{"id":"https://openalex.org/W7160961006","doi":"https://doi.org/10.1109/tii.2026.3669226","title":"A Hybrid End-to-End Neural Network for Accurate Global Natural Gas Trade Forecast","display_name":"A Hybrid End-to-End Neural Network for Accurate Global Natural Gas Trade Forecast","publication_year":2026,"publication_date":"2026-05-12","ids":{"openalex":"https://openalex.org/W7160961006","doi":"https://doi.org/10.1109/tii.2026.3669226"},"language":null,"primary_location":{"id":"doi:10.1109/tii.2026.3669226","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2026.3669226","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"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 Industrial Informatics","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/A5100596656","display_name":"Yu Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Yan","raw_affiliation_strings":["College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135932436","display_name":"Gang Lu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210094970","display_name":"Energy Research Institute","ror":"https://ror.org/00ndnb620","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094970","https://openalex.org/I4210142748"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gang Lu","raw_affiliation_strings":["Energy Strategy and Planning Research Department, State Grid Energy Research Institute Company, Ltd., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Energy Strategy and Planning Research Department, State Grid Energy Research Institute Company, Ltd., Beijing, China","institution_ids":["https://openalex.org/I4210094970"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135912994","display_name":"Xiaoqing Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210094970","display_name":"Energy Research Institute","ror":"https://ror.org/00ndnb620","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094970","https://openalex.org/I4210142748"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoqing Yan","raw_affiliation_strings":["Energy Strategy and Planning Research Department, State Grid Energy Research Institute Company, Ltd., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Energy Strategy and Planning Research Department, State Grid Energy Research Institute Company, Ltd., Beijing, China","institution_ids":["https://openalex.org/I4210094970"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135974764","display_name":"Peng Xia","orcid":null},"institutions":[{"id":"https://openalex.org/I4210094970","display_name":"Energy Research Institute","ror":"https://ror.org/00ndnb620","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094970","https://openalex.org/I4210142748"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Xia","raw_affiliation_strings":["Energy Strategy and Planning Research Department, State Grid Energy Research Institute Company, Ltd., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Energy Strategy and Planning Research Department, State Grid Energy Research Institute Company, Ltd., Beijing, China","institution_ids":["https://openalex.org/I4210094970"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011910065","display_name":"J Y Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jia Chen","raw_affiliation_strings":["School of Cyber Science and Technology, Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Cyber Science and Technology, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100404947","display_name":"Jie Yang","orcid":"https://orcid.org/0000-0003-4801-7162"},"institutions":[{"id":"https://openalex.org/I4210091787","display_name":"Zunyi Normal College","ror":"https://ror.org/00g2pnp92","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210091787"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Yang","raw_affiliation_strings":["School of Physics and Electronic Science, Zunyi Normal University, Zunyi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Physics and Electronic Science, Zunyi Normal University, Zunyi, China","institution_ids":["https://openalex.org/I4210091787"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5135979267","display_name":"Di Wu","orcid":"https://orcid.org/0000-0002-7788-9202"},"institutions":[{"id":"https://openalex.org/I142108993","display_name":"Southwest University","ror":"https://ror.org/01kj4z117","country_code":"CN","type":"education","lineage":["https://openalex.org/I142108993"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Di Wu","raw_affiliation_strings":["College of Computer and Information Science, Southwest University, Chongqing, China"],"raw_orcid":"https://orcid.org/0000-0002-7788-9202","affiliations":[{"raw_affiliation_string":"College of Computer and Information Science, Southwest University, Chongqing, China","institution_ids":["https://openalex.org/I142108993"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2645,"currency":"USD","value_usd":2645},"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.38371688,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"22","issue":"7","first_page":"5642","last_page":"5653"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.46070000529289246,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.46070000529289246,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.20440000295639038,"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/T11059","display_name":"Market Dynamics and Volatility","score":0.035100001841783524,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.671999990940094},{"id":"https://openalex.org/keywords/economic-forecasting","display_name":"Economic forecasting","score":0.32679998874664307},{"id":"https://openalex.org/keywords/hybrid-neural-network","display_name":"Hybrid neural network","score":0.32580000162124634},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.32330000400543213},{"id":"https://openalex.org/keywords/natural-gas","display_name":"Natural gas","score":0.311599999666214},{"id":"https://openalex.org/keywords/algorithm-design","display_name":"Algorithm