{"id":"https://openalex.org/W4206356216","doi":"https://doi.org/10.1109/bigdata52589.2021.9671962","title":"WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data","display_name":"WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W4206356216","doi":"https://doi.org/10.1109/bigdata52589.2021.9671962"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9671962","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671962","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2201.07125","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008887467","display_name":"Kamil Faber","orcid":"https://orcid.org/0000-0003-4221-0017"},"institutions":[{"id":"https://openalex.org/I686019","display_name":"AGH University of Krakow","ror":"https://ror.org/00bas1c41","country_code":"PL","type":"education","lineage":["https://openalex.org/I686019"]},{"id":"https://openalex.org/I4210087266","display_name":"Institute of Computer Science","ror":"https://ror.org/003fvp964","country_code":"PL","type":"facility","lineage":["https://openalex.org/I4210087266","https://openalex.org/I99542240"]}],"countries":["PL"],"is_corresponding":true,"raw_author_name":"Kamil Faber","raw_affiliation_strings":["Institute of Computer Science, AGH University of Science and Technology, Krakow, Poland"],"affiliations":[{"raw_affiliation_string":"Institute of Computer Science, AGH University of Science and Technology, Krakow, Poland","institution_ids":["https://openalex.org/I686019","https://openalex.org/I4210087266"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010914442","display_name":"Roberto Corizzo","orcid":"https://orcid.org/0000-0001-8366-6059"},"institutions":[{"id":"https://openalex.org/I181401687","display_name":"American University","ror":"https://ror.org/052w4zt36","country_code":"US","type":"education","lineage":["https://openalex.org/I181401687"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Roberto Corizzo","raw_affiliation_strings":["Department of Computer Science, American University, Washington, DC, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, American University, Washington, DC, USA","institution_ids":["https://openalex.org/I181401687"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067892569","display_name":"Bart\u0142omiej \u015anie\u017cy\u0144ski","orcid":"https://orcid.org/0000-0002-4206-9052"},"institutions":[{"id":"https://openalex.org/I686019","display_name":"AGH University of Krakow","ror":"https://ror.org/00bas1c41","country_code":"PL","type":"education","lineage":["https://openalex.org/I686019"]},{"id":"https://openalex.org/I4210087266","display_name":"Institute of Computer Science","ror":"https://ror.org/003fvp964","country_code":"PL","type":"facility","lineage":["https://openalex.org/I4210087266","https://openalex.org/I99542240"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Bartlomiej Sniezynski","raw_affiliation_strings":["Institute of Computer Science, AGH University of Science and Technology, Krakow, Poland"],"affiliations":[{"raw_affiliation_string":"Institute of Computer Science, AGH University of Science and Technology, Krakow, Poland","institution_ids":["https://openalex.org/I686019","https://openalex.org/I4210087266"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102863922","display_name":"Michael Baron","orcid":"https://orcid.org/0000-0003-2759-1181"},"institutions":[{"id":"https://openalex.org/I181401687","display_name":"American University","ror":"https://ror.org/052w4zt36","country_code":"US","type":"education","lineage":["https://openalex.org/I181401687"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michael Baron","raw_affiliation_strings":["Department of Mathematics and Statistics, American University, Washington, DC, USA"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics, American University, Washington, DC, USA","institution_ids":["https://openalex.org/I181401687"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018458084","display_name":"Nathalie Japkowicz","orcid":"https://orcid.org/0000-0003-1176-1617"},"institutions":[{"id":"https://openalex.org/I181401687","display_name":"American University","ror":"https://ror.org/052w4zt36","country_code":"US","type":"education","lineage":["https://openalex.org/I181401687"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nathalie Japkowicz","raw_affiliation_strings":["Department of Computer Science, American University, Washington, DC, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, American University, Washington, DC, USA","institution_ids":["https://openalex.org/I181401687"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5008887467"],"corresponding_institution_ids":["https://openalex.org/I4210087266","https://openalex.org/I686019"],"apc_list":null,"apc_paid":null,"fwci":1.6336,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.87249953,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"4450","last_page":"4459"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9988999962806702,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9886000156402588,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8066647052764893},{"id":"https://openalex.org/keywords/change-detection","display_name":"Change detection","score":0.7748576402664185},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6985363364219666},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6235685348510742},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5876861810684204},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.562953770160675},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.48397397994995117},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.46962714195251465},{"id":"https://openalex.org/keywords/dynamic-data","display_name":"Dynamic