{"id":"https://openalex.org/W4386825474","doi":"https://doi.org/10.1109/tim.2023.3315420","title":"CrossFuN: Multiview Joint Cross-Fusion Network for Time-Series Anomaly Detection","display_name":"CrossFuN: Multiview Joint Cross-Fusion Network for Time-Series Anomaly Detection","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4386825474","doi":"https://doi.org/10.1109/tim.2023.3315420"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2023.3315420","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2023.3315420","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"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 Transactions on Instrumentation and Measurement","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/A5056779048","display_name":"Yunfei Bai","orcid":"https://orcid.org/0000-0003-2588-0019"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yunfei Bai","raw_affiliation_strings":["School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China","Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100765674","display_name":"Jing Wang","orcid":"https://orcid.org/0000-0002-1017-2231"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]},{"id":"https://openalex.org/I4210117754","display_name":"Chinese Academy of Civil Aviation Science and Technology","ror":"https://ror.org/023zynq23","country_code":"CN","type":"nonprofit","lineage":["https://openalex.org/I4210117754"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Wang","raw_affiliation_strings":["School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China","CAAC Key Laboratory of Intelligent Passenger Service of Civil Aviation, Beijing, China","Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"CAAC Key Laboratory of Intelligent Passenger Service of Civil Aviation, Beijing, China","institution_ids":["https://openalex.org/I4210117754"]},{"raw_affiliation_string":"Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056991834","display_name":"X. H. Zhang","orcid":"https://orcid.org/0009-0003-8462-6608"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xueer Zhang","raw_affiliation_strings":["School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China","Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111041615","display_name":"Xiangtai Miao","orcid":null},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangtai Miao","raw_affiliation_strings":["School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China","Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102790320","display_name":"Youfang Lin","orcid":"https://orcid.org/0000-0002-1611-4323"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]},{"id":"https://openalex.org/I4210117754","display_name":"Chinese Academy of Civil Aviation Science and Technology","ror":"https://ror.org/023zynq23","country_code":"CN","type":"nonprofit","lineage":["https://openalex.org/I4210117754"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Youfang Lin","raw_affiliation_strings":["School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China","Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing, China","CAAC Key Laboratory of Intelligent Passenger Service of Civil Aviation, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing, China","institution_ids":[]},{"raw_affiliation_string":"CAAC Key Laboratory of Intelligent Passenger Service of Civil Aviation, Beijing, China","institution_ids":["https://openalex.org/I4210117754"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5056779048"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":3.6533,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.94427497,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"72","issue":null,"first_page":"1","last_page":"9"},"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":0.9993000030517578,"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":0.987500011920929,"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":0.7205038666725159},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7057150602340698},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6879306435585022},{"id":"https://openalex.org/keywords/univariate","display_name":"Univariate","score":0.5793322920799255},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5411171317100525},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5400055646896362},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5247809290885925},{"id":"https://openalex.org/keywords/time\u2013frequency-analysis","display_name":"Time\u2013frequency analysis","score":0.5152464509010315},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5041452646255493},{"id":"https://openalex.org/keywords/time-domain","display_name":"Time domain","score":0.4975025951862335},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.47775593400001526},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.45640599727630615},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4149557948112488},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.3433598279953003},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33192354440689087},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.14096975326538086},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10636571049690247},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.07903960347175598}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7205038666725159},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7057150602340698},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6879306435585022},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.5793322920799255},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5411171317100525},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5400055646896362},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5247809290885925},{"id":"https://openalex.org/C142433447","wikidata":"https://www.wikidata.org/wiki/Q7806653","display_name":"Time\u2013frequency