{"id":"https://openalex.org/W7106217313","doi":"https://doi.org/10.1109/jiot.2025.3636129","title":"Large Language Models (LLMs) for Network Traffic Prediction: A Trend-Aware Hybrid Framework","display_name":"Large Language Models (LLMs) for Network Traffic Prediction: A Trend-Aware Hybrid Framework","publication_year":2025,"publication_date":"2025-11-21","ids":{"openalex":"https://openalex.org/W7106217313","doi":"https://doi.org/10.1109/jiot.2025.3636129"},"language":null,"primary_location":{"id":"doi:10.1109/jiot.2025.3636129","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2025.3636129","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":null,"display_name":"Yuzhou Chen","orcid":"https://orcid.org/0009-0000-4319-6988"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Yuzhou Chen","raw_affiliation_strings":["Faculty of Science, National University of Singapore, Queenstown, Singapore"],"affiliations":[{"raw_affiliation_string":"Faculty of Science, National University of Singapore, Queenstown, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Kwok-Yan Lam","orcid":"https://orcid.org/0000-0001-7479-7970"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Kwok-Yan Lam","raw_affiliation_strings":["College of Computing and Data Science, Nanyang Technological University, Jurong West, Singapore"],"affiliations":[{"raw_affiliation_string":"College of Computing and Data Science, Nanyang Technological University, Jurong West, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"last","author":{"id":null,"display_name":"Feng Li","orcid":"https://orcid.org/0000-0002-4896-8905"},"institutions":[{"id":"https://openalex.org/I75059550","display_name":"Zhejiang Gongshang University","ror":"https://ror.org/0569mkk41","country_code":"CN","type":"education","lineage":["https://openalex.org/I75059550"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Li","raw_affiliation_strings":["School of Information and Electronic Engineering, Zhejiang Gongshang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Electronic Engineering, Zhejiang Gongshang University, Hangzhou, China","institution_ids":["https://openalex.org/I75059550"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I165932596"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.53170091,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":"4","first_page":"6422","last_page":"6436"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.963100016117096,"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":0.963100016117096,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.005200000014156103,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.00419999985024333,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/residual","display_name":"Residual","score":0.7455000281333923},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5641000270843506},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5331000089645386},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.510200023651123},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.46619999408721924},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.46309998631477356},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.44519999623298645},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4129999876022339},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.37070000171661377}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8235999941825867},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.7455000281333923},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5641000270843506},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5331000089645386},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5135999917984009},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.510200023651123},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4869000017642975},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47189998626708984},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.46619999408721924},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.46309998631477356},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.44519999623298645},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4129999876022339},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.37070000171661377},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.337799996137619},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.3328999876976013},{"id":"https://openalex.org/C2781317605","wikidata":"https://www.wikidata.org/wiki/Q7832483","display_name":"Traffic analysis","level":2,"score":0.3221000134944916},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.29789999127388},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.2919999957084656},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.2842000126838684},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.28299999237060547},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.27880001068115234},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2775999903678894},{"id":"https://openalex.org/C103088060","wikidata":"https://www.wikidata.org/wiki/Q1062839","display_name":"Error detection and