design","score":0.29330000281333923}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.671999990940094},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5935999751091003},{"id":"https://openalex.org/C163068380","wikidata":"https://www.wikidata.org/wiki/Q3409313","display_name":"Economic forecasting","level":2,"score":0.32679998874664307},{"id":"https://openalex.org/C2779990667","wikidata":"https://www.wikidata.org/wiki/Q5953266","display_name":"Hybrid neural network","level":3,"score":0.32580000162124634},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.32330000400543213},{"id":"https://openalex.org/C59427239","wikidata":"https://www.wikidata.org/wiki/Q40858","display_name":"Natural gas","level":2,"score":0.311599999666214},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29350000619888306},{"id":"https://openalex.org/C106516650","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm design","level":2,"score":0.29330000281333923},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2703999876976013},{"id":"https://openalex.org/C2776608160","wikidata":"https://www.wikidata.org/wiki/Q4785462","display_name":"Natural (archaeology)","level":2,"score":0.2621000111103058},{"id":"https://openalex.org/C51675839","wikidata":"https://www.wikidata.org/wiki/Q1665681","display_name":"Intelligent Network","level":2,"score":0.26159998774528503},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2614000141620636},{"id":"https://openalex.org/C2986605239","wikidata":"https://www.wikidata.org/wiki/Q925667","display_name":"Numerical models","level":3,"score":0.25429999828338623},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.25380000472068787},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.2517000138759613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tii.2026.3669226","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2026.3669226","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"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 Industrial Informatics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G189390890","display_name":null,"funder_award_id":"62576289","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3012468280","display_name":null,"funder_award_id":"62302030","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":32,"referenced_works":["https://openalex.org/W2904481397","https://openalex.org/W2962693006","https://openalex.org/W2963241486","https://openalex.org/W2998116985","https://openalex.org/W2998313947","https://openalex.org/W3000669063","https://openalex.org/W3123855443","https://openalex.org/W3161791016","https://openalex.org/W3187534341","https://openalex.org/W3204508881","https://openalex.org/W4294068674","https://openalex.org/W4303446355","https://openalex.org/W4308155563","https://openalex.org/W4308345369","https://openalex.org/W4308736847","https://openalex.org/W4385076503","https://openalex.org/W4388838079","https://openalex.org/W4389799552","https://openalex.org/W4390589313","https://openalex.org/W4392016358","https://openalex.org/W4392948201","https://openalex.org/W4393371008","https://openalex.org/W4396542079","https://openalex.org/W4401814071","https://openalex.org/W4403435383","https://openalex.org/W4404191785","https://openalex.org/W4405000011","https://openalex.org/W4405926008","https://openalex.org/W4407275056","https://openalex.org/W4408423546","https://openalex.org/W4410166681","https://openalex.org/W4410716777"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"forecast":[1,147,163],"of":[2,26,49,126,167,185],"global":[3,38,68,198,219],"natural":[4,39,50,199,220],"gas":[5,40,200,221],"trade":[6,41,128,149,165,201],"is":[7,42],"crucial":[8],"for":[9,20,90,152,193],"a":[10,24,99],"country":[11],"to":[12,33,146,162,205],"promptly":[13],"optimize":[14],"and":[15,54,59,67,87,116,182,191],"adjust":[16],"its":[17],"energy":[18,21,52],"strategies":[19],"security.":[22],"Recently,":[23],"variety":[25],"machine":[27],"learning-based":[28],"approaches":[29,71],"have":[30],"been":[31],"adopted":[32],"address":[34],"this":[35,94,96],"issue.":[36],"However,":[37],"influenced":[43],"by":[44,79,134,175],"various":[45,131],"complex":[46],"(e.g.,":[47,62],"reserves":[48],"gas,":[51],"imports":[53],"exports,":[55],"economy,":[56],"population,":[57],"etc.)":[58],"uncertain":[60],"factors":[61,78],"climate":[63],"change,":[64],"international":[65],"turmoil,":[66],"pandemics).":[69],"Existing":[70],"either":[72],"partially":[73],"or":[74],"loosely":[75],"explore":[76],"these":[77,139],"nonend-to-end":[80],"learning,":[81,177],"resulting":[82],"in":[83,216],"suboptimal":[84],"feature":[85,180],"extraction":[86,181],"temporal":[88,113,144,186],"integration":[89,184],"forecasting.":[91,194],"To":[92],"fill":[93],"gap,":[95],"article":[97],"proposes":[98],"novel":[100],"hybrid":[101],"end-to-end":[102,176],"neural":[103],"network":[104,111,129,166],"(HENN)":[105],"with":[106,143,197],"three":[107,171],"components:":[108],"graph":[109],"attentional":[110],"(GAT),":[112],"transformer":[114],"(TT),":[115],"multilayer":[117],"perceptron":[118],"(MLP).":[119],"First,":[120],"GAT":[121],"learns":[122],"the":[123,148,153,159,164,179,183,209,218],"latent":[124,141],"features":[125,142],"annual":[127,140],"among":[130],"countries":[132],"year":[133],"year.":[135,155,169],"Second,":[136],"TT":[137],"integrates":[138],"information":[145,187],"network's":[150],"representations":[151,161],"upcoming":[154],"Third,":[156],"MLP":[157],"employs":[158],"forecasted":[160],"next":[168],"The":[170],"components":[172],"are":[173,188],"trained":[174],"ensuring":[178],"both":[189],"comprehensive":[190],"optimal":[192],"Extensive":[195],"experiments":[196],"data":[202],"from":[203],"2000":[204],"2023":[206],"show":[207],"that":[208],"proposed":[210],"HENN":[211],"significantly":[212],"outperforms":[213],"advanced":[214],"models":[215],"forecasting":[217],"trade.":[222]},"counts_by_year":[],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2026-05-13T00:00:00"}