analysis","level":3,"score":0.5152464509010315},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5041452646255493},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.4975025951862335},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.47775593400001526},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.45640599727630615},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4149557948112488},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.3433598279953003},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33192354440689087},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.14096975326538086},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10636571049690247},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.07903960347175598},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","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},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2023.3315420","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2023.3315420","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"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 Transactions on Instrumentation and Measurement","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4739588433","display_name":null,"funder_award_id":"2023JBMC056","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1788497923","https://openalex.org/W2013619732","https://openalex.org/W2127543052","https://openalex.org/W2127979711","https://openalex.org/W2296719434","https://openalex.org/W2407991977","https://openalex.org/W2604247107","https://openalex.org/W2604847698","https://openalex.org/W2743617586","https://openalex.org/W2785362611","https://openalex.org/W2786827964","https://openalex.org/W2911200746","https://openalex.org/W2948517885","https://openalex.org/W2950361482","https://openalex.org/W2963166639","https://openalex.org/W3040266635","https://openalex.org/W3081497074","https://openalex.org/W3098957257","https://openalex.org/W3105931142","https://openalex.org/W3106543020","https://openalex.org/W3128634608","https://openalex.org/W3135550350","https://openalex.org/W3169450514","https://openalex.org/W3170937175","https://openalex.org/W3170981104","https://openalex.org/W4254182148","https://openalex.org/W4281388377","https://openalex.org/W4285600291","https://openalex.org/W4306317275","https://openalex.org/W4312458394","https://openalex.org/W6748102297","https://openalex.org/W6786152982","https://openalex.org/W6838819770"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W3210364259","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W2912112202","https://openalex.org/W2667207928","https://openalex.org/W4300558037","https://openalex.org/W4377864969","https://openalex.org/W3030345572"],"abstract_inverted_index":{"Time":[0,120],"Series":[1],"are":[2,35],"often":[3],"used":[4],"to":[5,28,38,94,104,131,178],"record":[6],"the":[7,16,39,42,57,73,114,134,138,145,148,152,159,180],"various":[8],"states":[9],"(i.e.,":[10],"metrics)":[11],"of":[12,41,59,76,98,116,136,158,182],"a":[13,86,123],"system.":[14],"Detecting":[15],"anomaly":[17,33,46],"state":[18],"is":[19,129],"challenging":[20],"because":[21],"temporal":[22,165],"dynamics":[23,166],"and":[24,32,70,78,107,119,140,143,151,167],"inter-metric":[25,168],"dependencies":[26],"need":[27],"be":[29],"learned":[30],"simultaneously,":[31],"types":[34,97],"diverse":[36,96],"due":[37],"complexity":[40],"time":[43,67,77,109,139,149],"series.":[44,110],"Many":[45],"detection":[47],"models":[48],"still":[49],"leave":[50],"some":[51],"challenges":[52],"unresolved.":[53],"They":[54],"mainly":[55],"ignore":[56],"importance":[58],"information":[60,135],"from":[61],"frequency":[62,79,141,153],"domain":[63,68,150],"while":[64],"concentrating":[65],"on":[66,113,174],"modeling":[69],"further":[71],"neglect":[72],"cross-domain":[74],"effects":[75],"domains.":[80],"In":[81],"this":[82],"paper,":[83],"we":[84],"proposed":[85],"novel":[87],"Multi-View":[88],"Joint":[89],"Cross":[90],"Fusion":[91],"Network":[92],"(CrossFuN)":[93],"detect":[95],"anomaly,":[99],"which":[100],"has":[101],"wide":[102],"applicability":[103],"both":[105,137],"univariate":[106],"multivariate":[108],"Particularly,":[111],"based":[112],"assumptions":[115],"Time-Frequency":[117],"Heterogeneity":[118],"Frequency":[121],"Coordination,":[122],"time-frequency":[124],"joint":[125],"cross":[126],"fusion":[127],"block":[128],"designed":[130],"simultaneously":[132],"model":[133],"domains,":[142],"captures":[144],"relationship":[146],"between":[147],"domain.":[154],"Moreover,":[155],"taking":[156],"advantage":[157],"attention":[160],"mechanism,":[161],"CrossFuN":[162],"can":[163],"capture":[164],"dependencies.":[169],"We":[170],"conduct":[171],"extensive":[172],"experiments":[173],"seven":[175],"real-world":[176],"datasets":[177],"demonstrate":[179],"effectiveness":[181],"CrossFuN.":[183]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":6}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
