{"id":"https://openalex.org/W4401452692","doi":"https://doi.org/10.1109/jiot.2024.3439672","title":"SGFM: Conditional Flow Matching for Time Series Anomaly Detection With State Space Models","display_name":"SGFM: Conditional Flow Matching for Time Series Anomaly Detection With State Space Models","publication_year":2024,"publication_date":"2024-08-09","ids":{"openalex":"https://openalex.org/W4401452692","doi":"https://doi.org/10.1109/jiot.2024.3439672"},"language":"en","primary_location":{"id":"doi:10.1109/jiot.2024.3439672","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2024.3439672","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","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/A5112123581","display_name":"Yongping He","orcid":"https://orcid.org/0009-0000-6566-2599"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongping He","raw_affiliation_strings":["School of Automation, Beijing Institute of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023623913","display_name":"Tijin Yan","orcid":"https://orcid.org/0000-0002-8029-8155"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tijin Yan","raw_affiliation_strings":["School of Automation, Beijing Institute of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058268261","display_name":"Yufeng Zhan","orcid":"https://orcid.org/0000-0002-4528-1489"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yufeng Zhan","raw_affiliation_strings":["School of Automation, Beijing Institute of Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-4528-1489","affiliations":[{"raw_affiliation_string":"School of Automation, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114020196","display_name":"Zihang Feng","orcid":"https://orcid.org/0000-0001-8870-4069"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zihang Feng","raw_affiliation_strings":["School of Automation, Beijing Institute of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064231378","display_name":"Yuanqing Xia","orcid":"https://orcid.org/0000-0002-5977-4911"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanqing Xia","raw_affiliation_strings":["School of Automation, Beijing Institute of Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-5977-4911","affiliations":[{"raw_affiliation_string":"School of Automation, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"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":0.73223624,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":"11","issue":"22","first_page":"36979","last_page":"36990"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9991000294685364,"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":0.9991000294685364,"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":0.9970999956130981,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9876000285148621,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6865631937980652},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6320415139198303},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6088957190513611},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5437894463539124},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.539359986782074},{"id":"https://openalex.org/keywords/state-space","display_name":"State space","score":0.5222457051277161},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.45342114567756653},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.45065733790397644},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.381314754486084},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33862751722335815},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17701005935668945},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.17438501119613647},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.10833370685577393}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6865631937980652},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6320415139198303},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6088957190513611},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5437894463539124},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.539359986782074},{"id":"https://openalex.org/C72434380","wikidata":"https://www.wikidata.org/wiki/Q230930","display_name":"State space","level":2,"score":0.5222457051277161},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.45342114567756653},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.45065733790397644},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.381314754486084},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33862751722335815},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17701005935668945},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.17438501119613647},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.10833370685577393},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jiot.2024.3439672","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2024.3439672","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.5099999904632568,"display_name":"Sustainable cities and