{"id":"https://openalex.org/W4415707810","doi":"https://doi.org/10.1109/icme59968.2025.11210227","title":"DiffMissing: Denoising Diffusion Model for Multivariate Time Series Forecasting with Variable Missing","display_name":"DiffMissing: Denoising Diffusion Model for Multivariate Time Series Forecasting with Variable Missing","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4415707810","doi":"https://doi.org/10.1109/icme59968.2025.11210227"},"language":null,"primary_location":{"id":"doi:10.1109/icme59968.2025.11210227","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11210227","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","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/A5108993645","display_name":"Bingheng Pang","orcid":null},"institutions":[{"id":"https://openalex.org/I151727225","display_name":"Harbin Engineering University","ror":"https://ror.org/03x80pn82","country_code":"CN","type":"education","lineage":["https://openalex.org/I151727225"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Bingheng Pang","raw_affiliation_strings":["Harbin Engineering University,College of Computer Science and Technology,Harbin,China"],"affiliations":[{"raw_affiliation_string":"Harbin Engineering University,College of Computer Science and Technology,Harbin,China","institution_ids":["https://openalex.org/I151727225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027775423","display_name":"Wei Li","orcid":"https://orcid.org/0000-0003-0998-5435"},"institutions":[{"id":"https://openalex.org/I151727225","display_name":"Harbin Engineering University","ror":"https://ror.org/03x80pn82","country_code":"CN","type":"education","lineage":["https://openalex.org/I151727225"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Li","raw_affiliation_strings":["Harbin Engineering University,College of Computer Science and Technology,Harbin,China"],"affiliations":[{"raw_affiliation_string":"Harbin Engineering University,College of Computer Science and Technology,Harbin,China","institution_ids":["https://openalex.org/I151727225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055514169","display_name":"Zhuoxuan Liang","orcid":"https://orcid.org/0009-0008-7141-5963"},"institutions":[{"id":"https://openalex.org/I151727225","display_name":"Harbin Engineering University","ror":"https://ror.org/03x80pn82","country_code":"CN","type":"education","lineage":["https://openalex.org/I151727225"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhuoxuan Liang","raw_affiliation_strings":["Harbin Engineering University,College of Computer Science and Technology,Harbin,China"],"affiliations":[{"raw_affiliation_string":"Harbin Engineering University,College of Computer Science and Technology,Harbin,China","institution_ids":["https://openalex.org/I151727225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101859995","display_name":"Yidan Chen","orcid":"https://orcid.org/0009-0003-1423-756X"},"institutions":[{"id":"https://openalex.org/I151727225","display_name":"Harbin Engineering University","ror":"https://ror.org/03x80pn82","country_code":"CN","type":"education","lineage":["https://openalex.org/I151727225"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yidan Chen","raw_affiliation_strings":["Harbin Engineering University,College of Computer Science and Technology,Harbin,China"],"affiliations":[{"raw_affiliation_string":"Harbin Engineering University,College of Computer Science and Technology,Harbin,China","institution_ids":["https://openalex.org/I151727225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100460993","display_name":"Zhihong Wang","orcid":"https://orcid.org/0000-0001-7418-5130"},"institutions":[{"id":"https://openalex.org/I151727225","display_name":"Harbin Engineering University","ror":"https://ror.org/03x80pn82","country_code":"CN","type":"education","lineage":["https://openalex.org/I151727225"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhihong Wang","raw_affiliation_strings":["Harbin Engineering University,College of Computer Science and Technology,Harbin,China"],"affiliations":[{"raw_affiliation_string":"Harbin Engineering University,College of Computer Science and Technology,Harbin,China","institution_ids":["https://openalex.org/I151727225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008352007","display_name":"Moustafa Youssef","orcid":"https://orcid.org/0000-0002-2063-4364"},"institutions":[{"id":"https://openalex.org/I80693520","display_name":"American University in Cairo","ror":"https://ror.org/0176yqn58","country_code":"EG","type":"education","lineage":["https://openalex.org/I80693520"]}],"countries":["EG"],"is_corresponding":false,"raw_author_name":"Moustafa Youssef","raw_affiliation_strings":["The American University in Cairo,Egyptian Wireless Research Center of Excellence,Cairo,Egypt"],"affiliations":[{"raw_affiliation_string":"The American University in Cairo,Egyptian Wireless Research Center of Excellence,Cairo,Egypt","institution_ids":["https://openalex.org/I80693520"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5108993645"],"corresponding_institution_ids":["https://openalex.org/I151727225"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.36133927,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.2329999953508377,"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"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.2329999953508377,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.1347000002861023,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.13369999825954437,"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/missing-data","display_name":"Missing