{"id":"https://openalex.org/W4414359928","doi":"https://doi.org/10.24963/ijcai.2025/391","title":"DGraFormer: Dynamic Graph Learning Guided Multi-Scale Transformer for Multivariate Time Series Forecasting","display_name":"DGraFormer: Dynamic Graph Learning Guided Multi-Scale Transformer for Multivariate Time Series Forecasting","publication_year":2025,"publication_date":"2025-09-01","ids":{"openalex":"https://openalex.org/W4414359928","doi":"https://doi.org/10.24963/ijcai.2025/391"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2025/391","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/391","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","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/A5045222938","display_name":"Han Yan","orcid":"https://orcid.org/0000-0002-9242-1915"},"institutions":[{"id":"https://openalex.org/I59028903","display_name":"Ocean University of China","ror":"https://ror.org/04rdtx186","country_code":"CN","type":"education","lineage":["https://openalex.org/I59028903"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Han Yan","raw_affiliation_strings":["Ocean University of China"],"affiliations":[{"raw_affiliation_string":"Ocean University of China","institution_ids":["https://openalex.org/I59028903"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100687770","display_name":"Dongliang Chen","orcid":"https://orcid.org/0000-0001-5619-1270"},"institutions":[{"id":"https://openalex.org/I59028903","display_name":"Ocean University of China","ror":"https://ror.org/04rdtx186","country_code":"CN","type":"education","lineage":["https://openalex.org/I59028903"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongliang Chen","raw_affiliation_strings":["Ocean University of China"],"affiliations":[{"raw_affiliation_string":"Ocean University of China","institution_ids":["https://openalex.org/I59028903"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076931362","display_name":"Guiyuan Jiang","orcid":"https://orcid.org/0000-0002-1398-821X"},"institutions":[{"id":"https://openalex.org/I59028903","display_name":"Ocean University of China","ror":"https://ror.org/04rdtx186","country_code":"CN","type":"education","lineage":["https://openalex.org/I59028903"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guiyuan Jiang","raw_affiliation_strings":["Ocean University of China"],"affiliations":[{"raw_affiliation_string":"Ocean University of China","institution_ids":["https://openalex.org/I59028903"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100449066","display_name":"Bin Wang","orcid":"https://orcid.org/0000-0001-5265-1030"},"institutions":[{"id":"https://openalex.org/I59028903","display_name":"Ocean University of China","ror":"https://ror.org/04rdtx186","country_code":"CN","type":"education","lineage":["https://openalex.org/I59028903"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Wang","raw_affiliation_strings":["Ocean University of China"],"affiliations":[{"raw_affiliation_string":"Ocean University of China","institution_ids":["https://openalex.org/I59028903"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049926126","display_name":"Lei Cao","orcid":"https://orcid.org/0000-0001-9909-8607"},"institutions":[{"id":"https://openalex.org/I138006243","display_name":"University of Arizona","ror":"https://ror.org/03m2x1q45","country_code":"US","type":"education","lineage":["https://openalex.org/I138006243"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lei Cao","raw_affiliation_strings":["University of Arizona"],"affiliations":[{"raw_affiliation_string":"University of Arizona","institution_ids":["https://openalex.org/I138006243"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029633264","display_name":"Junyu Dong","orcid":"https://orcid.org/0000-0001-7012-2087"},"institutions":[{"id":"https://openalex.org/I59028903","display_name":"Ocean University of China","ror":"https://ror.org/04rdtx186","country_code":"CN","type":"education","lineage":["https://openalex.org/I59028903"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junyu Dong","raw_affiliation_strings":["Ocean University of China"],"affiliations":[{"raw_affiliation_string":"Ocean University of China","institution_ids":["https://openalex.org/I59028903"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068209849","display_name":"Yanwei Yu","orcid":"https://orcid.org/0000-0001-6941-2132"},"institutions":[{"id":"https://openalex.org/I59028903","display_name":"Ocean University of China","ror":"https://ror.org/04rdtx186","country_code":"CN","type":"education","lineage":["https://openalex.org/I59028903"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanwei Yu","raw_affiliation_strings":["Ocean University of China"],"affiliations":[{"raw_affiliation_string":"Ocean University of