{"id":"https://openalex.org/W4385562582","doi":"https://doi.org/10.1145/3580305.3599391","title":"Imputation-based Time-Series Anomaly Detection with Conditional Weight-Incremental Diffusion Models","display_name":"Imputation-based Time-Series Anomaly Detection with Conditional Weight-Incremental Diffusion Models","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4385562582","doi":"https://doi.org/10.1145/3580305.3599391"},"language":"en","primary_location":{"id":"doi:10.1145/3580305.3599391","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599391","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and 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/A5108053370","display_name":"Chunjing Xiao","orcid":"https://orcid.org/0000-0001-8339-1278"},"institutions":[{"id":"https://openalex.org/I173899330","display_name":"Henan University","ror":"https://ror.org/003xyzq10","country_code":"CN","type":"education","lineage":["https://openalex.org/I173899330"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chunjing Xiao","raw_affiliation_strings":["Henan University, Kaifeng, China"],"affiliations":[{"raw_affiliation_string":"Henan University, Kaifeng, China","institution_ids":["https://openalex.org/I173899330"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092595084","display_name":"Zehua Gou","orcid":"https://orcid.org/0009-0008-7810-3399"},"institutions":[{"id":"https://openalex.org/I173899330","display_name":"Henan University","ror":"https://ror.org/003xyzq10","country_code":"CN","type":"education","lineage":["https://openalex.org/I173899330"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zehua Gou","raw_affiliation_strings":["Henan University, Kaifeng, China"],"affiliations":[{"raw_affiliation_string":"Henan University, Kaifeng, China","institution_ids":["https://openalex.org/I173899330"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090741164","display_name":"Wenxin Tai","orcid":"https://orcid.org/0000-0001-7364-8324"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenxin Tai","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014223717","display_name":"Kunpeng Zhang","orcid":"https://orcid.org/0000-0002-1474-3169"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kunpeng Zhang","raw_affiliation_strings":["University of Maryland, College Park, College Park, MD, USA"],"affiliations":[{"raw_affiliation_string":"University of Maryland, College Park, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100403505","display_name":"Fan Zhou","orcid":"https://orcid.org/0000-0002-8038-8150"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fan Zhou","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5108053370"],"corresponding_institution_ids":["https://openalex.org/I173899330"],"apc_list":null,"apc_paid":null,"fwci":13.9263,"has_fulltext":false,"cited_by_count":81,"citation_normalized_percentile":{"value":0.99236167,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2742","last_page":"2751"},"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/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9916999936103821,"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/imputation","display_name":"Imputation (statistics)","score":0.7662345170974731},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6888999938964844},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6674681901931763},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6198632717132568},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.49199220538139343},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4901176393032074},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.4845999479293823},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.43526098132133484},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3775487542152405},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35856732726097107},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3251400291919708},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2845969796180725}],"concepts":[{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.7662345170974731},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6888999938964844},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6674681901931763},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6198632717132568},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.49199220538139343},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4901176393032074},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.4845999479293823},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.43526098132133484},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3775487542152405},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35856732726097107},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3251400291919708},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2845969796180725},{"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/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3580305.3599391","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599391","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2294451369","display_name":null,"funder_award_id":"2022NSFSC0505","funder_id":"https://openalex.org/F4320329861","funder_display_name":"Natural Science Foundation of Sichuan Province"},{"id":"https://openalex.org/G3085993365","display_name":null,"funder_award_id":"(Grant