{"id":"https://openalex.org/W4414359572","doi":"https://doi.org/10.24963/ijcai.2025/1187","title":"Deep Learning for Multivariate Time Series Imputation: A Survey","display_name":"Deep Learning for Multivariate Time Series Imputation: A Survey","publication_year":2025,"publication_date":"2025-09-01","ids":{"openalex":"https://openalex.org/W4414359572","doi":"https://doi.org/10.24963/ijcai.2025/1187"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2025/1187","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/1187","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/A5100384785","display_name":"Jun Wang","orcid":"https://orcid.org/0000-0002-7033-5012"},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]},{"id":"https://openalex.org/I4210122282","display_name":"Praktisk-Teologiske Seminar","ror":"https://ror.org/02jv9pf54","country_code":"NO","type":"other","lineage":["https://openalex.org/I4210122282"]}],"countries":["HK","NO"],"is_corresponding":true,"raw_author_name":"Jun Wang","raw_affiliation_strings":["Hong Kong University of Science and Technology","Hong Kong University of Science and Technology (Guangzhou)","PyPOTS Research"],"affiliations":[{"raw_affiliation_string":"Hong Kong University of Science and Technology","institution_ids":["https://openalex.org/I200769079"]},{"raw_affiliation_string":"Hong Kong University of Science and Technology (Guangzhou)","institution_ids":[]},{"raw_affiliation_string":"PyPOTS Research","institution_ids":["https://openalex.org/I4210122282"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101521555","display_name":"Wenjie Du","orcid":"https://orcid.org/0000-0003-3046-7835"},"institutions":[{"id":"https://openalex.org/I4210122282","display_name":"Praktisk-Teologiske Seminar","ror":"https://ror.org/02jv9pf54","country_code":"NO","type":"other","lineage":["https://openalex.org/I4210122282"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Wenjie Du","raw_affiliation_strings":["PyPOTS Research"],"affiliations":[{"raw_affiliation_string":"PyPOTS Research","institution_ids":["https://openalex.org/I4210122282"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019084435","display_name":"Yiyuan Yang","orcid":"https://orcid.org/0000-0002-5320-095X"},"institutions":[{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]},{"id":"https://openalex.org/I4210122282","display_name":"Praktisk-Teologiske Seminar","ror":"https://ror.org/02jv9pf54","country_code":"NO","type":"other","lineage":["https://openalex.org/I4210122282"]}],"countries":["GB","NO"],"is_corresponding":false,"raw_author_name":"Yiyuan Yang","raw_affiliation_strings":["PyPOTS Research","University of Oxford"],"affiliations":[{"raw_affiliation_string":"PyPOTS Research","institution_ids":["https://openalex.org/I4210122282"]},{"raw_affiliation_string":"University of Oxford","institution_ids":["https://openalex.org/I40120149"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059099215","display_name":"Linglong Qian","orcid":"https://orcid.org/0000-0003-3930-045X"},"institutions":[{"id":"https://openalex.org/I183935753","display_name":"King's College London","ror":"https://ror.org/0220mzb33","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I183935753"]},{"id":"https://openalex.org/I4210122282","display_name":"Praktisk-Teologiske Seminar","ror":"https://ror.org/02jv9pf54","country_code":"NO","type":"other","lineage":["https://openalex.org/I4210122282"]}],"countries":["GB","NO"],"is_corresponding":false,"raw_author_name":"Linglong Qian","raw_affiliation_strings":["King's College London","PyPOTS Research"],"affiliations":[{"raw_affiliation_string":"King's College London","institution_ids":["https://openalex.org/I183935753"]},{"raw_affiliation_string":"PyPOTS Research","institution_ids":["https://openalex.org/I4210122282"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100625019","display_name":"Wei Cao","orcid":"https://orcid.org/0000-0001-5640-0917"},"institutions":[{"id":"https://openalex.org/I4210122282","display_name":"Praktisk-Teologiske Seminar","ror":"https://ror.org/02jv9pf54","country_code":"NO","type":"other","lineage":["https://openalex.org/I4210122282"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Wei Cao","raw_affiliation_strings":["PyPOTS Research"],"affiliations":[{"raw_affiliation_string":"PyPOTS Research","institution_ids":["https://openalex.org/I4210122282"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063644818","display_name":"Keli Zhang","orcid":"https://orcid.org/0000-0001-5889-8106"},"institutions":[{"id":"https://openalex.org/I4210159102","display_name":"Huawei Technologies (Sweden)","ror":"https://ror.org/0500fyd17","country_code":"SE","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210159102"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Keli Zhang","raw_affiliation_strings":["Huawei Noah\u2019s Ark Lab"],"affiliations":[{"raw_affiliation_string":"Huawei Noah\u2019s Ark Lab","institution_ids":["https://openalex.org/I4210159102"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100655526","display_name":"Wenjia Wang","orcid":"https://orcid.org/0000-0001-9219-0494"},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Wenjia Wang","raw_affiliation_strings":["Hong Kong University of Science and Technology","Hong Kong University of Science and Technology (Guangzhou)"],"affiliations":[{"raw_affiliation_string":"Hong Kong University of Science and Technology","institution_ids":["https://openalex.org/I200769079"]},{"raw_affiliation_string":"Hong Kong University of Science and Technology (Guangzhou)","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018828723","display_name":"Yuxuan Liang","orcid":"https://orcid.org/0000-0003-2817-7337"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuxuan Liang","raw_affiliation_strings":["Hong Kong University of Science and Technology (Guangzhou)"],"affiliations":[{"raw_affiliation_string":"Hong Kong University of Science and Technology (Guangzhou)","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048346353","display_name":"Qingsong Wen","orcid":"https://orcid.org/0000-0003-4516-2524"},"institutions":[{"id":"https://openalex.org/I1328914791","display_name":"Center for Inquiry","ror":"https://ror.org/054m6hn21","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I1328914791"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qingsong Wen","raw_affiliation_strings":["Squirrel Ai Learning"],"affiliations":[{"raw_affiliation_string":"Squirrel Ai Learning","institution_ids":["https://openalex.org/I1328914791"]}]}],"institutions":[],"countries_distinct_count":5,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5100384785"],"corresponding_institution_ids":["https://openalex.org/I200769079","https://openalex.org/I4210122282"],"apc_list":null,"apc_paid":null,"fwci":11.3029,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.98834164,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"10696","last_page":"10704"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9800000190734863,"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":0.9800000190734863,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9397000074386597,"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/missing-data","display_name":"Missing data","score":0.7785999774932861},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.7484999895095825},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6967999935150146},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.6388999819755554},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6044999957084656},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4659999907016754},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4226999878883362}],"concepts":[{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.7785999774932861},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.7484999895095825},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7128000259399414},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6967999935150146},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.6388999819755554},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6044999957084656},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.595300018787384},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5425000190734863},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5066999793052673},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4659999907016754},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.44130000472068787},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4226999878883362},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.39640000462532043},{"id":"https://openalex.org/C38180746","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate analysis","level":2,"score":0.32429999113082886},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.29919999837875366},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.2946000099182129},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2888000011444092},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2025/1187","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/1187","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Missing":[0],"values":[1],"are":[2],"ubiquitous":[3],"in":[4,131],"multivariate":[5,52],"time":[6,53,135],"series":[7,54,136],"(MTS)":[8],"data,":[9],"posing":[10],"significant":[11],"challenges":[12,106],"for":[13,51,99,114,127],"accurate":[14],"analysis":[15,137],"and":[16,35,74,96,107,129,138,147],"downstream":[17],"applications.":[18],"In":[19,39],"recent":[20],"years,":[21],"deep":[22,48],"learning-based":[23],"methods":[24,66],"have":[25],"successfully":[26],"handled":[27],"missing":[28,139],"data":[29,37,140],"by":[30],"leveraging":[31],"complex":[32],"temporal":[33],"dependencies":[34],"learned":[36],"distributions.":[38],"this":[40],"survey,":[41],"we":[42,79,103],"provide":[43],"a":[44,60,85,124],"comprehensive":[45],"summary":[46],"of":[47,134],"learning":[49],"approaches":[50],"imputation":[55,72,141],"(MTSI)":[56],"tasks.":[57,142],"We":[58],"propose":[59],"novel":[61],"taxonomy":[62],"that":[63],"categorizes":[64],"existing":[65,81],"based":[67],"on":[68,88],"two":[69],"key":[70,105],"perspectives:":[71],"uncertainty":[73],"neural":[75],"network":[76],"architecture.":[77],"Furthermore,":[78],"summarize":[80],"MTSI":[82,100,116,145],"toolkits":[83],"with":[84],"particular":[86],"emphasis":[87],"the":[89,132],"PyPOTS":[90],"Ecosystem,":[91],"which":[92,111],"provides":[93],"an":[94],"integrated":[95],"standardized":[97],"foundation":[98],"research.":[101,117],"Finally,":[102],"discuss":[104],"future":[108],"research":[109],"directions,":[110],"give":[112],"insight":[113],"further":[115],"This":[118],"survey":[119],"aims":[120],"to":[121],"serve":[122],"as":[123],"valuable":[125],"resource":[126],"researchers":[128],"practitioners":[130],"field":[133],"A":[143],"well-maintained":[144],"paper":[146],"tool":[148],"list":[149],"is":[150],"available":[151],"at":[152],"https://github.com/WenjieDu/Awesome_Imputation.":[153]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":4}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}
