{"id":"https://openalex.org/W4392904765","doi":"https://doi.org/10.1109/icassp48485.2024.10446434","title":"Enhanced KPI Anomaly Detection: An Unsupervised Hybrid Model with Dynamic Threshold","display_name":"Enhanced KPI Anomaly Detection: An Unsupervised Hybrid Model with Dynamic Threshold","publication_year":2024,"publication_date":"2024-03-18","ids":{"openalex":"https://openalex.org/W4392904765","doi":"https://doi.org/10.1109/icassp48485.2024.10446434"},"language":"en","primary_location":{"id":"doi:10.1109/icassp48485.2024.10446434","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10446434","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5100449850","display_name":"Yilin Wang","orcid":"https://orcid.org/0000-0002-6610-8036"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yilin Wang","raw_affiliation_strings":["Xiamen University,Xiamen,China","Xiamen University, Xiamen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiamen University,Xiamen,China","institution_ids":["https://openalex.org/I191208505"]},{"raw_affiliation_string":"Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100357819","display_name":"Tao Chen","orcid":"https://orcid.org/0000-0003-4565-5548"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Chen","raw_affiliation_strings":["Xiamen University,Xiamen,China","Xiamen University, Xiamen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiamen University,Xiamen,China","institution_ids":["https://openalex.org/I191208505"]},{"raw_affiliation_string":"Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070637003","display_name":"Yuliang Tang","orcid":"https://orcid.org/0000-0002-5923-9011"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuliang Tang","raw_affiliation_strings":["Xiamen University,Xiamen,China","Xiamen University, Xiamen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiamen University,Xiamen,China","institution_ids":["https://openalex.org/I191208505"]},{"raw_affiliation_string":"Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019883648","display_name":"Lianfen Huang","orcid":"https://orcid.org/0000-0003-1620-1504"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lianfen Huang","raw_affiliation_strings":["Xiamen University,Xiamen,China","Xiamen University, Xiamen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiamen University,Xiamen,China","institution_ids":["https://openalex.org/I191208505"]},{"raw_affiliation_string":"Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6109,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.71087115,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"7140","last_page":"7144"},"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":0.9997000098228455,"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":0.9965000152587891,"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":0.8214083909988403},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7951482534408569},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.7585132122039795},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6416783332824707},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5997209548950195},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5728504061698914},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4644114673137665},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4514469504356384},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.43834349513053894},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3951936662197113}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8214083909988403},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7951482534408569},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.7585132122039795},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6416783332824707},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5997209548950195},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5728504061698914},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4644114673137665},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4514469504356384},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.43834349513053894},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3951936662197113},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","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},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp48485.2024.10446434","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10446434","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2766761849","https://openalex.org/W2911200746","https://openalex.org/W2918465401","https://openalex.org/W3015959599","https://openalex.org/W3081497074","https://openalex.org/W3152601077","https://openalex.org/W3153872861","https://openalex.org/W4206118545","https://openalex.org/W4224933758","https://openalex.org/W4281643792","https://openalex.org/W4285176337","https://openalex.org/W4308579843","https://openalex.org/W6838461383","https://openalex.org/W7016021835"],"related_works":["https://openalex.org/W1557094818","https://openalex.org/W2183246718","https://openalex.org/W2099261052","https://openalex.org/W3209204065","https://openalex.org/W1755711892","https://openalex.org/W2160907113","https://openalex.org/W2070813941","https://openalex.org/W2105707930","https://openalex.org/W3046510185","https://openalex.org/W4214835788"],"abstract_inverted_index":{"Anomaly":[0],"detection":[1],"based":[2],"on":[3,54,149],"key":[4],"performance":[5,37,163],"indicator":[6],"(KPI)":[7],"is":[8,26],"an":[9,68],"important":[10],"topic":[11],"in":[12,28,100],"the":[13,29,36,45,55,58,79,90,96,101,122,125,144],"field":[14],"of":[15,22,38,57,82,124],"intelligent":[16],"operation":[17],"and":[18,32,88,138,153,164],"maintenance.":[19],"The":[20],"problem":[21,46],"insufficient":[23],"annotated":[24],"samples":[25],"widespread":[27],"industrial":[30],"Internet,":[31],"it":[33],"severely":[34],"impairs":[35],"data-driven":[39],"anomaly":[40],"detection.":[41],"Previous":[42],"methods":[43],"tackled":[44],"mainly":[47],"through":[48],"unsupervised":[49,69],"methods,":[50],"which":[51],"rely":[52],"heavily":[53],"ability":[56],"algorithm":[59],"to":[60,72,93,134],"extract":[61],"features.":[62],"In":[63],"this":[64],"work,":[65],"we":[66,77,112,132],"propose":[67,113],"hybrid":[70],"model":[71],"address":[73],"these":[74],"issues.":[75],"Technically,":[76],"capture":[78],"long-term":[80],"dependencies":[81],"time":[83,102],"series":[84,103],"by":[85],"stacked":[86],"BiLSTM":[87],"use":[89],"self-attention":[91],"mechanism":[92],"adaptively":[94],"select":[95],"most":[97],"noteworthy":[98],"information":[99],"data":[104,126,156],"for":[105],"global":[106],"consideration.":[107],"To":[108],"effectively":[109],"distinguish":[110],"anomalies,":[111],"a":[114],"dynamic":[115],"threshold":[116],"method":[117,160],"that":[118,158],"takes":[119],"into":[120],"account":[121],"context":[123],"being":[127],"measured.":[128],"By":[129],"doing":[130],"so,":[131],"aim":[133],"minimize":[135],"false":[136],"positives":[137],"missed":[139],"positives,":[140],"thus":[141],"significantly":[142],"enhancing":[143],"overall":[145],"performance.":[146],"Extensive":[147],"experiments":[148],"various":[150],"public":[151],"benchmarks":[152],"real-world":[154],"measured":[155],"demonstrate":[157],"our":[159],"offers":[161],"advanced":[162],"practicality.":[165]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}