{"id":"https://openalex.org/W4319998087","doi":"https://doi.org/10.1109/mis.2023.3239797","title":"Irregularly Sampled Multivariate Time Series Classification: A Graph Learning Approach","display_name":"Irregularly Sampled Multivariate Time Series Classification: A Graph Learning Approach","publication_year":2023,"publication_date":"2023-01-26","ids":{"openalex":"https://openalex.org/W4319998087","doi":"https://doi.org/10.1109/mis.2023.3239797"},"language":"en","primary_location":{"id":"doi:10.1109/mis.2023.3239797","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mis.2023.3239797","pdf_url":null,"source":{"id":"https://openalex.org/S114241109","display_name":"IEEE Intelligent Systems","issn_l":"1541-1672","issn":["1541-1672","1941-1294"],"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 Intelligent Systems","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/A5100422381","display_name":"Zhen Wang","orcid":"https://orcid.org/0000-0002-8637-8375"},"institutions":[{"id":"https://openalex.org/I4210123185","display_name":"Zhejiang Lab","ror":"https://ror.org/02m2h7991","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210123185"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhen Wang","raw_affiliation_strings":["Zhejiang Lab, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-8637-8375","affiliations":[{"raw_affiliation_string":"Zhejiang Lab, Hangzhou, China","institution_ids":["https://openalex.org/I4210123185"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102019380","display_name":"Ting Jiang","orcid":"https://orcid.org/0000-0003-4925-2033"},"institutions":[{"id":"https://openalex.org/I4210123185","display_name":"Zhejiang Lab","ror":"https://ror.org/02m2h7991","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210123185"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ting Jiang","raw_affiliation_strings":["Zhejiang Lab, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-4925-2033","affiliations":[{"raw_affiliation_string":"Zhejiang Lab, Hangzhou, China","institution_ids":["https://openalex.org/I4210123185"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069575392","display_name":"Zenghui Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210123185","display_name":"Zhejiang Lab","ror":"https://ror.org/02m2h7991","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210123185"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zenghui Xu","raw_affiliation_strings":["Zhejiang Lab, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang Lab, Hangzhou, China","institution_ids":["https://openalex.org/I4210123185"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100705326","display_name":"Ji Zhang","orcid":"https://orcid.org/0000-0001-7167-6970"},"institutions":[{"id":"https://openalex.org/I185523456","display_name":"University of Southern Queensland","ror":"https://ror.org/04sjbnx57","country_code":"AU","type":"education","lineage":["https://openalex.org/I185523456"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Ji Zhang","raw_affiliation_strings":["University of Southern Queensland, Toowoomba, QLD, Australia"],"raw_orcid":"https://orcid.org/0000-0001-7167-6970","affiliations":[{"raw_affiliation_string":"University of Southern Queensland, Toowoomba, QLD, Australia","institution_ids":["https://openalex.org/I185523456"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060720664","display_name":"Jianliang Gao","orcid":"https://orcid.org/0000-0002-9363-9908"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianliang Gao","raw_affiliation_strings":["Central South University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0002-9363-9908","affiliations":[{"raw_affiliation_string":"Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2995,"currency":"USD","value_usd":2995},"apc_paid":null,"fwci":1.1769,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.765116,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"38","issue":"3","first_page":"3","last_page":"11"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9994000196456909,"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":0.9994000196456909,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.995199978351593,"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/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9793000221252441,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8142244815826416},{"id":"https://openalex.org/keywords/timestamp","display_name":"Timestamp","score":0.7161612510681152},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.64485764503479},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5971385836601257},{"id":"https://openalex.org/keywords/asynchronous-communication","display_name":"Asynchronous communication","score":0.5964785814285278},{"id":"https://openalex.org/keywords/temporal-database","display_name":"Temporal