{"id":"https://openalex.org/W4309951073","doi":"https://doi.org/10.1109/tkde.2022.3221989","title":"Multivariate Time Series Forecasting With Dynamic Graph Neural ODEs","display_name":"Multivariate Time Series Forecasting With Dynamic Graph Neural ODEs","publication_year":2022,"publication_date":"2022-11-14","ids":{"openalex":"https://openalex.org/W4309951073","doi":"https://doi.org/10.1109/tkde.2022.3221989"},"language":"en","primary_location":{"id":"doi:10.1109/tkde.2022.3221989","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2022.3221989","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Knowledge and Data Engineering","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/A5101811842","display_name":"Ming Jin","orcid":"https://orcid.org/0000-0002-6833-4811"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Ming Jin","raw_affiliation_strings":["Department of Data Science and AI, Faculty of IT, Monash University, Clayton, VIC, Australia"],"affiliations":[{"raw_affiliation_string":"Department of Data Science and AI, Faculty of IT, Monash University, Clayton, VIC, Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107092573","display_name":"Yu Zheng","orcid":"https://orcid.org/0000-0003-0757-4210"},"institutions":[{"id":"https://openalex.org/I196829312","display_name":"La Trobe University","ror":"https://ror.org/01rxfrp27","country_code":"AU","type":"education","lineage":["https://openalex.org/I196829312"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Yu Zheng","raw_affiliation_strings":["Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, Australia"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I196829312"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017943466","display_name":"Yuan-Fang Li","orcid":"https://orcid.org/0000-0003-4651-2821"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Yuan-Fang Li","raw_affiliation_strings":["Department of Data Science and AI, Faculty of IT, Monash University, Clayton, VIC, Australia"],"affiliations":[{"raw_affiliation_string":"Department of Data Science and AI, Faculty of IT, Monash University, Clayton, VIC, Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066373402","display_name":"Siheng Chen","orcid":"https://orcid.org/0000-0002-9144-0583"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siheng Chen","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072309548","display_name":"Bin Yang","orcid":"https://orcid.org/0000-0002-1658-1079"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Yang","raw_affiliation_strings":["School of Data Science and Engineering, East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Data Science and Engineering, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008056593","display_name":"Shirui Pan","orcid":"https://orcid.org/0000-0003-0794-527X"},"institutions":[{"id":"https://openalex.org/I11701301","display_name":"Griffith University","ror":"https://ror.org/02sc3r913","country_code":"AU","type":"education","lineage":["https://openalex.org/I11701301"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Shirui Pan","raw_affiliation_strings":["School of Information and Communication Technology, Griffith University, Southport, QLD, Australia"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Technology, Griffith University, Southport, QLD, Australia","institution_ids":["https://openalex.org/I11701301"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5101811842"],"corresponding_institution_ids":["https://openalex.org/I56590836"],"apc_list":null,"apc_paid":null,"fwci":21.5949,"has_fulltext":false,"cited_by_count":157,"citation_normalized_percentile":{"value":0.99866633,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"35","issue":"9","first_page":"9168","last_page":"9180"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9990000128746033,"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":0.9990000128746033,"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":0.9988999962806702,"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":0.9957000017166138,"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/computer-science","display_name":"Computer science","score":0.6987007856369019},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5371848344802856},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5312706232070923},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.4823419749736786},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4723908603191376},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.46604621410369873},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.46554479002952576},{"id":"https://openalex.org/keywords/ode","display_name":"Ode","score":0.44516581296920776},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.445041686296463},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.29008448123931885},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18694272637367249},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.18649983406066895},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.17026343941688538}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6987007856369019},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5371848344802856},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5312706232070923},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.4823419749736786},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4723908603191376},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.46604621410369873},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.46554479002952576},{"id":"https://openalex.org/C34862557","wikidata":"https://www.wikidata.org/wiki/Q178985","display_name":"Ode","level":2,"score":0.44516581296920776},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.445041686296463},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29008448123931885},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18694272637367249},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.18649983406066895},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.17026343941688538},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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":2,"locations":[{"id":"doi:10.1109/tkde.2022.3221989","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2022.3221989","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Knowledge and Data