{"id":"https://openalex.org/W4391054939","doi":"https://doi.org/10.14778/3632093.3632101","title":"ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection","display_name":"ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection","publication_year":2023,"publication_date":"2023-11-01","ids":{"openalex":"https://openalex.org/W4391054939","doi":"https://doi.org/10.14778/3632093.3632101"},"language":"en","primary_location":{"id":"doi:10.14778/3632093.3632101","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3632093.3632101","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-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/A5076397605","display_name":"Yuhang Chen","orcid":"https://orcid.org/0000-0002-8421-190X"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuhang Chen","raw_affiliation_strings":["Peking University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030520880","display_name":"Chaoyun Zhang","orcid":"https://orcid.org/0000-0002-1304-6839"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Chaoyun Zhang","raw_affiliation_strings":["Microsoft"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070950193","display_name":"Minghua Ma","orcid":"https://orcid.org/0000-0002-6303-1731"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Minghua Ma","raw_affiliation_strings":["Microsoft"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101940299","display_name":"Yudong Liu","orcid":"https://orcid.org/0000-0002-6004-0966"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yudong Liu","raw_affiliation_strings":["Microsoft"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076137287","display_name":"Ruomeng Ding","orcid":"https://orcid.org/0000-0002-7800-8227"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ruomeng Ding","raw_affiliation_strings":["Georgia Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100387285","display_name":"Bowen Li","orcid":"https://orcid.org/0000-0002-6331-1629"},"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":"Bowen Li","raw_affiliation_strings":["Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078511386","display_name":"Shilin He","orcid":"https://orcid.org/0000-0002-8595-5388"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Shilin He","raw_affiliation_strings":["Microsoft"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108894474","display_name":"Saravan Rajmohan","orcid":"https://orcid.org/0009-0003-0204-7187"},"institutions":[{"id":"https://openalex.org/I4210135422","display_name":"Microsoft (Norway)","ror":"https://ror.org/03jtz4s80","country_code":"NO","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210135422"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Saravan Rajmohan","raw_affiliation_strings":["Microsoft 365"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft 365","institution_ids":["https://openalex.org/I4210135422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088646345","display_name":"Qingwei Lin","orcid":"https://orcid.org/0000-0003-2559-2383"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Qingwei Lin","raw_affiliation_strings":["Microsoft"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100331488","display_name":"Dongmei Zhang","orcid":"https://orcid.org/0000-0002-9230-2799"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Dongmei Zhang","raw_affiliation_strings":["Microsoft"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft","institution_ids":["https://openalex.org/I4210164937"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":14.4032,"has_fulltext":false,"cited_by_count":110,"citation_normalized_percentile":{"value":0.99326738,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"17","issue":"3","first_page":"359","last_page":"372"},"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9854999780654907,"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"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9829999804496765,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7768875360488892},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7346617579460144},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6641677618026733},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6054438948631287},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5549347400665283},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.550186812877655},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.5483218431472778},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5173146724700928},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.4740963876247406},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36240312457084656},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.3471892476081848},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3363187313079834}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7768875360488892},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7346617579460144},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6641677618026733},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6054438948631287},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5549347400665283},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.550186812877655},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.5483218431472778},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5173146724700928},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.4740963876247406},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36240312457084656},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.3471892476081848},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