{"id":"https://openalex.org/W4414360469","doi":"https://doi.org/10.24963/ijcai.2025/320","title":"Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion","display_name":"Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion","publication_year":2025,"publication_date":"2025-09-01","ids":{"openalex":"https://openalex.org/W4414360469","doi":"https://doi.org/10.24963/ijcai.2025/320"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2025/320","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/320","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":"conference-paper","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/A5044728846","display_name":"Wenying He","orcid":"https://orcid.org/0009-0009-2452-7580"},"institutions":[{"id":"https://openalex.org/I184843921","display_name":"Hebei University of Technology","ror":"https://ror.org/018hded08","country_code":"CN","type":"education","lineage":["https://openalex.org/I184843921"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenying He","raw_affiliation_strings":["Hebei University of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hebei University of Technology","institution_ids":["https://openalex.org/I184843921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101004507","display_name":"Jieling Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I184843921","display_name":"Hebei University of Technology","ror":"https://ror.org/018hded08","country_code":"CN","type":"education","lineage":["https://openalex.org/I184843921"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jieling Huang","raw_affiliation_strings":["Hebei University of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hebei University of Technology","institution_ids":["https://openalex.org/I184843921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100370030","display_name":"Junhua Gu","orcid":"https://orcid.org/0000-0001-8896-1735"},"institutions":[{"id":"https://openalex.org/I184843921","display_name":"Hebei University of Technology","ror":"https://ror.org/018hded08","country_code":"CN","type":"education","lineage":["https://openalex.org/I184843921"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junhua Gu","raw_affiliation_strings":["Hebei University of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hebei University of Technology","institution_ids":["https://openalex.org/I184843921"]}]},{"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"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Southern Queensland","institution_ids":["https://openalex.org/I185523456"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053292340","display_name":"Yude Bai","orcid":null},"institutions":[{"id":"https://openalex.org/I198091727","display_name":"Tiangong University","ror":"https://ror.org/00xsr9m91","country_code":"CN","type":"education","lineage":["https://openalex.org/I198091727"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yude Bai","raw_affiliation_strings":["Tiangong university"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tiangong university","institution_ids":["https://openalex.org/I198091727"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":75.1862,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.99904348,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2874","last_page":"2882"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11911","display_name":"Spatial and Panel Data Analysis","score":0.9685999751091003,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11911","display_name":"Spatial and Panel Data Analysis","score":0.9685999751091003,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.5117999911308289},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4498000144958496},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4117000102996826},{"id":"https://openalex.org/keywords/software","display_name":"Software","score":0.36820000410079956},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.36320000886917114},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.3483000099658966},{"id":"https://openalex.org/keywords/noisy-data","display_name":"Noisy data","score":0.3393999934196472},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.3077000081539154}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7357000112533569},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5968000292778015},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.5117999911308289},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4498000144958496},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41200000047683716},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4117000102996826},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4041000008583069},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.36820000410079956},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.36320000886917114},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3483000099658966},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.3393999934196472},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.3077000081539154},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.30720001459121704},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3018999993801117},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29840001463890076},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.28859999775886536},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.2800999879837036},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.273499995470047},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.27230000495910645},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.2702000141143799},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.26460000872612},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2563999891281128},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2025/320","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/320","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":{"Missing":[0],"data":[1,24,67,90,169],"in":[2,34,125,140,180],"spatiotemporal":[3,23,89],"systems":[4],"presents":[5],"a":[6,83],"significant":[7],"challenge":[8],"for":[9,88],"modern":[10],"applications,":[11],"ranging":[12],"from":[13,62],"environmental":[14],"monitoring":[15],"to":[16,28,47,101],"urban":[17],"traffic":[18],"management.":[19],"The":[20,148,171,185],"integrity":[21],"of":[22,98,182],"often":[25],"deteriorates":[26],"due":[27],"hardware":[29],"malfunctions":[30],"and":[31,43,54,72,121,137],"software":[32],"failures":[33],"real-world":[35],"deployments.":[36],"Current":[37],"approaches":[38],"based":[39],"on":[40,94,107],"machine":[41],"learning":[42,45],"deep":[44],"struggle":[46],"model":[48],"the":[49,66,95,141,167],"intricate":[50],"interdependencies":[51],"between":[52],"spatial":[53],"temporal":[55,120],"dimensions":[56],"effectively":[57],"and,":[58],"more":[59,145],"importantly,":[60],"suffer":[61],"cumulative":[63],"errors":[64],"during":[65],"imputation":[68,183],"process,":[69],"which":[70,143],"propagate":[71],"amplify":[73],"through":[74],"iterations.":[75],"To":[76],"address":[77],"these":[78,129],"limitations,":[79],"we":[80],"propose":[81],"CoFILL,":[82],"novel":[84],"Conditional":[85],"Diffusion":[86],"Model":[87],"imputation.":[91,147],"CoFILL":[92,132,176],"builds":[93],"inherent":[96],"advantages":[97],"diffusion":[99],"models":[100],"generate":[102],"high-quality":[103],"imputations":[104],"without":[105],"relying":[106],"potentially":[108],"error-prone":[109],"prior":[110],"estimates.":[111],"It":[112],"incorporates":[113],"an":[114],"innovative":[115],"dual-stream":[116],"architecture":[117],"that":[118,152,164,175],"processes":[119],"frequency":[122],"domain":[123],"features":[124],"parallel.":[126],"By":[127],"fusing":[128],"complementary":[130],"features,":[131],"captures":[133],"both":[134],"rapid":[135],"fluctuations":[136],"underlying":[138],"patterns":[139],"data,":[142],"enables":[144],"robust":[146],"extensive":[149],"experiments":[150],"demonstrate":[151],"CoFILL's":[153],"noise":[154,160],"prediction":[155],"network":[156],"successfully":[157],"transforms":[158],"random":[159],"into":[161],"meaningful":[162],"values":[163],"align":[165],"with":[166],"true":[168],"distribution.":[170],"results":[172],"also":[173],"show":[174],"outperforms":[177],"state-of-the-art":[178],"methods":[179],"terms":[181],"accuracy.":[184],"source":[186],"code":[187],"is":[188],"publicly":[189],"available":[190],"at":[191],"https://github.com/joyHJL/CoFILL.":[192]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":1}],"updated_date":"2026-08-11T07:18:39.950985","created_date":"2025-10-10T00:00:00"}