Engineering","raw_type":"journal-article"},{"id":"pmh:oai:research-repository.griffith.edu.au:10072/420775","is_oa":false,"landing_page_url":"http://hdl.handle.net/10072/420775","pdf_url":null,"source":{"id":"https://openalex.org/S4306402548","display_name":"Griffith Research Online (Griffith University, Queensland, Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I11701301","host_organization_name":"Griffith University","host_organization_lineage":["https://openalex.org/I11701301"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Journal article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.9200000166893005,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W1498436455","https://openalex.org/W1924770834","https://openalex.org/W2064675550","https://openalex.org/W2117014758","https://openalex.org/W2117829824","https://openalex.org/W2131774270","https://openalex.org/W2135591260","https://openalex.org/W2157331557","https://openalex.org/W2320477162","https://openalex.org/W2604847698","https://openalex.org/W2756203131","https://openalex.org/W2890096158","https://openalex.org/W2916106175","https://openalex.org/W2963358464","https://openalex.org/W2963755523","https://openalex.org/W2964015378","https://openalex.org/W2965341826","https://openalex.org/W2989915885","https://openalex.org/W2997848713","https://openalex.org/W2998559444","https://openalex.org/W3025713978","https://openalex.org/W3035805403","https://openalex.org/W3036926095","https://openalex.org/W3044150349","https://openalex.org/W3080253043","https://openalex.org/W3103720336","https://openalex.org/W3123909522","https://openalex.org/W3128461917","https://openalex.org/W3129863273","https://openalex.org/W3134210100","https://openalex.org/W3170140111","https://openalex.org/W3174823757","https://openalex.org/W3177318507","https://openalex.org/W3185846556","https://openalex.org/W4280535549","https://openalex.org/W4283817628","https://openalex.org/W4385245566","https://openalex.org/W6640212811","https://openalex.org/W6726873649","https://openalex.org/W6739901393","https://openalex.org/W6746015598","https://openalex.org/W6752307458","https://openalex.org/W6760001035","https://openalex.org/W6760979436","https://openalex.org/W6770064641","https://openalex.org/W6778353478","https://openalex.org/W6789531297","https://openalex.org/W6789720248","https://openalex.org/W6790465003","https://openalex.org/W6798549046","https://openalex.org/W6837660727"],"related_works":["https://openalex.org/W2371448224","https://openalex.org/W3134495997","https://openalex.org/W2363680170","https://openalex.org/W2376428685","https://openalex.org/W4256082577","https://openalex.org/W1861848143","https://openalex.org/W2118640767","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660"],"abstract_inverted_index":{"Multivariate":[0],"time":[1,157,222],"series":[2,127,158,223],"forecasting":[3,24,57],"has":[4],"long":[5],"received":[6],"significant":[7],"attention":[8],"in":[9,100],"real-world":[10,101],"applications,":[11],"such":[12],"as":[13],"energy":[14],"consumption":[15],"and":[16,41,55,72,79,98,166,173,183,187,195,203],"traffic":[17],"prediction.":[18],"While":[19],"recent":[20],"methods":[21],"demonstrate":[22,210],"good":[23],"abilities,":[25],"they":[26],"have":[27],"three":[28],"fundamental":[29],"limitations.":[30],"(i).":[31],"<italic":[32,61,83],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[33,62,84,120,124,131,136,140,144,149,215],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">Discrete":[34],"neural":[35,134,176],"architectures:</i>":[36],"Interlacing":[37],"individually":[38],"parameterized":[39],"spatial":[40,186],"temporal":[42,188,197],"blocks":[43],"to":[44,50,76,117,178,200],"encode":[45],"rich":[46],"underlying":[47],"patterns":[48],"leads":[49],"discontinuous":[51],"latent":[52,205],"state":[53],"trajectories":[54],"higher":[56,77],"numerical":[58],"errors.":[59],"(ii).":[60],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">High":[63],"complexity:</i>":[64],"Discrete":[65],"approaches":[66],"complicate":[67],"models":[68],"with":[69,128,162],"dedicated":[70],"designs":[71],"redundant":[73],"parameters,":[74],"leading":[75],"computational":[78],"memory":[80],"overheads.":[81],"(iii).":[82],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">Reliance":[85],"on":[86,90,220],"graph":[87,93,168,181,193],"priors:</i>":[88],"Relying":[89],"predefined":[91],"static":[92],"structures":[94],"limits":[95],"their":[96],"effectiveness":[97],"practicability":[99],"applications.":[102],"In":[103],"this":[104],"paper,":[105],"we":[106,153,171],"address":[107],"all":[108],"the":[109,211],"above":[110],"limitations":[111],"by":[112],"proposing":[113],"a":[114,175],"continuous":[115],"model":[116],"forecast":[118],"<underline":[119,123,130,135,139,143],"xmlns:xlink=\"http://www.w3.org/1999/xlink\"><b>M</b></u>":[121],"ultivariate":[122],"xmlns:xlink=\"http://www.w3.org/1999/xlink\"><b>T</b></u>":[125],"ime":[126],"dynamic":[129,160],"xmlns:xlink=\"http://www.w3.org/1999/xlink\"><b>G</b></u>":[132],"raph":[133],"xmlns:xlink=\"http://www.w3.org/1999/xlink\"><b>O</b></u>":[137],"rdinary":[138],"xmlns:xlink=\"http://www.w3.org/1999/xlink\"><b>D</b></u>":[141],"ifferential":[142],"xmlns:xlink=\"http://www.w3.org/1999/xlink\"><b>E</b></u>":[145],"quations":[146],"(":[147],"<monospace":[148,214],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">MTGODE</monospace>":[150,216],").":[151],"Specifically,":[152],"first":[154],"abstract":[155],"multivariate":[156],"into":[159],"graphs":[161],"time-evolving":[163],"node":[164],"features":[165],"unknown":[167],"structures.":[169],"Then,":[170],"design":[172],"solve":[174],"ODE":[177],"complement":[179],"missing":[180],"topologies":[182],"unify":[184],"both":[185],"message":[189],"passing,":[190],"allowing":[191],"deeper":[192],"propagation":[194],"fine-grained":[196],"information":[198],"aggregation":[199],"characterize":[201],"stable":[202],"precise":[204],"spatial-temporal":[206],"dynamics.":[207],"Our":[208],"experiments":[209],"superiorities":[212],"of":[213],"from":[217],"various":[218],"perspectives":[219],"five":[221],"benchmark":[224],"datasets.":[225]},"counts_by_year":[{"year":2026,"cited_by_count":12},{"year":2025,"cited_by_count":80},{"year":2024,"cited_by_count":44},{"year":2023,"cited_by_count":17},{"year":2022,"cited_by_count":4}],"updated_date":"2026-04-12T07:58:50.170612","created_date":"2025-10-10T00:00:00"}