correction","level":2,"score":0.26919999718666077},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.2680000066757202},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jiot.2025.3636129","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2025.3636129","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":[],"awards":[{"id":"https://openalex.org/G6203638019","display_name":null,"funder_award_id":"DTC","funder_id":"https://openalex.org/F4320320709","funder_display_name":"National Research Foundation Singapore"}],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320320709","display_name":"National Research Foundation Singapore","ror":"https://ror.org/03cpyc314"},{"id":"https://openalex.org/F4320320746","display_name":"Infocomm Development Authority of Singapore","ror":"https://ror.org/04pyne868"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W2004353783","https://openalex.org/W2062978452","https://openalex.org/W2064675550","https://openalex.org/W2098291789","https://openalex.org/W2117014758","https://openalex.org/W2194775991","https://openalex.org/W2278186031","https://openalex.org/W2295598076","https://openalex.org/W2410123663","https://openalex.org/W2573587735","https://openalex.org/W2604847698","https://openalex.org/W2950635152","https://openalex.org/W2963507686","https://openalex.org/W2964010366","https://openalex.org/W2981096252","https://openalex.org/W3015228848","https://openalex.org/W3027414966","https://openalex.org/W3123760665","https://openalex.org/W3127291124","https://openalex.org/W3171884590","https://openalex.org/W3177318507","https://openalex.org/W4221163895","https://openalex.org/W4231136072","https://openalex.org/W4361275072","https://openalex.org/W4386566488","https://openalex.org/W4393145940","https://openalex.org/W4399999370","https://openalex.org/W4401539291","https://openalex.org/W4408519728","https://openalex.org/W4408611562"],"related_works":[],"abstract_inverted_index":{"The":[0],"explosive":[1],"growth":[2],"and":[3,16,28,43,53,73,109,123,141,164,186,212,215,222],"increasing":[4],"complexity":[5],"of":[6,136],"modern":[7],"5G/6G":[8],"networks,":[9],"driven":[10],"by":[11],"Internet-of-Things":[12],"(IoT),":[13],"industrial":[14],"automation,":[15],"real\u2011time":[17],"multimedia":[18],"streaming,":[19],"demand":[20],"forecasting":[21,93],"methods":[22],"that":[23,132],"address":[24],"non\u2011stationarity,":[25],"abrupt":[26,36,187],"shifts,":[27],"incomplete":[29],"observations.":[30],"Non\u2011stationarity":[31],"involves":[32],"changing":[33],"statistical":[34,51],"properties,":[35],"shifts":[37],"stem":[38],"from":[39,70],"events":[40,81],"or":[41,82],"outages,":[42],"data":[44,154],"gaps":[45],"can":[46],"impair":[47],"model":[48,130],"accuracy.":[49],"Traditional":[50],"models":[52],"deep":[54],"sequence":[55],"learners":[56],"partially":[57],"handle":[58],"these":[59,87],"challenges":[60],"but":[61],"often":[62],"leave":[63],"systematic":[64],"residuals,":[65],"which":[66],"are":[67],"structured":[68],"errors":[69],"unmodeled":[71],"scenarios":[72],"overlook":[74],"high\u2011level":[75],"contextual":[76],"cues":[77],"such":[78],"as":[79],"external":[80],"semantic":[83,134],"patterns.":[84],"To":[85],"overcome":[86],"limitations,":[88],"we":[89],"propose":[90],"a":[91,96,124,193,205],"hybrid":[92,176],"framework":[94],"combining":[95],"convolutional":[97],"neural":[98],"network\u2013long":[99],"short-term":[100],"memory":[101],"(CNN\u2013LSTM)":[102],"trend":[103,137],"predictor":[104],"for":[105,199],"capturing":[106],"local":[107],"fluctuations":[108],"long\u2011range":[110],"dependencies,":[111],"an":[112],"Extreme":[113],"Gradient":[114],"Boosting":[115],"(XGBoost)":[116],"residual":[117,146,207],"corrector":[118],"to":[119,144,157,174,182,202],"refine":[120],"forecast":[121],"errors,":[122],"Low-Rank":[125],"Adaptation":[126],"(LoRA)\u2011fine\u2011tuned":[127],"large":[128],"language":[129],"(LLM)":[131],"generates":[133],"labels":[135],"direction,":[138],"anomaly":[139],"type,":[140],"volatility":[142],"regime":[143],"enrich":[145],"learning.":[147],"Experimental":[148],"evaluation":[149],"on":[150],"real\u2011world":[151],"cellular":[152],"traffic":[153,188],"shows":[155],"up":[156],"15%":[158],"reduction":[159,166],"in":[160,167,224],"root-mean-square":[161],"error":[162,171],"(RMSE)":[163],"10%":[165],"mean":[168],"absolute":[169],"percentage":[170],"(MAPE)":[172],"compared":[173],"state\u2011of\u2011the\u2011art":[175],"baselines,":[177],"with":[178],"substantially":[179],"improved":[180],"resilience":[181],"noise,":[183],"missing":[184],"data,":[185],"surges.":[189],"Our":[190],"contributions":[191],"include":[192],"parameter\u2011efficient":[194],"prompt\u2011based":[195],"LoRA":[196],"fine\u2011tuning":[197],"pipeline":[198],"adapting":[200],"LLMs":[201],"time\u2011series":[203],"forecasting,":[204],"context\u2011aware":[206],"learning":[208],"architecture":[209],"fusing":[210],"numerical":[211],"linguistic":[213],"features,":[214],"comprehensive":[216],"empirical":[217],"validation":[218],"demonstrating":[219],"superior":[220],"accuracy":[221],"robustness":[223],"dynamic":[225],"network":[226],"environments.":[227]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-11-23T00:00:00"}