No.","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G37568934","display_name":null,"funder_award_id":"Grant","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3976612152","display_name":null,"funder_award_id":"62176043","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5096268229","display_name":null,"funder_award_id":"62176043,62072077","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5249178904","display_name":null,"funder_award_id":"Grant No. 6","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G541483543","display_name":null,"funder_award_id":"Grant No. 2022NSFSC0505","funder_id":"https://openalex.org/F4320329861","funder_display_name":"Natural Science Foundation of Sichuan Province"},{"id":"https://openalex.org/G5625929805","display_name":null,"funder_award_id":"6217604","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5983462902","display_name":null,"funder_award_id":"62072077","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6544442918","display_name":null,"funder_award_id":"62176043 and 62072077","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7216457744","display_name":null,"funder_award_id":"2022NSFSC","funder_id":"https://openalex.org/F4320329861","funder_display_name":"Natural Science Foundation of Sichuan Province"},{"id":"https://openalex.org/G7652787523","display_name":null,"funder_award_id":"Grant No. 62176043 and 62072077","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7726157001","display_name":null,"funder_award_id":"Grant No.","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320329861","display_name":"Natural Science Foundation of Sichuan Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W2144182447","https://openalex.org/W2407991977","https://openalex.org/W2438471924","https://openalex.org/W2593048559","https://openalex.org/W2785362611","https://openalex.org/W2786827964","https://openalex.org/W2809400334","https://openalex.org/W2811266412","https://openalex.org/W2909693411","https://openalex.org/W2911200746","https://openalex.org/W2950361482","https://openalex.org/W2963166639","https://openalex.org/W2963360736","https://openalex.org/W2964425131","https://openalex.org/W2965433388","https://openalex.org/W3004207920","https://openalex.org/W3017279187","https://openalex.org/W3080585419","https://openalex.org/W3081497074","https://openalex.org/W3098957257","https://openalex.org/W3099971460","https://openalex.org/W3106543020","https://openalex.org/W3107582219","https://openalex.org/W3128634608","https://openalex.org/W3136173905","https://openalex.org/W3155567600","https://openalex.org/W3169450514","https://openalex.org/W3170981104","https://openalex.org/W3185481507","https://openalex.org/W3190748826","https://openalex.org/W3217364055","https://openalex.org/W4225539031","https://openalex.org/W4291653271","https://openalex.org/W4372265740"],"related_works":["https://openalex.org/W2541565311","https://openalex.org/W3049453136","https://openalex.org/W2784019465","https://openalex.org/W3192727092","https://openalex.org/W3170920059","https://openalex.org/W4386249425","https://openalex.org/W2159586267","https://openalex.org/W2063729131","https://openalex.org/W3082117105","https://openalex.org/W3082860126"],"abstract_inverted_index":{"Existing":[0],"anomaly":[1,78,91,129,148],"detection":[2],"models":[3],"for":[4,59,127],"time":[5,40],"series":[6,41],"are":[7],"primarily":[8],"trained":[9],"with":[10,109,146],"normal-point-dominant":[11],"data":[12,55,124],"and":[13,46,121],"would":[14],"become":[15],"ineffective":[16],"when":[17],"anomalous":[18],"points":[19],"intensively":[20],"occur":[21],"in":[22,87],"certain":[23],"episodes.":[24],"To":[25,81],"solve":[26],"this":[27],"problem,":[28],"we":[29,93,132],"propose":[30],"a":[31,63,95,134],"new":[32,96],"approach,":[33],"called":[34],"DiffAD,":[35],"from":[36],"the":[37,77,83,88,103,116,141],"perspective":[38],"of":[39,90,106,118],"imputation.":[42],"Unlike":[43],"previous":[44],"prediction-":[45],"reconstruction-based":[47],"methods":[48],"that":[49,72,157],"adopt":[50],"either":[51],"partial":[52],"or":[53],"complete":[54],"as":[56],"observed":[57,119],"values":[58,108,120],"estimation,":[60],"DiffAD":[61,158],"uses":[62],"density":[64],"ratio-based":[65],"strategy":[66],"to":[67,76,101,139],"select":[68],"normal":[69],"observations":[70],"flexibly":[71],"can":[73,114],"easily":[74],"adapt":[75],"concentration":[79],"scenarios.":[80],"alleviate":[82],"model":[84,138],"bias":[85],"problem":[86],"presence":[89],"concentration,":[92],"design":[94],"denoising":[97],"diffusion-based":[98],"imputation":[99,104],"method":[100],"enhance":[102],"performance":[105],"missing":[107],"conditional":[110],"weight-incremental":[111],"diffusion,":[112],"which":[113],"preserve":[115],"information":[117],"substantially":[122],"improves":[123],"generation":[125],"quality":[126],"stable":[128],"detection.":[130],"Besides,":[131],"customize":[133],"multi-scale":[135],"state":[136],"space":[137],"capture":[140],"long-term":[142],"dependencies":[143],"across":[144],"episodes":[145],"different":[147],"patterns.":[149],"Extensive":[150],"experimental":[151],"results":[152],"on":[153],"real-world":[154],"datasets":[155],"show":[156],"performs":[159],"better":[160],"than":[161],"state-of-the-art":[162],"benchmarks.":[163]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":42},{"year":2024,"cited_by_count":30},{"year":2023,"cited_by_count":1}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}