database","score":0.5460067987442017},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5227877497673035},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5225793123245239},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4200010299682617},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.350737988948822},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34165650606155396}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8142244815826416},{"id":"https://openalex.org/C113954288","wikidata":"https://www.wikidata.org/wiki/Q186885","display_name":"Timestamp","level":2,"score":0.7161612510681152},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.64485764503479},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5971385836601257},{"id":"https://openalex.org/C151319957","wikidata":"https://www.wikidata.org/wiki/Q752739","display_name":"Asynchronous communication","level":2,"score":0.5964785814285278},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.5460067987442017},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5227877497673035},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5225793123245239},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4200010299682617},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.350737988948822},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34165650606155396},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mis.2023.3239797","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mis.2023.3239797","pdf_url":null,"source":{"id":"https://openalex.org/S114241109","display_name":"IEEE Intelligent Systems","issn_l":"1541-1672","issn":["1541-1672","1941-1294"],"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 Intelligent Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1485009520","https://openalex.org/W2106981782","https://openalex.org/W2161664683","https://openalex.org/W2742491462","https://openalex.org/W2952179887","https://openalex.org/W2963532813","https://openalex.org/W2964010366","https://openalex.org/W2964959375","https://openalex.org/W2973432116","https://openalex.org/W2976348667","https://openalex.org/W2997353686","https://openalex.org/W3081360282","https://openalex.org/W3094497296","https://openalex.org/W3095306945","https://openalex.org/W3118676997","https://openalex.org/W3129938072","https://openalex.org/W3134794314","https://openalex.org/W3156650687","https://openalex.org/W3205978767","https://openalex.org/W4288364163","https://openalex.org/W4385245566","https://openalex.org/W6628877408","https://openalex.org/W6684008259","https://openalex.org/W6739901393","https://openalex.org/W6759258263","https://openalex.org/W6762009602","https://openalex.org/W6768707575","https://openalex.org/W6780817209","https://openalex.org/W6784530016","https://openalex.org/W6787569234","https://openalex.org/W6802566335"],"related_works":["https://openalex.org/W2483088531","https://openalex.org/W2950359320","https://openalex.org/W4205519360","https://openalex.org/W2384492104","https://openalex.org/W1591675436","https://openalex.org/W3199125616","https://openalex.org/W2966366588","https://openalex.org/W3128634608","https://openalex.org/W2354804553","https://openalex.org/W3083250332"],"abstract_inverted_index":{"To":[0,59],"date,":[1],"graph-based":[2],"learning":[3],"methods":[4],"are":[5],"proven":[6],"to":[7,19,27,70,93,114,130],"be":[8],"effective":[9],"for":[10,56],"modeling":[11],"spatial":[12],"and":[13,46,78,98,136],"structural":[14,73],"dependencies.":[15],"However,":[16],"when":[17],"applied":[18],"IS-MTS,":[20],"they":[21],"encounter":[22],"three":[23],"major":[24],"challenges":[25],"due":[26],"the":[28,100,116,133,147,151],"complex":[29],"data":[30],"characteristics":[31,81],"of":[32,50,53,82,150],"IS-MTS:":[33],"1)":[34],"variable":[35],"time":[36,42],"intervals":[37],"between":[38],"observations;":[39],"2)":[40],"asynchronous":[41],"points":[43],"across":[44],"dimensions;":[45],"3)":[47],"a":[48,65,89,108,125],"lack":[49],"prior":[51],"knowledge":[52],"connectivity":[54],"structure":[55,102],"message":[57],"propagation.":[58],"fill":[60],"these":[61],"gaps,":[62],"we":[63,86,106,123,145],"propose":[64],"multivariate":[66,90],"temporal":[67,76],"graph":[68,101,111],"network":[69],"coherently":[71],"capture":[72],"interactions,":[74],"learn":[75],"dependencies,":[77],"handle":[79,94],"challenging":[80],"IS-MTS":[83],"data.":[84],"Specifically,":[85],"first":[87],"build":[88],"interaction":[91],"module":[92,129],"frequent":[95],"missing":[96],"values":[97],"extract":[99],"relation":[103],"automatically.":[104],"Second,":[105],"design":[107],"novel":[109],"adjacent":[110],"propagation":[112],"mechanism":[113],"aggregate":[115],"neighbor":[117],"information":[118],"from":[119],"multistep":[120],"snapshots.":[121],"Third,":[122],"construct":[124],"masked":[126],"temporal-aware":[127],"attention":[128],"explicitly":[131],"consider":[132],"timestamp":[134],"context":[135],"interval":[137],"irregularity.":[138],"Based":[139],"on":[140],"an":[141],"extensive":[142],"experimental":[143],"evaluation,":[144],"demonstrate":[146],"superior":[148],"performance":[149],"proposed":[152],"method.":[153]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":2}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}