data","score":0.9025999903678894},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.6822999715805054},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5557000041007996},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5457000136375427},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.44839999079704285},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4442000091075897},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.42559999227523804},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.34790000319480896},{"id":"https://openalex.org/keywords/conditional-independence","display_name":"Conditional independence","score":0.3440999984741211}],"concepts":[{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.9025999903678894},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.6822999715805054},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5557000041007996},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5457000136375427},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5406000018119812},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.44839999079704285},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4442000091075897},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43560001254081726},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.42559999227523804},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37130001187324524},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35280001163482666},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.34790000319480896},{"id":"https://openalex.org/C79772020","wikidata":"https://www.wikidata.org/wiki/Q5159264","display_name":"Conditional independence","level":2,"score":0.3440999984741211},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.3418000042438507},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.3301999866962433},{"id":"https://openalex.org/C137002209","wikidata":"https://www.wikidata.org/wiki/Q898521","display_name":"Hidden variable theory","level":3,"score":0.3287000060081482},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3183000087738037},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.314300000667572},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3082999885082245},{"id":"https://openalex.org/C93361087","wikidata":"https://www.wikidata.org/wiki/Q4426698","display_name":"Data consistency","level":2,"score":0.28769999742507935},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.2863999903202057},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2858999967575073},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.2685000002384186},{"id":"https://openalex.org/C27574286","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Variables","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.258899986743927},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2542000114917755},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme59968.2025.11210227","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11210227","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","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":14,"referenced_works":["https://openalex.org/W2964010366","https://openalex.org/W3126367810","https://openalex.org/W3197040406","https://openalex.org/W4306317966","https://openalex.org/W4313304560","https://openalex.org/W4381245607","https://openalex.org/W4385567802","https://openalex.org/W4386321962","https://openalex.org/W4387846717","https://openalex.org/W4391402146","https://openalex.org/W4393159606","https://openalex.org/W4394625844","https://openalex.org/W4401857089","https://openalex.org/W4402386891"],"related_works":[],"abstract_inverted_index":{"Missing":[0],"values":[1],"are":[2,13,31],"prevalent":[3],"in":[4,118,159,167],"multivariate":[5,90],"time":[6,91],"series":[7,92],"forecasting,":[8],"especially":[9,166],"when":[10,57],"certain":[11],"variables":[12,121,136],"entirely":[14],"missing,":[15],"posing":[16],"a":[17,81,99,140],"significant":[18],"challenge":[19],"to":[20,33,38,53,69,103],"traditional":[21],"methods.":[22],"Two-stage":[23],"models":[24,46],"that":[25,122,154],"combine":[26],"imputation":[27],"and":[28,36,106,134],"forecasting":[29,45,93],"methods":[30,158],"prone":[32],"introduce":[34],"bias":[35],"lead":[37],"error":[39],"accumulation.":[40],"In":[41],"contrast,":[42],"existing":[43,157],"end-to-end":[44,83],"with":[47,94,124],"missing":[48,58,120,135,170],"data":[49,55],"recovery":[50],"components":[51],"fail":[52],"ensure":[54],"consistency":[56,147],"variables.":[59,149],"Diffusion":[60],"models,":[61],"known":[62],"for":[63,89],"their":[64],"robust":[65],"generative":[66],"capabilities,":[67],"prefer":[68],"generate":[70],"consistent":[71],"results":[72,152],"based":[73],"on":[74,162],"the":[75,111,115,125,129,168,176,186],"available":[76],"observations.":[77],"Consequently,":[78],"we":[79],"propose":[80],"novel":[82],"denoising":[84,116],"diffusion":[85],"model":[86],"named":[87],"DiffMissing":[88,97,155],"variable":[95],"missing.":[96],"employs":[98],"contextual":[100,108],"conditional":[101],"encoder":[102],"extract":[104],"local":[105],"global":[107],"information":[109],"from":[110],"observed":[112,133],"variables,":[113],"assisting":[114],"network":[117],"generating":[119],"align":[123],"true":[126],"distribution.":[127],"Meanwhile,":[128],"dynamic":[130],"interaction":[131],"between":[132],"is":[137,178],"modeled":[138],"through":[139],"carefully":[141],"designed":[142],"adaptive":[143],"sparse":[144],"attention,":[145],"ensuring":[146],"among":[148],"Extensive":[150],"experimental":[151],"show":[153],"outperforms":[156],"prediction":[160],"performance":[161],"multiple":[163],"real-world":[164],"datasets,":[165],"high":[169],"rate":[171],"scenario":[172],"of":[173,183],"90%,":[174],"where":[175],"MAE":[177],"improved":[179],"by":[180],"an":[181],"average":[182],"9.90%":[184],"over":[185],"best":[187],"baseline.":[188]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-30T00:00:00"}
