{"id":"https://openalex.org/W4401857089","doi":"https://doi.org/10.1145/3637528.3672055","title":"GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing","display_name":"GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing","publication_year":2024,"publication_date":"2024-08-24","ids":{"openalex":"https://openalex.org/W4401857089","doi":"https://doi.org/10.1145/3637528.3672055"},"language":"en","primary_location":{"id":"doi:10.1145/3637528.3672055","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3672055","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3672055","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3672055","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5090943298","display_name":"Chengqing Yu","orcid":"https://orcid.org/0000-0001-8314-8251"},"institutions":[{"id":"https://openalex.org/I4210090176","display_name":"Institute of Computing Technology","ror":"https://ror.org/0090r4d87","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210090176"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengqing Yu","raw_affiliation_strings":["Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-8314-8251","affiliations":[{"raw_affiliation_string":"Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210090176","https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100455817","display_name":"Fei Wang","orcid":"https://orcid.org/0000-0002-3282-0535"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Wang","raw_affiliation_strings":["Institute of Computig Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-3282-0535","affiliations":[{"raw_affiliation_string":"Institute of Computig Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056183845","display_name":"Zezhi Shao","orcid":"https://orcid.org/0000-0002-0815-2768"},"institutions":[{"id":"https://openalex.org/I4210090176","display_name":"Institute of Computing Technology","ror":"https://ror.org/0090r4d87","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210090176"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zezhi Shao","raw_affiliation_strings":["Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-0815-2768","affiliations":[{"raw_affiliation_string":"Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210090176","https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020332722","display_name":"Tangwen Qian","orcid":"https://orcid.org/0000-0001-5694-3831"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210090176","display_name":"Institute of Computing Technology","ror":"https://ror.org/0090r4d87","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210090176"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tangwen Qian","raw_affiliation_strings":["Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-5694-3831","affiliations":[{"raw_affiliation_string":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210090176","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100422985","display_name":"Zhao Zhang","orcid":"https://orcid.org/0000-0001-6680-160X"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210090176","display_name":"Institute of Computing Technology","ror":"https://ror.org/0090r4d87","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210090176"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhao Zhang","raw_affiliation_strings":["Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-6680-160X","affiliations":[{"raw_affiliation_string":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210090176","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100323842","display_name":"Wei Wei","orcid":"https://orcid.org/0000-0003-4488-0102"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Wei","raw_affiliation_strings":["School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0003-4488-0102","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103245119","display_name":"Yongjun Xu","orcid":"https://orcid.org/0000-0001-6647-0986"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210090176","display_name":"Institute of Computing Technology","ror":"https://ror.org/0090r4d87","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210090176"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongjun Xu","raw_affiliation_strings":["Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-6647-0986","affiliations":[{"raw_affiliation_string":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210090176","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":11.3117,"has_fulltext":false,"cited_by_count":37,"citation_normalized_percentile":{"value":0.99173247,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"3989","last_page":"4000"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9998000264167786,"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":0.9998000264167786,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9984999895095825,"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":0.9979000091552734,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.7323812246322632},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.6477683782577515},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.5882748365402222},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5503427982330322},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5331425070762634},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5034436583518982},{"id":"https://openalex.org/keywords/end-to-end-principle","display_name":"End-to-end principle","score":0.4525541365146637},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.38042914867401123},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.30484119057655334},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.28423985838890076},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.18237900733947754},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.08875957131385803}],"concepts":[{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.7323812246322632},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.6477683782577515},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.5882748365402222},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5503427982330322},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5331425070762634},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5034436583518982},{"id":"https://openalex.org/C74296488","wikidata":"https://www.wikidata.org/wiki/Q2527392","display_name":"End-to-end