{"id":"https://openalex.org/W4360604834","doi":"https://doi.org/10.1109/icnc57223.2023.10074369","title":"Evaluation of Different Time Series Forecasting Models for 5G V2V Networks","display_name":"Evaluation of Different Time Series Forecasting Models for 5G V2V Networks","publication_year":2023,"publication_date":"2023-02-20","ids":{"openalex":"https://openalex.org/W4360604834","doi":"https://doi.org/10.1109/icnc57223.2023.10074369"},"language":"en","primary_location":{"id":"doi:10.1109/icnc57223.2023.10074369","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnc57223.2023.10074369","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Conference on Computing, Networking and Communications (ICNC)","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/A5100414623","display_name":"Jian Liu","orcid":"https://orcid.org/0000-0001-7484-401X"},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jian Liu","raw_affiliation_strings":["University of South Carolina,Dept. of Computer Science and Engineering,Columbia,South Carolina,USA","Dept. of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of South Carolina,Dept. of Computer Science and Engineering,Columbia,South Carolina,USA","institution_ids":["https://openalex.org/I155781252"]},{"raw_affiliation_string":"Dept. of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina, USA","institution_ids":["https://openalex.org/I155781252"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056673342","display_name":"Chin\u2010Tser Huang","orcid":"https://orcid.org/0000-0003-3983-972X"},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chin-Tser Huang","raw_affiliation_strings":["University of South Carolina,Dept. of Computer Science and Engineering,Columbia,South Carolina,USA","Dept. of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of South Carolina,Dept. of Computer Science and Engineering,Columbia,South Carolina,USA","institution_ids":["https://openalex.org/I155781252"]},{"raw_affiliation_string":"Dept. of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina, USA","institution_ids":["https://openalex.org/I155781252"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I155781252"],"apc_list":null,"apc_paid":null,"fwci":0.3637,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.56921627,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"278","last_page":"282"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.9959999918937683,"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"}},"topics":[{"id":"https://openalex.org/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.9959999918937683,"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"}},{"id":"https://openalex.org/T12146","display_name":"Power Line Communications and Noise","score":0.9933000206947327,"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"}},{"id":"https://openalex.org/T10936","display_name":"Millimeter-Wave Propagation and Modeling","score":0.9914000034332275,"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/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.7859041690826416},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6915299296379089},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5789448022842407},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.48178812861442566},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46794596314430237},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4675546884536743},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35483336448669434},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32666879892349243}],"concepts":[{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.7859041690826416},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6915299296379089},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5789448022842407},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.48178812861442566},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46794596314430237},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4675546884536743},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35483336448669434},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32666879892349243}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icnc57223.2023.10074369","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnc57223.2023.10074369","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Conference on Computing, Networking and Communications (ICNC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2064675550","https://openalex.org/W2164106087","https://openalex.org/W2255466643","https://openalex.org/W2613625031","https://openalex.org/W2755087487","https://openalex.org/W2917716869","https://openalex.org/W2954586649","https://openalex.org/W2976765088","https://openalex.org/W2982064046","https://openalex.org/W3034140633","https://openalex.org/W3091180885","https://openalex.org/W3096945920","https://openalex.org/W3106302305","https://openalex.org/W3132856834","https://openalex.org/W3162348573","https://openalex.org/W4229058446","https://openalex.org/W6742924651"],"related_works":["https://openalex.org/W4312200629","https://openalex.org/W4223943233","https://openalex.org/W4360585206","https://openalex.org/W4364306694","https://openalex.org/W4225161397","https://openalex.org/W3014300295","https://openalex.org/W3164822677","https://openalex.org/W4250304930","https://openalex.org/W4309045103","https://openalex.org/W4213225422"],"abstract_inverted_index":{"The":[0,43,138],"automated":[1,26,85],"driving":[2,27,86],"system":[3,87],"is":[4,50,75,170],"revolutionizing":[5],"transportation,":[6],"and":[7,9,62,64,121,159],"more":[8,10],"companies":[11],"are":[12,115],"investing":[13],"in":[14,25,47,93,143],"this":[15,96,144],"technology.":[16],"5G":[17,29,104,134],"wireless":[18],"network":[19],"will":[20],"play":[21],"a":[22,70,79],"fundamental":[23],"role":[24],"since":[28],"uses":[30],"millimeter":[31],"waves":[32],"to":[33,69,77,88,101,132],"provide":[34],"high":[35],"data":[36],"transfer":[37],"rates":[38],"for":[39,83,90,175],"Vehicle-to-Vehicle":[40],"(V2V)":[41],"communications.":[42],"performance":[44,91],"of":[45],"communications":[46],"5GV2V":[48,176],"networks":[49],"often":[51],"affected":[52],"by":[53],"harsh":[54,180],"weather":[55,111],"conditions,":[56],"such":[57],"as":[58,117],"rain,":[59],"snow,":[60],"sand,":[61],"dust,":[63],"the":[65,84,103,171],"impact":[66],"could":[67],"be":[68],"large":[71],"extent.":[72],"Therefore,":[73],"it":[74],"crucial":[76],"have":[78],"precise":[80],"forecasting":[81,130,139,173],"model":[82,174],"prepare":[89],"degradation":[92],"advance.":[94],"In":[95],"paper,":[97],"we":[98,122,141],"use":[99],"NS-3":[100],"simulate":[102],"mm-Wave":[105,135],"V2V":[106],"signal":[107,136,177],"strength":[108,178],"under":[109,179],"different":[110],"conditions.":[112],"Weather":[113],"conditions":[114],"represented":[116],"time":[118],"series":[119],"data,":[120],"propose":[123],"using":[124],"various":[125],"statistical":[126],"or":[127],"machine":[128],"learning":[129],"models":[131,140],"predict":[133],"strength.":[137],"evaluate":[142],"paper":[145],"include":[146],"auto":[147],"regressive":[148],"integrated":[149],"moving":[150],"average":[151],"(ARIMA),":[152],"Meta":[153],"Prophet,":[154],"long":[155],"short-term":[156],"memory":[157],"(LSTM),":[158],"gated":[160],"recurrent":[161],"unit":[162],"(GRU).":[163],"Our":[164],"evaluation":[165],"results":[166],"show":[167],"that":[168],"LSTM":[169],"best":[172],"weather.":[181]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