communities"},{"id":"https://metadata.un.org/sdg/13","score":0.4099999964237213,"display_name":"Climate action"}],"awards":[{"id":"https://openalex.org/G3164609535","display_name":null,"funder_award_id":"62102022","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7007940310","display_name":null,"funder_award_id":"61836001","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":64,"referenced_works":["https://openalex.org/W2080099070","https://openalex.org/W2110485445","https://openalex.org/W2117902495","https://openalex.org/W2122646361","https://openalex.org/W2756203131","https://openalex.org/W2768947629","https://openalex.org/W2785362611","https://openalex.org/W2786827964","https://openalex.org/W2911200746","https://openalex.org/W2950361482","https://openalex.org/W2953352227","https://openalex.org/W2954340342","https://openalex.org/W2964015378","https://openalex.org/W2965341826","https://openalex.org/W2998559444","https://openalex.org/W3004207920","https://openalex.org/W3010542492","https://openalex.org/W3015959599","https://openalex.org/W3044515030","https://openalex.org/W3081497074","https://openalex.org/W3094614637","https://openalex.org/W3096831136","https://openalex.org/W3098957257","https://openalex.org/W3103720336","https://openalex.org/W3105324058","https://openalex.org/W3106543020","https://openalex.org/W3110257065","https://openalex.org/W3140570329","https://openalex.org/W3169450514","https://openalex.org/W3175420826","https://openalex.org/W3177318507","https://openalex.org/W3199473923","https://openalex.org/W3209374680","https://openalex.org/W4226371211","https://openalex.org/W4252337780","https://openalex.org/W4283318673","https://openalex.org/W4293476335","https://openalex.org/W4297798428","https://openalex.org/W4297814361","https://openalex.org/W4311415873","https://openalex.org/W4319653963","https://openalex.org/W4321020991","https://openalex.org/W4385245566","https://openalex.org/W4385562582","https://openalex.org/W6631943919","https://openalex.org/W6678914141","https://openalex.org/W6682889407","https://openalex.org/W6714644935","https://openalex.org/W6720514713","https://openalex.org/W6736057607","https://openalex.org/W6738536549","https://openalex.org/W6748102297","https://openalex.org/W6752307458","https://openalex.org/W6764817228","https://openalex.org/W6767782324","https://openalex.org/W6779823529","https://openalex.org/W6782420349","https://openalex.org/W6784869275","https://openalex.org/W6786375611","https://openalex.org/W6803444062","https://openalex.org/W6810225340","https://openalex.org/W6846539466","https://openalex.org/W6850146916","https://openalex.org/W6981849894"],"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/W4377864969","https://openalex.org/W2972971679"],"abstract_inverted_index":{"The":[0,34,171,188],"industrial":[1,32],"Internet":[2],"of":[3,13,37,44,60,84,98,107,153,168,173,194],"Things":[4],"(IoT)":[5],"landscape":[6],"is":[7,175],"enriched":[8],"with":[9],"a":[10,118,154,191],"diverse":[11],"array":[12],"sensors,":[14],"which":[15,143],"are":[16],"configured":[17],"for":[18,41,68,159],"the":[19,27,57,61,76,81,85,94,103,108,150,166,178,181,200],"real-time":[20],"monitoring":[21,36],"and":[22,30,55,96,131,163],"data":[23,99,186],"collection":[24],"to":[25,79,93,112,135,148,196,199],"improve":[26],"production":[28],"efficiency":[29],"optimizing":[31],"processes.":[33],"continual":[35],"IoT":[38],"systems":[39],"allows":[40],"promptly":[42],"detection":[43,72,105,122],"anomalies":[45],"in":[46,140],"these":[47,88,113],"time":[48,69,141,184],"series":[49,70,185],"data,":[50],"thereby":[51],"minimizing":[52],"economic":[53],"losses":[54],"ensuring":[56],"safe":[58],"operation":[59],"overall":[62,104],"system.":[63],"Existing":[64],"deep":[65],"learning-based":[66],"methods":[67,89],"anomaly":[71,121,169],"often":[73],"rely":[74],"on":[75,180],"generative":[77],"models":[78,130],"learn":[80],"normal":[82],"behavior":[83],"data.":[86],"However,":[87],"face":[90],"challenges":[91],"related":[92],"speed":[95],"quality":[97],"generation,":[100],"ultimately":[101],"impacting":[102],"performance":[106],"models.":[109],"In":[110],"response":[111],"challenges,":[114],"this":[115],"article":[116],"proposes":[117],"new":[119],"unsupervised":[120],"method":[123],"named":[124],"SGFM.":[125],"It":[126],"combines":[127],"state":[128],"space":[129],"graph":[132],"neural":[133],"networks":[134],"extract":[136],"complex":[137],"spatiotemporal":[138],"dependencies":[139],"series,":[142],"then":[144],"serve":[145],"as":[146],"guidance":[147],"facilitate":[149],"learning":[151],"process":[152],"flow":[155],"matching":[156],"model,":[157],"aiming":[158],"more":[160],"refined":[161],"predictions":[162],"consequently":[164],"enhancing":[165],"effectiveness":[167,172],"detection.":[170],"SGFM":[174],"validated":[176],"through":[177],"experiments":[179],"three":[182],"classic":[183],"sets.":[187],"results":[189],"exhibits":[190],"notable":[192],"improvement":[193],"up":[195],"4%":[197],"compared":[198],"existing":[201],"methods.":[202]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}