principle","level":2,"score":0.4525541365146637},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.38042914867401123},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30484119057655334},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28423985838890076},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.18237900733947754},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.08875957131385803},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3637528.3672055","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3672055","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3672055","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3637528.3672055","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3672055","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3672055","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.6600000262260437,"display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4401857089.pdf"},"referenced_works_count":62,"referenced_works":["https://openalex.org/W2194775991","https://openalex.org/W2901504064","https://openalex.org/W2924877262","https://openalex.org/W2941466173","https://openalex.org/W2964425131","https://openalex.org/W2965341826","https://openalex.org/W2997705255","https://openalex.org/W2997848713","https://openalex.org/W3023213286","https://openalex.org/W3080253043","https://openalex.org/W3101942798","https://openalex.org/W3165312697","https://openalex.org/W3166508292","https://openalex.org/W3173539742","https://openalex.org/W3174686248","https://openalex.org/W3193281533","https://openalex.org/W3201047833","https://openalex.org/W3202635391","https://openalex.org/W3208915345","https://openalex.org/W3210165781","https://openalex.org/W3212890323","https://openalex.org/W4210732327","https://openalex.org/W4214692098","https://openalex.org/W4220672066","https://openalex.org/W4224436100","https://openalex.org/W4225519437","https://openalex.org/W4283315029","https://openalex.org/W4283706581","https://openalex.org/W4285489795","https://openalex.org/W4289516034","https://openalex.org/W4289533938","https://openalex.org/W4289866548","https://openalex.org/W4290877088","https://openalex.org/W4306317258","https://openalex.org/W4306317966","https://openalex.org/W4308929797","https://openalex.org/W4312703862","https://openalex.org/W4312713717","https://openalex.org/W4313644197","https://openalex.org/W4319335604","https://openalex.org/W4319866170","https://openalex.org/W4320009727","https://openalex.org/W4321052080","https://openalex.org/W4321460300","https://openalex.org/W4323843555","https://openalex.org/W4365794555","https://openalex.org/W4366377753","https://openalex.org/W4379929595","https://openalex.org/W4380303532","https://openalex.org/W4381327046","https://openalex.org/W4381463107","https://openalex.org/W4382202895","https://openalex.org/W4382318091","https://openalex.org/W4382449675","https://openalex.org/W4386071805","https://openalex.org/W4387641291","https://openalex.org/W4387731421","https://openalex.org/W4387846717","https://openalex.org/W4387848755","https://openalex.org/W4391213212","https://openalex.org/W4392397350","https://openalex.org/W4403600951"],"related_works":["https://openalex.org/W4380150146","https://openalex.org/W3024870410","https://openalex.org/W2410652950","https://openalex.org/W4283773154","https://openalex.org/W3139174110","https://openalex.org/W4289597203","https://openalex.org/W2085630472","https://openalex.org/W1977098485","https://openalex.org/W4285201053","https://openalex.org/W3179968364"],"abstract_inverted_index":{"Multivariate":[0],"time":[1,198],"series":[2,199],"forecasting":[3,117],"(MTSF)":[4],"is":[5,76],"crucial":[6],"for":[7,148,193],"decision-making":[8],"to":[9,65,79,137,166],"precisely":[10,138],"forecast":[11],"the":[12,17,36,56,81,105,113,121,140,144,170,189,229],"future":[13,230],"values/trends,":[14],"based":[15],"on":[16,55,205],"complex":[18],"relationships":[19],"identified":[20],"from":[21,104],"historical":[22,59,83],"observations":[23,84],"of":[24,38,51,58,98,115,154,169,174,182,196,220,232],"multiple":[25],"sequences.":[26],"Recently,":[27],"Spatial-Temporal":[28],"Graph":[29,130],"Neural":[30],"Networks":[31],"(STGNNs)":[32],"have":[33],"gradually":[34],"become":[35],"theme":[37],"MTSF":[39],"model":[40,139],"as":[41,68],"their":[42,116],"powerful":[43],"capability":[44],"in":[45,112,123],"mining":[46],"spatial-temporal":[47,107,141,191],"dependencies,":[48],"but":[49],"almost":[50],"them":[52],"heavily":[53],"rely":[54],"assumption":[57],"data":[60,69,147],"integrity.":[61],"In":[62,89,150],"reality,":[63],"due":[64],"factors":[66],"such":[67],"collector":[70],"failures":[71],"and":[72,101,162,178,187,216],"time-consuming":[73],"repairment,":[74],"it":[75,152,224],"extremely":[77],"challenging":[78],"collect":[80],"whole":[82],"without":[85],"missing":[86,185],"any":[87],"variable.":[88],"this":[90,124],"case,":[91],"STGNNs":[92],"can":[93,225],"only":[94],"utilize":[95],"a":[96,128],"subset":[97],"normal":[99],"variables":[100,186,221],"easily":[102],"suffer":[103],"incorrect":[106],"dependency":[108],"modeling":[109,195],"issue,":[110],"resulting":[111],"degradation":[114],"performance.":[118],"To":[119],"address":[120],"problem,":[122],"paper,":[125],"we":[126],"propose":[127],"novel":[129],"Interpolation":[131],"Attention":[132],"Recursive":[133],"Network":[134],"(named":[135],"GinAR)":[136],"dependencies":[142,192],"over":[143],"limited":[145],"collected":[146],"forecasting.":[149],"GinAR,":[151],"consists":[153],"two":[155],"key":[156],"components,":[157],"that":[158,210],"is,":[159],"interpolation":[160],"attention":[161],"adaptive":[163],"graph":[164],"convolution":[165],"take":[167],"place":[168],"fully":[171],"connected":[172],"layer":[173],"simple":[175],"recursive":[176],"units,":[177],"thus":[179],"are":[180,222],"capable":[181],"recovering":[183],"all":[184,233],"reconstructing":[188],"correct":[190],"recursively":[194],"multivariate":[197],"data,":[200],"respectively.":[201],"Extensive":[202],"experiments":[203],"conducted":[204],"five":[206],"real-world":[207],"datasets":[208],"demonstrate":[209],"GinAR":[211],"outperforms":[212],"11":[213],"SOTA":[214],"baselines,":[215],"even":[217],"when":[218],"90%":[219],"missing,":[223],"still":[226],"accurately":[227],"predict":[228],"values":[231],"variables.":[234]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":22},{"year":2024,"cited_by_count":10}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}