data","score":0.4688378870487213},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.42412522435188293},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4067278206348419},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3394443392753601},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.07895871996879578}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8066647052764893},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.7748576402664185},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6985363364219666},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6235685348510742},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5876861810684204},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.562953770160675},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.48397397994995117},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.46962714195251465},{"id":"https://openalex.org/C197298091","wikidata":"https://www.wikidata.org/wiki/Q5318963","display_name":"Dynamic data","level":2,"score":0.4688378870487213},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.42412522435188293},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4067278206348419},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3394443392753601},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.07895871996879578},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/bigdata52589.2021.9671962","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671962","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2201.07125","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2201.07125","pdf_url":"https://arxiv.org/pdf/2201.07125","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"pmh:oai:arXiv.org:2201.07125","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2201.07125","pdf_url":"https://arxiv.org/pdf/2201.07125","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":62,"referenced_works":["https://openalex.org/W3487859","https://openalex.org/W134960717","https://openalex.org/W164029384","https://openalex.org/W1483365869","https://openalex.org/W1537430301","https://openalex.org/W1565746575","https://openalex.org/W1597681648","https://openalex.org/W1975684011","https://openalex.org/W2028563099","https://openalex.org/W2044535354","https://openalex.org/W2047287351","https://openalex.org/W2057797785","https://openalex.org/W2093104233","https://openalex.org/W2110158442","https://openalex.org/W2112724113","https://openalex.org/W2119823327","https://openalex.org/W2121044470","https://openalex.org/W2122646361","https://openalex.org/W2143266849","https://openalex.org/W2150427470","https://openalex.org/W2200491676","https://openalex.org/W2219995598","https://openalex.org/W2244109919","https://openalex.org/W2257742059","https://openalex.org/W2330210193","https://openalex.org/W2515822248","https://openalex.org/W2525449401","https://openalex.org/W2747599906","https://openalex.org/W2803260261","https://openalex.org/W2805502496","https://openalex.org/W2811266412","https://openalex.org/W2914973938","https://openalex.org/W2939423850","https://openalex.org/W2963746531","https://openalex.org/W2980415170","https://openalex.org/W3007271490","https://openalex.org/W3007919696","https://openalex.org/W3010896178","https://openalex.org/W3011204221","https://openalex.org/W3080995048","https://openalex.org/W3098561233","https://openalex.org/W3136297330","https://openalex.org/W3138576619","https://openalex.org/W3145717837","https://openalex.org/W3167553563","https://openalex.org/W3168573009","https://openalex.org/W3189826645","https://openalex.org/W3201769528","https://openalex.org/W3205303036","https://openalex.org/W4238553616","https://openalex.org/W4287826930","https://openalex.org/W6600140087","https://openalex.org/W6605479355","https://openalex.org/W6606729362","https://openalex.org/W6628808406","https://openalex.org/W6673763086","https://openalex.org/W6677031804","https://openalex.org/W6678230397","https://openalex.org/W6745535286","https://openalex.org/W6751250540","https://openalex.org/W6752137081","https://openalex.org/W6758543641"],"related_works":["https://openalex.org/W1995239016","https://openalex.org/W96694040","https://openalex.org/W3008871774","https://openalex.org/W4394789000","https://openalex.org/W2884729156","https://openalex.org/W4206987598","https://openalex.org/W3156373638","https://openalex.org/W4388425994","https://openalex.org/W2991818765","https://openalex.org/W2619865162"],"abstract_inverted_index":{"Detecting":[0],"relevant":[1],"changes":[2,32],"in":[3,8,20,33,42,76,133],"dynamic":[4,134],"time":[5],"series":[6],"data":[7,17,49,123],"a":[9,39,103,142],"timely":[10],"manner":[11],"is":[12,60,73,151],"crucially":[13],"important":[14],"for":[15],"many":[16],"analysis":[18,44],"tasks":[19],"real-world":[21],"settings.":[22],"Change":[23],"point":[24,108],"detection":[25,109,129],"methods":[26],"have":[27],"the":[28,43,57],"ability":[29,65],"to":[30,66,96],"discover":[31],"an":[34,113],"unsupervised":[35],"fashion,":[36],"which":[37,72],"represents":[38],"desirable":[40],"property":[41],"of":[45,54,56,130,145,153],"unbounded":[46],"and":[47,69,87,116,127,158],"unlabeled":[48],"streams.":[50],"However,":[51],"one":[52],"limitation":[53],"most":[55],"existing":[58],"approaches":[59],"represented":[61],"by":[62,100],"their":[63],"limited":[64],"handle":[67],"multivariate":[68],"high-dimensional":[70,135],"data,":[71],"frequently":[74],"observed":[75],"modern":[77],"applications":[78],"such":[79],"as":[80],"traffic":[81],"flow":[82],"prediction,":[83],"human":[84],"activity":[85],"recognition,":[86],"smart":[88],"grids":[89],"monitoring.":[90],"In":[91],"this":[92,98],"paper,":[93],"we":[94],"attempt":[95],"fill":[97],"gap":[99],"proposing":[101],"WATCH,":[102],"novel":[104],"Wasserstein":[105],"distance-based":[106],"change":[107,131,156],"approach":[110],"that":[111,149],"models":[112],"initial":[114],"distribution":[115],"monitors":[117],"its":[118],"behavior":[119],"while":[120],"processing":[121],"new":[122],"points,":[124],"providing":[125],"accurate":[126],"robust":[128],"points":[132,157],"data.":[136],"An":[137],"extensive":[138],"experimental":[139],"evaluation":[140],"involving":[141],"large":[143],"number":[144],"benchmark":[146],"datasets":[147],"shows":[148],"WATCH":[150],"capable":[152],"accurately":[154],"identifying":[155],"outperforming":[159],"state-of-the-art":[160],"methods.":[161]},"counts_by_year":[{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3}],"updated_date":"2026-02-27T16:54:17.756197","created_date":"2022-01-25T00:00:00"}