{"id":"https://openalex.org/W7092299577","doi":"https://doi.org/10.1109/tsipn.2025.3622886","title":"Score-Based Variational Modeling for Spatiotemporal Forecasting","display_name":"Score-Based Variational Modeling for Spatiotemporal Forecasting","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W7092299577","doi":"https://doi.org/10.1109/tsipn.2025.3622886"},"language":null,"primary_location":{"id":"doi:10.1109/tsipn.2025.3622886","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsipn.2025.3622886","pdf_url":null,"source":{"id":"https://openalex.org/S4306422866","display_name":"IEEE Transactions on Signal and Information Processing over Networks","issn_l":"2373-776X","issn":["2373-776X","2373-7778"],"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 Signal and Information Processing over Networks","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":"Jia Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jia Zhou","raw_affiliation_strings":["College of Information Science and Engineering, Hohai University, Changzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Hohai University, Changzhou, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Aimin Jiang","orcid":"https://orcid.org/0000-0002-9181-934X"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Aimin Jiang","raw_affiliation_strings":["College of Information Science and Engineering, Hohai University, Changzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-9181-934X","affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Hohai University, Changzhou, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chang Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chang Liu","raw_affiliation_strings":["College of Information Science and Engineering, Hohai University, Changzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Hohai University, Changzhou, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ning Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ning Xu","raw_affiliation_strings":["College of Information Science and Engineering, Hohai University, Changzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Hohai University, Changzhou, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"last","author":{"id":null,"display_name":"Yanping Zhu","orcid":"https://orcid.org/0000-0002-8107-0101"},"institutions":[{"id":"https://openalex.org/I4210153482","display_name":"Changzhou University","ror":"https://ror.org/04ymgwq66","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210153482"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanping Zhu","raw_affiliation_strings":["School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-8107-0101","affiliations":[{"raw_affiliation_string":"School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China","institution_ids":["https://openalex.org/I4210153482"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"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":0.45244666,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"11","issue":null,"first_page":"1368","last_page":"1381"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.718999981880188,"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":0.718999981880188,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.04500000178813934,"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"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.03999999910593033,"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/robustness","display_name":"Robustness (evolution)","score":0.6881999969482422},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4803999960422516},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.47189998626708984},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4101000130176544},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.3783000111579895},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.34700000286102295},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.3188999891281128}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7475000023841858},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6881999969482422},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4803999960422516},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.47189998626708984},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.453000009059906},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4226999878883362},{"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/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3783000111579895},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3560999929904938},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.34700000286102295},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.3188999891281128},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3107999861240387},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29319998621940613},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.2824000120162964},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2549999952316284}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsipn.2025.3622886","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsipn.2025.3622886","pdf_url":null,"source":{"id":"https://openalex.org/S4306422866","display_name":"IEEE Transactions on Signal and Information Processing over Networks","issn_l":"2373-776X","issn":["2373-776X","2373-7778"],"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 Signal and Information Processing over Networks","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4300683971","display_name":null,"funder_award_id":"62571178","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":41,"referenced_works":["https://openalex.org/W2031674781","https://openalex.org/W2528639018","https://openalex.org/W2604847698","https://openalex.org/W2756203131","https://openalex.org/W2788134583","https://openalex.org/W2808535700","https://openalex.org/W2809035759","https://openalex.org/W2900749811","https://openalex.org/W2901504064","https://openalex.org/W2903871660","https://openalex.org/W2962790412","https://openalex.org/W2965341826","https://openalex.org/W2988244882","https://openalex.org/W2991178611","https://openalex.org/W2996847713","https://openalex.org/W3024186890","https://openalex.org/W3039628929","https://openalex.org/W3080253043","https://openalex.org/W3089455215","https://openalex.org/W3112062303","https://openalex.org/W3133663379","https://openalex.org/W3170140111","https://openalex.org/W3195017936","https://openalex.org/W4210858069","https://openalex.org/W4281965008","https://openalex.org/W4283706581","https://openalex.org/W4312347576","https://openalex.org/W4312933868","https://openalex.org/W4319594598","https://openalex.org/W4365443833","https://openalex.org/W4372351183","https://openalex.org/W4379116774","https://openalex.org/W4382239668","https://openalex.org/W4385270120","https://openalex.org/W4390100390","https://openalex.org/W4394625831","https://openalex.org/W4394730888","https://openalex.org/W4396601427","https://openalex.org/W4396757510","https://openalex.org/W4400447734","https://openalex.org/W4404611842"],"related_works":[],"abstract_inverted_index":{"Spatiotemporal":[0],"data":[1,90,117,123],"exhibit":[2],"intricate":[3],"dependencies":[4,114],"across":[5],"both":[6],"spatial":[7,39],"and":[8,40,43,51,74,102,131,138,160,177],"temporal":[9,41],"dimensions,":[10],"making":[11],"it":[12],"crucial":[13],"to":[14,30,45,84,141,154],"capture":[15,112],"these":[16,58],"relationships":[17],"accurately":[18],"for":[19,48],"improved":[20],"forecasting":[21],"performance.":[22],"While":[23],"deep":[24],"learning":[25],"models":[26],"are":[27],"extensively":[28],"applied":[29],"spatiotemporal":[31,116,180],"sequence":[32],"prediction,":[33],"most":[34],"existing":[35,172],"approaches":[36],"separately":[37],"model":[38,98],"features":[42],"fail":[44],"adequately":[46],"account":[47],"inherent":[49],"uncertainties":[50],"noise":[52],"in":[53,115,121,179],"the":[54,62,107,126,129],"data.":[55],"To":[56],"address":[57],"limitations,":[59],"we":[60],"propose":[61],"Score-based":[63],"Spatio-Temporal":[64],"Variational":[65,70],"Model":[66],"(SSTVM),":[67],"which":[68],"combines":[69],"Graph":[71,134],"Auto-Encoders":[72],"(VGAEs)":[73],"Stochastic":[75],"Differential":[76],"Equations":[77],"(SDEs).":[78],"SSTVM":[79,132,150,170],"utilizes":[80],"a":[81,92,96,155],"preprocessing":[82],"module":[83],"generate":[85],"high-quality":[86],"inputs,":[87],"mapping":[88],"high-dimensional":[89],"into":[91],"latent":[93],"space":[94],"where":[95],"diffusion":[97],"simulates":[99],"dynamic":[100],"changes":[101],"uncertainties.":[103],"In":[104],"this":[105],"way,":[106],"proposed":[108],"approach":[109],"can":[110],"effectively":[111],"global":[113],"while":[118],"maintaining":[119],"robustness":[120,178],"sparse":[122],"scenarios.":[124],"Furthermore,":[125],"decoder":[127],"generates":[128],"graph,":[130],"leverages":[133],"Convolutional":[135],"Networks":[136],"(GCNs)":[137],"attention":[139],"mechanisms":[140],"predict":[142],"future":[143],"states.":[144],"The":[145],"flexible":[146],"encoder-decoder":[147],"architecture":[148],"of":[149,158],"ensures":[151],"seamless":[152],"adaptation":[153],"wide":[156],"range":[157],"scenarios":[159],"tasks.":[161],"Experimental":[162],"results":[163],"on":[164],"multiple":[165],"real-world":[166],"datasets":[167],"demonstrate":[168],"that":[169],"outperforms":[171],"methods,":[173],"highlighting":[174],"its":[175],"effectiveness":[176],"modeling.":[181]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-18T00:00:00"}