China","institution_ids":["https://openalex.org/I59028903"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5045222938"],"corresponding_institution_ids":["https://openalex.org/I59028903"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.33433055,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3516","last_page":"3524"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9729999899864197,"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":0.9729999899864197,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.680400013923645},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.579800009727478},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5770999789237976},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4984999895095825},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.43560001254081726},{"id":"https://openalex.org/keywords/dynamic-time-warping","display_name":"Dynamic time warping","score":0.39559999108314514},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35249999165534973}],"concepts":[{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.680400013923645},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6801000237464905},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.579800009727478},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5770999789237976},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4984999895095825},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47200000286102295},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4657999873161316},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44749999046325684},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.43560001254081726},{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.39559999108314514},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35249999165534973},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.3346000015735626},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.30070000886917114},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2782000005245209},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.27320000529289246},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.259799987077713},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2531000077724457},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2529999911785126}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2025/391","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/391","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","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":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multivariate":[0],"time":[1,60,90],"series":[2,61],"forecasting":[3],"is":[4,164],"a":[5,49],"critical":[6],"focus":[7],"across":[8,88,154],"many":[9],"fields.":[10],"Existing":[11],"transformer-based":[12],"models":[13,145],"have":[14,26],"overlooked":[15],"the":[16,23,31,38,101,121,151],"explicit":[17],"modeling":[18],"of":[19,34,67,103,161],"inter-variable":[20],"correlations.":[21],"Similarly,":[22],"graph-based":[24],"methods":[25],"also":[27],"failed":[28],"to":[29,84,99],"address":[30],"dynamic":[32,86],"nature":[33],"multivariate":[35,59],"correlations":[36,87],"and":[37,76,95,131],"noise":[39],"in":[40],"correlation":[41,128],"modeling.":[42],"To":[43],"overcome":[44],"these":[45],"challenges,":[46],"we":[47],"propose":[48],"novel":[50],"Dynamic":[51,71],"Graph":[52],"Learning":[53,74],"Guided":[54],"Multi-Scale":[55],"Transformer":[56],"(DGraFormer)":[57],"for":[58],"forecasting.":[62],"Specifically,":[63],"our":[64,162],"method":[65,123],"consists":[66],"two":[68],"main":[69],"components:":[70],"correlation-aware":[72],"graph":[73,129],"(DCGL)":[75],"multi-scale":[77,132],"temporal":[78,112,134],"transformer":[79],"(MTT).":[80],"The":[81,107,158],"former":[82],"aims":[83],"capture":[85,125],"different":[89],"windows,":[91],"filters":[92],"out":[93],"noise,":[94],"selects":[96],"key":[97],"weights":[98],"guide":[100],"aggregation":[102],"relevant":[104],"feature":[105],"representations.":[106],"latter":[108],"can":[109,124],"effectively":[110],"extract":[111],"patterns":[113],"from":[114],"patch":[115],"data":[116],"at":[117,166],"varying":[118],"scales.":[119],"Finally,":[120],"proposed":[122],"rich":[126],"local":[127],"structures":[130],"global":[133],"features.":[135],"Experimental":[136],"results":[137],"demonstrate":[138],"that":[139],"DGraformer":[140],"significantly":[141],"outperforms":[142],"existing":[143],"state-of-the-art":[144],"on":[146],"ten":[147],"real-world":[148],"datasets,":[149],"achieving":[150],"best":[152],"performance":[153],"multiple":[155],"evaluation":[156],"metrics.":[157],"source":[159],"code":[160],"model":[163],"available":[165],"\\url{https://anonymous.4open.science/r/DGraFormer}.":[167]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-10T00:00:00"}
