{"id":"https://openalex.org/W4396844322","doi":"https://doi.org/10.1145/3589335.3651492","title":"Disentangled Anomaly Detection For Multivariate Time Series","display_name":"Disentangled Anomaly Detection For Multivariate Time Series","publication_year":2024,"publication_date":"2024-05-12","ids":{"openalex":"https://openalex.org/W4396844322","doi":"https://doi.org/10.1145/3589335.3651492"},"language":"en","primary_location":{"id":"doi:10.1145/3589335.3651492","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3589335.3651492","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3589335.3651492","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM Web Conference 2024","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3589335.3651492","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102869411","display_name":"Xin Jie","orcid":"https://orcid.org/0009-0003-9065-770X"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Jie","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0009-0003-9065-770X","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101612611","display_name":"Xixi Zhou","orcid":"https://orcid.org/0000-0003-0389-3994"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xixi Zhou","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-0389-3994","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011530273","display_name":"Chan-Fei Su","orcid":"https://orcid.org/0000-0003-3891-9672"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chanfei Su","raw_affiliation_strings":["OPPO Research Institute, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0003-3891-9672","affiliations":[{"raw_affiliation_string":"OPPO Research Institute, Shenzhen, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083833449","display_name":"Zijun Zhou","orcid":"https://orcid.org/0009-0003-7971-6625"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zijun Zhou","raw_affiliation_strings":["OPPO Research Institute, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0003-7971-6625","affiliations":[{"raw_affiliation_string":"OPPO Research Institute, Shenzhen, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5097795024","display_name":"Yuqing Yuan","orcid":"https://orcid.org/0009-0007-6584-5433"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuqing Yuan","raw_affiliation_strings":["OPPO Research Institute, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0007-6584-5433","affiliations":[{"raw_affiliation_string":"OPPO Research Institute, Shenzhen, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052757755","display_name":"Jiajun Bu","orcid":"https://orcid.org/0000-0002-1097-2044"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiajun Bu","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-1097-2044","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047118636","display_name":"Haishuai Wang","orcid":"https://orcid.org/0000-0003-1617-0920"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haishuai Wang","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-1617-0920","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"931","last_page":"934"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9995999932289124,"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/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9901000261306763,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7372475862503052},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.7327477931976318},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6798030138015747},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6282880306243896},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5547095537185669},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5009050369262695},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43817445635795593},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.39346104860305786},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3441474139690399},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.22672361135482788},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.15450230240821838},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.06044629216194153}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7372475862503052},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.7327477931976318},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6798030138015747},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6282880306243896},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5547095537185669},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5009050369262695},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43817445635795593},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39346104860305786},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3441474139690399},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.22672361135482788},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.15450230240821838},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06044629216194153},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3589335.3651492","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3589335.3651492","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3589335.3651492","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM Web Conference 2024","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3589335.3651492","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3589335.3651492","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3589335.3651492","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM Web Conference 2024","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.4399999976158142,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G1425435225","display_name":null,"funder_award_id":"Grant Nos. 62202422 and 62372408","funder_id":"https://openalex.org/F4320323817","funder_display_name":"Universitas Brawijaya"}],"funders":[{"id":"https://openalex.org/F4320323817","display_name":"Universitas Brawijaya","ror":"https://ror.org/01wk3d929"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4396844322.pdf"},"referenced_works_count":14,"referenced_works":["https://openalex.org/W2753738274","https://openalex.org/W2786827964","https://openalex.org/W2904753829","https://openalex.org/W2905361499","https://openalex.org/W2950361482","https://openalex.org/W3106543020","https://openalex.org/W3173539742","https://openalex.org/W3177318507","https://openalex.org/W3212890323","https://openalex.org/W4248850814","https://openalex.org/W4290943459","https://openalex.org/W4306884390","https://openalex.org/W4365794555","https://openalex.org/W4390859859"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2912112202","https://openalex.org/W2667207928","https://openalex.org/W2118640767","https://openalex.org/W4377864969"],"abstract_inverted_index":{"Anomaly":[0],"detection":[1,71,80],"in":[2,48,55,81,93],"time":[3,83],"series":[4],"that":[5,73,110],"aims":[6],"to":[7,28,98],"identify":[8],"unusual":[9],"patterns":[10],"has":[11,44],"attracted":[12],"a":[13,67,94],"lot":[14],"of":[15,21,102],"attention":[16],"recently.":[17],"However,":[18],"the":[19,100],"representation":[20,96],"abnormal":[22],"and":[23,51,58],"normal":[24,105],"data":[25],"is":[26],"difffcult":[27],"be":[29],"distinguished":[30],"because":[31],"they":[32],"are":[33],"usually":[34],"entangled.":[35],"Recently,":[36],"disentanglement":[37,76],"theory":[38],"based":[39],"on":[40],"variational":[41],"auto-encoder":[42],"(VAE)":[43],"shown":[45],"great":[46,53],"potential":[47],"machine":[49],"learning":[50],"achieved":[52],"success":[54],"computer":[56],"vision":[57],"natural":[59],"language":[60],"processing.":[61],"In":[62],"this":[63],"paper,":[64],"we":[65],"propose":[66],"novel":[68],"disentangled":[69,90],"anomaly":[70,79],"approach":[72],"adopts":[74],"VAE-based":[75],"networks":[77],"for":[78],"multivariate":[82],"series.":[84],"The":[85],"proposed":[86,112],"method":[87],"learns":[88],"highquality":[89],"latent":[91],"factors":[92],"continuous":[95],"space":[97],"facilitate":[99],"identiffcation":[101],"anomalies":[103],"from":[104],"data.":[106],"Extensive":[107],"experiments":[108],"demonstrate":[109],"our":[111],"lightweight":[113],"model":[114],"DA-VAE":[115],"achieves":[116],"state-of-the-art":[117],"performance.":[118]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":6}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
