{"id":"https://openalex.org/W3170937175","doi":"https://doi.org/10.1145/3447548.3467075","title":"Multivariate Time Series Anomaly Detection and Interpretation using Hierarchical Inter-Metric and Temporal Embedding","display_name":"Multivariate Time Series Anomaly Detection and Interpretation using Hierarchical Inter-Metric and Temporal Embedding","publication_year":2021,"publication_date":"2021-08-12","ids":{"openalex":"https://openalex.org/W3170937175","doi":"https://doi.org/10.1145/3447548.3467075","mag":"3170937175"},"language":"en","primary_location":{"id":"doi:10.1145/3447548.3467075","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447548.3467075","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100605168","display_name":"Zhihan Li","orcid":"https://orcid.org/0000-0002-6290-6575"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhihan Li","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101491459","display_name":"Youjian Zhao","orcid":"https://orcid.org/0000-0001-9841-1796"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Youjian Zhao","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067077438","display_name":"Jiaqi Han","orcid":"https://orcid.org/0000-0002-5229-8609"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiaqi Han","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028459386","display_name":"Ya Su","orcid":"https://orcid.org/0000-0002-4432-1135"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ya Su","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063798691","display_name":"Rui Jiao","orcid":"https://orcid.org/0000-0002-7516-0469"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rui Jiao","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001239748","display_name":"Xidao Wen","orcid":"https://orcid.org/0000-0003-0527-947X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xidao Wen","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046419834","display_name":"Dan Pei","orcid":"https://orcid.org/0000-0002-5113-838X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dan Pei","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":29.9501,"has_fulltext":false,"cited_by_count":380,"citation_normalized_percentile":{"value":0.99772319,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"3220","last_page":"3230"},"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":0.9983000159263611,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/anomaly-detection","display_name":"Anomaly detection","score":0.8541933298110962},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7176196575164795},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.7053743004798889},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.6673265695571899},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.634164571762085},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5874844193458557},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.544285774230957},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5341654419898987},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5029405951499939},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.479343980550766},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.43912482261657715},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.42838963866233826},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3936367630958557},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.30718955397605896},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.14303284883499146},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08946630358695984}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8541933298110962},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7176196575164795},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.7053743004798889},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.6673265695571899},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.634164571762085},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5874844193458557},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.544285774230957},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5341654419898987},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5029405951499939},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.479343980550766},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.43912482261657715},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.42838963866233826},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3936367630958557},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30718955397605896},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.14303284883499146},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08946630358695984},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3447548.3467075","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447548.3467075","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.5799999833106995}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W648786980","https://openalex.org/W1992665562","https://openalex.org/W2163605009","https://openalex.org/W2282861635","https://openalex.org/W2407991977","https://openalex.org/W2592049476","https://openalex.org/W2593390416","https://openalex.org/W2609822318","https://openalex.org/W2785362611","https://openalex.org/W2786827964","https://openalex.org/W2804243936","https://openalex.org/W2911200746","https://openalex.org/W2948517885","https://openalex.org/W2950361482","https://openalex.org/W2962736999","https://openalex.org/W2963135265","https://openalex.org/W2963166639","https://openalex.org/W2975649990","https://openalex.org/W2988226917","https://openalex.org/W3081497074","https://openalex.org/W3098957257","https://openalex.org/W3105931142","https://openalex.org/W3106543020","https://openalex.org/W4212774754","https://openalex.org/W4293417080"],"related_works":["https://openalex.org/W3186512740","https://openalex.org/W3194885736","https://openalex.org/W4363671829","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/W2667207928"],"abstract_inverted_index":{"Anomaly":[0],"detection":[1,160,177],"is":[2,82],"a":[3,48],"crucial":[4],"task":[5],"for":[6,77,124],"monitoring":[7],"various":[8],"status":[9],"(i.e.,":[10],"metrics)":[11],"of":[12,50,101,153,169],"entities":[13],"(e.g.,":[14],"manufacturing":[15],"systems":[16],"and":[17,74,119,143,165],"Internet":[18],"services),":[19],"which":[20,102],"are":[21,131],"often":[22],"characterized":[23],"by":[24],"multivariate":[25],"time":[26],"series":[27],"(MTS).":[28],"In":[29,60],"practice,":[30],"it's":[31],"important":[32],"to":[33,41,54,83,115],"precisely":[34],"detect":[35],"the":[36,43,57,72,85],"anomalies,":[37],"as":[38,40],"well":[39],"interpret":[42],"detected":[44],"anomalies":[45],"through":[46,91,149],"localizing":[47],"group":[49],"most":[51],"anomalous":[52,122],"metrics,":[53],"further":[55],"assist":[56],"failure":[58],"troubleshooting.":[59],"this":[61],"paper,":[62],"we":[63,110],"propose":[64,111],"InterFusion,":[65],"an":[66,112,157],"unsupervised":[67],"method":[68,114],"that":[69],"simultaneously":[70],"models":[71],"inter-metric":[73,105],"temporal":[75,107],"dependency":[76],"MTS.":[78],"Its":[79],"core":[80],"idea":[81],"model":[84],"normal":[86],"patterns":[87],"inside":[88],"MTS":[89,125,175],"data":[90],"hierarchical":[92],"Variational":[93],"AutoEncoder":[94],"with":[95],"two":[96],"stochastic":[97],"latent":[98],"variables,":[99],"each":[100],"learns":[103],"low-dimensional":[104],"or":[106],"embeddings.":[108],"Furthermore,":[109],"MCMC-based":[113],"obtain":[116],"reasonable":[117],"embeddings":[118],"reconstructions":[120],"at":[121],"parts":[123],"anomaly":[126,159,166,176],"interpretation.":[127],"Our":[128],"evaluation":[129],"experiments":[130],"conducted":[132],"on":[133],"four":[134],"real-world":[135],"datasets":[136],"from":[137],"different":[138],"industrial":[139],"domains":[140],"(three":[141],"existing":[142],"one":[144],"newly":[145],"published":[146],"dataset":[147],"collected":[148],"our":[150],"pilot":[151],"deployment":[152],"InterFusion).":[154],"InterFusion":[155],"achieves":[156],"average":[158],"F1-Score":[161],"higher":[162],"than":[163],"0.94":[164],"interpretation":[167],"performance":[168],"0.87,":[170],"significantly":[171],"outperforming":[172],"recent":[173],"state-of-the-art":[174],"methods.":[178]},"counts_by_year":[{"year":2026,"cited_by_count":33},{"year":2025,"cited_by_count":133},{"year":2024,"cited_by_count":94},{"year":2023,"cited_by_count":87},{"year":2022,"cited_by_count":31},{"year":2021,"cited_by_count":2}],"updated_date":"2026-06-18T10:00:31.954636","created_date":"2025-10-10T00:00:00"}