{"id":"https://openalex.org/W4403420492","doi":"https://doi.org/10.1109/tgrs.2024.3480888","title":"Predicting Global Average Temperature Time Series Using an Entire Graph Node Training Approach","display_name":"Predicting Global Average Temperature Time Series Using an Entire Graph Node Training Approach","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4403420492","doi":"https://doi.org/10.1109/tgrs.2024.3480888"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2024.3480888","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2024.3480888","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Geoscience and Remote Sensing","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/A5006895046","display_name":"Zhiguo Wang","orcid":"https://orcid.org/0000-0003-0343-7278"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiguo Wang","raw_affiliation_strings":["School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, Shaanxi, China"],"raw_orcid":"https://orcid.org/0000-0003-0343-7278","affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, Shaanxi, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017774264","display_name":"Ziwei Chen","orcid":"https://orcid.org/0000-0003-4418-7121"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziwei Chen","raw_affiliation_strings":["School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, China"],"raw_orcid":"https://orcid.org/0000-0003-4418-7121","affiliations":[{"raw_affiliation_string":"School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109697181","display_name":"Zihao Shi","orcid":null},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zihao Shi","raw_affiliation_strings":["School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, Shaanxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, Shaanxi, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077552109","display_name":"Jinghuai Gao","orcid":"https://orcid.org/0000-0002-0448-8555"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinghuai Gao","raw_affiliation_strings":["School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, China"],"raw_orcid":"https://orcid.org/0000-0002-0448-8555","affiliations":[{"raw_affiliation_string":"School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, China","institution_ids":["https://openalex.org/I87445476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I87445476"],"apc_list":null,"apc_paid":null,"fwci":0.8446,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.72384011,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"62","issue":null,"first_page":"1","last_page":"14"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9760000109672546,"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"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9760000109672546,"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/T10320","display_name":"Neural Networks and Applications","score":0.9233999848365784,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6398191452026367},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5955083966255188},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5947625041007996},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.527775764465332},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5214751958847046},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.4631717801094055},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3944091498851776},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3919403851032257},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34447401762008667},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.27785995602607727},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.22754141688346863},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.21705931425094604},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.2143014371395111}],"concepts":[{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6398191452026367},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5955083966255188},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5947625041007996},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.527775764465332},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5214751958847046},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4631717801094055},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3944091498851776},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3919403851032257},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34447401762008667},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27785995602607727},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.22754141688346863},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.21705931425094604},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.2143014371395111},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2024.3480888","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2024.3480888","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.7799999713897705}],"awards":[{"id":"https://openalex.org/G650469707","display_name":null,"funder_award_id":"42474139","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":62,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1789155650","https://openalex.org/W1901129140","https://openalex.org/W1971696102","https://openalex.org/W1982140334","https://openalex.org/W2027343789","https://openalex.org/W2051196068","https://openalex.org/W2055538060","https://openalex.org/W2064675550","https://openalex.org/W2078580016","https://openalex.org/W2087946919","https://openalex.org/W2108184065","https://openalex.org/W2108527489","https://openalex.org/W2110242546","https://openalex.org/W2116341502","https://openalex.org/W2131774270","https://openalex.org/W2148650246","https://openalex.org/W2165242200","https://openalex.org/W2167018289","https://openalex.org/W2167220187","https://openalex.org/W2194775991","https://openalex.org/W2333252900","https://openalex.org/W2599542682","https://openalex.org/W2609881461","https://openalex.org/W2619995677","https://openalex.org/W2738516213","https://openalex.org/W2778580105","https://openalex.org/W2796431263","https://openalex.org/W2797162213","https://openalex.org/W2805976575","https://openalex.org/W2899816880","https://openalex.org/W2913323966","https://openalex.org/W2914034186","https://openalex.org/W2954996726","https://openalex.org/W2963768879","https://openalex.org/W2968972294","https://openalex.org/W3015153359","https://openalex.org/W3025949386","https://openalex.org/W3039204554","https://openalex.org/W3046391772","https://openalex.org/W3112414201","https://openalex.org/W3152893301","https://openalex.org/W3171145878","https://openalex.org/W3182706339","https://openalex.org/W4213327538","https://openalex.org/W4214949671","https://openalex.org/W4233976936","https://openalex.org/W4255501777","https://openalex.org/W4283313907","https://openalex.org/W4285682248","https://openalex.org/W4309889704","https://openalex.org/W4311144837","https://openalex.org/W4383218913","https://openalex.org/W4388654737","https://openalex.org/W4394643672","https://openalex.org/W4401820038","https://openalex.org/W6631190155","https://openalex.org/W6720006811","https://openalex.org/W6726873649","https://openalex.org/W6752110883","https://openalex.org/W6764679822","https://openalex.org/W6768314895"],"related_works":["https://openalex.org/W230091440","https://openalex.org/W2233261550","https://openalex.org/W2810751659","https://openalex.org/W258997015","https://openalex.org/W2997094352","https://openalex.org/W3216976533","https://openalex.org/W2119012848","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660"],"abstract_inverted_index":{"The":[0,182],"data-driven":[1],"approach":[2],"has":[3],"become":[4],"significant":[5,40],"in":[6,88,178],"various":[7],"scientific":[8],"fields,":[9],"such":[10],"as":[11,136,202,216],"climate":[12,175],"modeling":[13],"and":[14,23,112,143,165,206],"weather":[15],"forecasting,":[16],"where":[17],"a":[18,39,83,100,107,113,137,203,217],"mechanistic":[19],"description":[20],"of":[21,46],"physics":[22],"chemistry":[24],"is":[25,185],"either":[26],"unavailable":[27],"or":[28],"insufficient":[29],"for":[30,140,209,219],"the":[31,35,43,70,89,95,123,148,174,179,193,198],"desired":[32],"purpose.":[33],"However,":[34],"prominent":[36],"nonstationarity":[37],"poses":[38],"challenge":[41],"to":[42,75,172],"accurate":[44],"prediction":[45,160,167],"recent":[47],"global":[48,210],"average":[49],"temperature":[50,190,211],"(GAT)":[51],"using":[52,147,224],"conventional":[53],"methods.":[54],"To":[55],"address":[56],"this":[57],"challenge,":[58],"we":[59,68,93,121],"draw":[60],"inspiration":[61],"from":[62],"signal":[63],"analysis\u2019s":[64],"moving-window":[65],"approach,":[66],"wherein":[67],"split":[69],"GAT":[71],"into":[72,82],"shorter":[73],"segments":[74,79],"alleviate":[76],"nonstationarity.":[77],"These":[78],"are":[80],"transformed":[81],"time-symmetric":[84],"graph":[85,101,125,138,225],"(TSG)":[86],"structure":[87],"non-Euclidean":[90],"domain.":[91],"Consequently,":[92,192],"introduce":[94],"GlobalTempNet":[96,153],"model,":[97],"which":[98],"incorporates":[99],"convolutional":[102],"network":[103,110],"(GCN)":[104],"embedded":[105],"with":[106,197],"residual":[108],"neural":[109],"(NN)":[111],"long":[114],"short-term":[115],"memory":[116],"(LSTM)":[117],"network.":[118],"In":[119],"addition,":[120],"propose":[122],"entire":[124],"node":[126,139],"training":[127],"(EGNT)":[128],"process,":[129,200],"optimizing":[130],"parameters":[131],"by":[132],"treating":[133],"each":[134],"sample":[135],"feature":[141],"aggregation":[142],"information":[144],"updating.":[145],"Validation":[146],"HadCRUT5":[149],"dataset":[150],"demonstrates":[151],"that":[152],"outperforms":[154],"nine":[155],"established":[156],"models,":[157],"showcasing":[158],"higher":[159],"accuracy.":[161],"Furthermore,":[162],"long-term":[163],"estimation":[164],"future":[166],"analyses":[168],"reveal":[169],"GlobalTempNet\u2019s":[170],"capability":[171],"predict":[173],"change":[176],"trend":[177],"coming":[180],"years.":[181],"model\u2019s":[183],"applicability":[184],"confirmed":[186],"across":[187],"ten":[188],"different":[189],"datasets.":[191],"proposed":[194],"GlobalTempNet,":[195],"coupled":[196],"EGNT":[199],"emerges":[201],"robust,":[204],"reliable,":[205],"open-source":[207],"method":[208],"prediction,":[212],"offering":[213],"promising":[214],"potential":[215],"tool":[218],"univariate":[220],"time":[221],"series":[222],"analysis":[223],"NNs":[226],"(GNNs).":[227]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-10-10T00:00:00"}