level":3,"score":0.46860000491142273},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4684000015258789},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.420199990272522},{"id":"https://openalex.org/C2779466056","wikidata":"https://www.wikidata.org/wiki/Q107630651","display_name":"Time point","level":2,"score":0.4117000102996826},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40049999952316284},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.36730000376701355},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35359999537467957},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.32339999079704285},{"id":"https://openalex.org/C88871306","wikidata":"https://www.wikidata.org/wiki/Q7208287","display_name":"Point process","level":2,"score":0.3077000081539154},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.30219998955726624},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2948000133037567},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2904999852180481},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2825999855995178},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.2680000066757202},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.25540000200271606},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.2549999952316284}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3442381.3449903","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3442381.3449903","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Web Conference 2021","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2011.14097","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2011.14097","pdf_url":"https://arxiv.org/pdf/2011.14097","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3442381.3449903","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3442381.3449903","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Web Conference 2021","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1682770403","https://openalex.org/W1869500417","https://openalex.org/W2019478724","https://openalex.org/W2028563099","https://openalex.org/W2046868034","https://openalex.org/W2073069519","https://openalex.org/W2073401630","https://openalex.org/W2091266471","https://openalex.org/W2096733369","https://openalex.org/W2145343602","https://openalex.org/W2157364932","https://openalex.org/W2304267454","https://openalex.org/W2552544550","https://openalex.org/W2569173133","https://openalex.org/W2583336059","https://openalex.org/W2767470436","https://openalex.org/W2892644395","https://openalex.org/W2907028289","https://openalex.org/W2907410812","https://openalex.org/W2921310091","https://openalex.org/W2945829110","https://openalex.org/W2951866553","https://openalex.org/W2963350250","https://openalex.org/W2963460797","https://openalex.org/W2963853051","https://openalex.org/W2979329071","https://openalex.org/W2983436756","https://openalex.org/W3034514548","https://openalex.org/W3047637857","https://openalex.org/W3083281679","https://openalex.org/W3099025572"],"related_works":[],"abstract_inverted_index":{"Change":[0,64],"Point":[1,65],"Detection":[2],"(CPD)":[3],"methods":[4,150,164],"identify":[5],"the":[6,12,24,28,33,80,147,170,174],"times":[7],"associated":[8,37],"with":[9,38,167],"changes":[10,34],"in":[11,20],"trends":[13],"and":[14,35,137,161],"properties":[15],"of":[16,27,99,101,107,149],"time":[17,102,121],"series":[18,122],"data":[19],"order":[21],"to":[22,83,145,169],"describe":[23],"underlying":[25],"behaviour":[26,46],"system.":[29],"For":[30],"instance,":[31],"detecting":[32],"anomalies":[36],"web":[39],"service":[40],"usage,":[41],"application":[42],"usage":[43],"or":[44,156],"human":[45],"can":[47],"provide":[48],"valuable":[49],"insights":[50],"for":[51,60,89],"downstream":[52],"modelling":[53],"tasks.":[54],"We":[55],"propose":[56],"a":[57,85],"novel":[58],"approach":[59,82],"self-supervised":[61],"Time":[62],"Series":[63],"detection":[66],"method":[67,128],"based":[68],"on":[69,116],"Contrastive":[70],"Predictive":[71],"coding":[72],"(TS":[73],"\u2212":[74,77,141],"CP2).":[75],"TS":[76,140],"CP2":[78,142],"is":[79,143],"first":[81],"employ":[84],"contrastive":[86],"learning":[87,92],"strategy":[88],"CPD":[90,132],"by":[91,159,165],"an":[93],"embedded":[94],"representation":[95],"that":[96,126,151],"separates":[97],"pairs":[98,106],"embeddings":[100,109],"adjacent":[103],"intervals":[104],"from":[105],"interval":[108],"separated":[110],"across":[111,173],"time.":[112],"Through":[113],"extensive":[114],"experiments":[115],"three":[117,175],"diverse,":[118],"widely":[119],"used":[120],"datasets,":[123],"we":[124],"demonstrate":[125],"our":[127],"outperforms":[129],"five":[130],"state-of-the-art":[131],"methods,":[133],"which":[134],"include":[135],"unsupervised":[136],"semi-supervised":[138],"approaches.":[139],"shown":[144],"improve":[146],"performance":[148],"use":[152],"either":[153],"handcrafted":[154],"statistical":[155],"temporal":[157],"features":[158],"79.4%":[160],"deep":[162],"learning-based":[163],"17.0%":[166],"respect":[168],"F1-score":[171],"averaged":[172],"datasets.":[176]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":30},{"year":2024,"cited_by_count":41},{"year":2023,"cited_by_count":20},{"year":2022,"cited_by_count":15},{"year":2021,"cited_by_count":1}],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2020-12-07T00:00:00"}