3363187313079834},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.14778/3632093.3632101","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3632093.3632101","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":58,"referenced_works":["https://openalex.org/W2003797551","https://openalex.org/W2194775991","https://openalex.org/W2340222647","https://openalex.org/W2407991977","https://openalex.org/W2743617586","https://openalex.org/W2766098680","https://openalex.org/W2767289262","https://openalex.org/W2786827964","https://openalex.org/W2800015774","https://openalex.org/W2901543039","https://openalex.org/W2906498146","https://openalex.org/W2950361482","https://openalex.org/W2962736999","https://openalex.org/W2962883549","https://openalex.org/W2963035276","https://openalex.org/W2963166639","https://openalex.org/W2965433388","https://openalex.org/W2965960151","https://openalex.org/W2983029853","https://openalex.org/W3009634742","https://openalex.org/W3031577140","https://openalex.org/W3036167779","https://openalex.org/W3081497074","https://openalex.org/W3083560262","https://openalex.org/W3086526924","https://openalex.org/W3088381833","https://openalex.org/W3096831136","https://openalex.org/W3106543020","https://openalex.org/W3128634608","https://openalex.org/W3133696297","https://openalex.org/W3155567600","https://openalex.org/W3169450514","https://openalex.org/W3170937175","https://openalex.org/W3170981104","https://openalex.org/W3176476506","https://openalex.org/W3177318507","https://openalex.org/W3181975995","https://openalex.org/W3198800015","https://openalex.org/W3212867051","https://openalex.org/W4224307896","https://openalex.org/W4254182148","https://openalex.org/W4283318673","https://openalex.org/W4283324222","https://openalex.org/W4285451014","https://openalex.org/W4288057688","https://openalex.org/W4290877962","https://openalex.org/W4290878309","https://openalex.org/W4308426104","https://openalex.org/W4308643994","https://openalex.org/W4312377605","https://openalex.org/W4312750676","https://openalex.org/W4312757401","https://openalex.org/W4312933868","https://openalex.org/W4312939613","https://openalex.org/W4385568016","https://openalex.org/W4394946189","https://openalex.org/W6779823529","https://openalex.org/W6790690058"],"related_works":["https://openalex.org/W2181530120","https://openalex.org/W4211215373","https://openalex.org/W2024529227","https://openalex.org/W2055961818","https://openalex.org/W2903115227","https://openalex.org/W1574575415","https://openalex.org/W3144172081","https://openalex.org/W3179858851","https://openalex.org/W3028371478","https://openalex.org/W2081476516"],"abstract_inverted_index":{"Anomaly":[0],"detection":[1,53,108,151,179,201],"in":[2,18,84,98,144,176,192,200],"multivariate":[3],"time":[4,59,86,116,225],"series":[5,60,117,226],"data":[6,20,30],"is":[7,184],"of":[8,91,105,149,157,178,212,234],"paramount":[9],"importance":[10],"for":[11,28,140],"large-scale":[12],"systems.":[13],"However,":[14],"accurately":[15,120],"detecting":[16],"anomalies":[17],"such":[19],"poses":[21],"significant":[22],"challenges":[23,42],"due":[24],"to":[25,39,65,119,135,205,237],"the":[26,79,85,99,103,106,126,132,150,155,188,206,210,231,238],"need":[27],"precise":[29,89],"modeling":[31,90],"capability.":[32],"Existing":[33],"forecasting":[34],"and":[35,62,68,93,147,181,194],"reconstruction-based":[36],"methods":[37],"struggle":[38],"address":[40],"these":[41,46],"effectively.":[43],"To":[44,209],"overcome":[45],"limitations,":[47],"we":[48],"propose":[49],"a":[50,196,217],"novel":[51,232],"anomaly":[52,70,107,141,227],"framework":[54,171],"named":[55],"ImDiffusion,":[56],"which":[57],"combines":[58,221],"imputation":[61],"diffusion":[63,113,235],"models":[64,114,236],"achieve":[66],"accurate":[67],"robust":[69],"detection.":[71],"The":[72,165],"imputation-based":[73,222],"approach":[74,219],"employed":[75],"by":[76],"ImDiffusion":[77,110,158,183,215],"leverages":[78,112],"information":[80],"from":[81],"neighboring":[82],"values":[83],"series,":[87],"enabling":[88],"temporal":[92],"inter-correlated":[94],"dependencies,":[95],"reducing":[96],"uncertainty":[97],"data,":[100],"thereby":[101],"enhancing":[102],"robustness":[104,148],"process.":[109,152],"further":[111,185],"as":[115,137],"imputers":[118],"capture":[121],"complex":[122],"dependencies.":[123],"We":[124,153],"leverage":[125],"step-by-step":[127],"denoised":[128],"outputs":[129],"generated":[130],"during":[131],"inference":[133],"process":[134],"serve":[136],"valuable":[138],"signals":[139],"prediction,":[142],"resulting":[143],"improved":[145],"accuracy":[146,180],"evaluate":[154],"performance":[156],"via":[159],"extensive":[160],"experiments":[161],"on":[162],"benchmark":[163],"datasets.":[164],"results":[166],"demonstrate":[167],"that":[168,220],"our":[169,213],"proposed":[170],"significantly":[172],"outperforms":[173],"state-of-the-art":[174],"approaches":[175],"terms":[177],"timeliness.":[182],"integrated":[186],"into":[187],"real":[189],"production":[190],"system":[191],"Microsoft":[193],"observes":[195],"remarkable":[197],"11.4%":[198],"increase":[199],"F1":[202],"score":[203],"compared":[204],"legacy":[207],"approach.":[208],"best":[211],"knowledge,":[214],"represents":[216],"pioneering":[218],"techniques":[223],"with":[224],"detection,":[228],"while":[229],"introducing":[230],"use":[233],"field.":[239]},"counts_by_year":[{"year":2026,"cited_by_count":31},{"year":2025,"cited_by_count":49},{"year":2024,"cited_by_count":27},{"year":2023,"cited_by_count":3}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}
