{"id":"https://openalex.org/W7150853817","doi":"https://doi.org/10.1109/jiot.2026.3681003","title":"Masked Generative Models for Real-Time Network Traffic Forecasting","display_name":"Masked Generative Models for Real-Time Network Traffic Forecasting","publication_year":2026,"publication_date":"2026-04-06","ids":{"openalex":"https://openalex.org/W7150853817","doi":"https://doi.org/10.1109/jiot.2026.3681003"},"language":"en","primary_location":{"id":"doi:10.1109/jiot.2026.3681003","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2026.3681003","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/A5133053799","display_name":"Lei Deng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lei Deng","raw_affiliation_strings":["Internet of Things Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China"],"raw_orcid":"https://orcid.org/0000-0003-4688-2240","affiliations":[{"raw_affiliation_string":"Internet of Things Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133000464","display_name":"Xiao-Yang Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiao-Yang Liu","raw_affiliation_strings":["Department of Electrical Engineering, Columbia University, New York, NY, USA"],"raw_orcid":"https://orcid.org/0000-0002-9532-1709","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Columbia University, New York, NY, USA","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021384697","display_name":"Wenhan Xu","orcid":"https://orcid.org/0000-0003-0170-6630"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenhan Xu","raw_affiliation_strings":["Internet of Things Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China"],"raw_orcid":"https://orcid.org/0000-0003-0170-6630","affiliations":[{"raw_affiliation_string":"Internet of Things Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133029565","display_name":"Jingwei Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jingwei Li","raw_affiliation_strings":["Internet of Things Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Internet of Things Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5133028009","display_name":"Danny H. K. Tsang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Danny H. K. Tsang","raw_affiliation_strings":["Internet of Things Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China"],"raw_orcid":"https://orcid.org/0000-0003-0135-7098","affiliations":[{"raw_affiliation_string":"Internet of Things Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.34917097,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":"12","first_page":"27912","last_page":"27926"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.8058000206947327,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.8058000206947327,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10698","display_name":"Transportation Planning and Optimization","score":0.02500000037252903,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10524","display_name":"Traffic control and management","score":0.015300000086426735,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4521999955177307},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4101000130176544},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.3508000075817108},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.3375999927520752},{"id":"https://openalex.org/keywords/probabilistic-forecasting","display_name":"Probabilistic forecasting","score":0.31940001249313354},{"id":"https://openalex.org/keywords/demand-forecasting","display_name":"Demand forecasting","score":0.31040000915527344},{"id":"https://openalex.org/keywords/intelligent-network","display_name":"Intelligent Network","score":0.3091000020503998}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8069000244140625},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5095000267028809},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4521999955177307},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4101000130176544},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3774000108242035},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35109999775886536},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3508000075817108},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3375999927520752},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.31940001249313354},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.31040000915527344},{"id":"https://openalex.org/C51675839","wikidata":"https://www.wikidata.org/wiki/Q1665681","display_name":"Intelligent Network","level":2,"score":0.3091000020503998},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.29589998722076416},{"id":"https://openalex.org/C161657586","wikidata":"https://www.wikidata.org/wiki/Q1203326","display_name":"Technology forecasting","level":2,"score":0.27730000019073486},{"id":"https://openalex.org/C3020028006","wikidata":"https://www.wikidata.org/wiki/Q9158","display_name":"Electronic mail","level":2,"score":0.2728999853134155},{"id":"https://openalex.org/C163068380","wikidata":"https://www.wikidata.org/wiki/Q3409313","display_name":"Economic forecasting","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C106516650","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm design","level":2,"score":0.25769999623298645},{"id":"https://openalex.org/C176715033","wikidata":"https://www.wikidata.org/wiki/Q2080768","display_name":"Traffic generation model","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/jiot.2026.3681003","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2026.3681003","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"},{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-172099","is_oa":false,"landing_page_url":"http://repository.hkust.edu.hk/ir/Record/1783.1-172099","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","display_name":"Climate action","score":0.4424017667770386}],"awards":[{"id":"https://openalex.org/G2938312049","display_name":null,"funder_award_id":"2021JC02X149","funder_id":"https://openalex.org/F4320314147","funder_display_name":"Guangdong Provincial Key Laboratory of Urology"},{"id":"https://openalex.org/G537185493","display_name":null,"funder_award_id":"2023A03J0011","funder_id":"https://openalex.org/F4320335480","funder_display_name":"Guangzhou Municipal Science and Technology Project"},{"id":"https://openalex.org/G6569124713","display_name":null,"funder_award_id":"2023B1212010007","funder_id":"https://openalex.org/F4320314147","funder_display_name":"Guangdong Provincial Key Laboratory of Urology"}],"funders":[{"id":"https://openalex.org/F4320314147","display_name":"Guangdong Provincial Key Laboratory of Urology","ror":null},{"id":"https://openalex.org/F4320335480","display_name":"Guangzhou Municipal Science and Technology Project","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Network":[0],"traffic":[1,15,42,87,97,133],"forecasting":[2,16,43,51,134],"is":[3,20],"crucial":[4],"for":[5,39,103],"dynamic":[6],"resource":[7],"allocation":[8],"and":[9,59],"network":[10,14,41,132],"management.":[11],"However,":[12],"real-time":[13,40,104],"in":[17],"real-world":[18,124],"scenarios":[19],"challenging":[21],"due":[22],"to":[23,80,94],"the":[24,50,114,118],"limitation":[25],"of":[26,117],"incomplete":[27,45,86],"data.":[28,46,88],"In":[29],"this":[30],"paper,":[31],"we":[32,48,73],"propose":[33,74],"a":[34,54,109,139],"masked":[35,78],"generative":[36,62,101],"model":[37],"(MGM)":[38],"from":[44,85],"Firstly,":[47],"formulate":[49],"task":[52],"as":[53],"low-tubal-rank":[55,66,96],"tensor":[56],"completion":[57],"problem":[58],"verify":[60],"that":[61,112,127],"models":[63,102],"can":[64],"produce":[65],"tensors":[67,98],"through":[68,99],"low-dimensional":[69],"latent":[70,83],"variables.":[71],"Secondly,":[72],"MGM,":[75],"which":[76],"adapts":[77],"autoencoders":[79],"robustly":[81],"learn":[82],"variables":[84,90],"The":[89],"are":[91],"then":[92],"mapped":[93],"complete":[95],"pretrained":[100],"forecasting.":[105],"We":[106],"also":[107],"establish":[108],"performance":[110],"guarantee":[111],"quantifies":[113],"error":[115,144],"bound":[116],"proposed":[119],"approach.":[120],"Finally,":[121],"experiments":[122],"on":[123],"datasets":[125],"demonstrate":[126],"our":[128],"approach":[129],"achieves":[130],"accurate":[131],"within":[135],"100":[136],"ms,":[137],"with":[138],"normalized":[140],"root":[141],"mean":[142],"squared":[143],"(NRMSE)":[145],"below":[146],"0.1.":[147]},"counts_by_year":[],"updated_date":"2026-06-11T06:19:23.411458","created_date":"2026-04-07T00:00:00"}
