{"id":"https://openalex.org/W7163676121","doi":"https://doi.org/10.48550/arxiv.2606.06285","title":"TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models","display_name":"TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models","publication_year":2026,"publication_date":"2026-06-04","ids":{"openalex":"https://openalex.org/W7163676121","doi":"https://doi.org/10.48550/arxiv.2606.06285"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.06285","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.06285","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.06285","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137971125","display_name":"Ziwen Kan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kan, Ziwen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138008435","display_name":"Yishuo Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yishuo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077795335","display_name":"Kecheng Li","orcid":"https://orcid.org/0000-0001-8862-9943"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Kecheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135537026","display_name":"Andrew Wen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wen, Andrew","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137980312","display_name":"Xiaomeng Wang","orcid":"https://orcid.org/0000-0002-4516-4389"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xiaomeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137935245","display_name":"Liwei Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Liwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137924268","display_name":"Jihao Duan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Duan, Jihao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137996595","display_name":"Song Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Song","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137958406","display_name":"Hongfang Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Hongfang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137951000","display_name":"Tianlong Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Tianlong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.6060000061988831,"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"}},"topics":[{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.6060000061988831,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.08889999985694885,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.0575999990105629,"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":0.6887000203132629},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6028000116348267},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.49480000138282776},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.4925000071525574},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.48179998993873596},{"id":"https://openalex.org/keywords/trace","display_name":"TRACE (psycholinguistics)","score":0.4706000089645386},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.4341999888420105},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.42289999127388}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7372999787330627},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.6887000203132629},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6028000116348267},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5889000296592712},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5521000027656555},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.49480000138282776},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.4925000071525574},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.48179998993873596},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.4706000089645386},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.4341999888420105},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.42289999127388},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4077000021934509},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.3725999891757965},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.3391999900341034},{"id":"https://openalex.org/C2775953691","wikidata":"https://www.wikidata.org/wiki/Q5013874","display_name":"CRFS","level":3,"score":0.3292999863624573},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.3156999945640564},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.287200003862381},{"id":"https://openalex.org/C79772020","wikidata":"https://www.wikidata.org/wiki/Q5159264","display_name":"Conditional independence","level":2,"score":0.2809999883174896},{"id":"https://openalex.org/C2779714256","wikidata":"https://www.wikidata.org/wiki/Q25305062","display_name":"Multiple Models","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.27300000190734863},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.2596000134944916}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.06285","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.06285","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.06285","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.06285","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"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":{"Time":[0],"series":[1,27,86],"foundation":[2,87],"models":[3],"(TS-FMs)":[4],"aim":[5],"to":[6,15,63,71,99,154],"learn":[7],"generalizable":[8],"temporal":[9,32],"representations":[10],"that":[11],"can":[12],"be":[13,100],"adapted":[14],"a":[16,79,135],"wide":[17],"range":[18,136],"of":[19,137],"downstream":[20,138],"tasks.":[21],"In":[22],"real-world":[23],"multimodal":[24,84,112,131,148],"settings,":[25,143],"time":[26,45,85],"are":[28,41,48],"frequently":[29],"affected":[30],"by":[31],"misalignment":[33],"and":[34,68,92,116,124,127,141,158],"partial":[35],"modality":[36,156],"missingness,":[37],"where":[38],"different":[39],"modalities":[40,98],"observed":[42],"at":[43],"heterogeneous":[44],"scales":[46],"or":[47,58,73],"partially":[49],"absent.":[50],"Existing":[51],"approaches":[52],"typically":[53],"rely":[54],"on":[55,110],"naive":[56],"imputation":[57],"masking":[59],"strategies,":[60],"which":[61],"fail":[62],"account":[64],"for":[65,83,130],"cross-modal":[66,161],"dependencies":[67],"often":[69],"lead":[70],"misaligned":[72],"degraded":[74],"representations.":[75,162],"We":[76,107],"propose":[77],"TRACE,":[78],"conditional":[80],"estimation":[81],"paradigm":[82],"model":[88],"pipelines":[89],"under":[90],"missingness":[91,157],"irregular":[93],"sampling,":[94],"allowing":[95],"incomplete":[96],"target":[97],"systematically":[101],"inferred":[102],"from":[103],"available":[104],"auxiliary":[105],"modalities.":[106],"evaluate":[108],"TRACE":[109,144],"diverse":[111],"benchmarks":[113,129],"spanning":[114],"healthcare":[115],"affective":[117],"computing,":[118],"including":[119],"the":[120,125],"MIMIC-IV":[121],"clinical":[122],"dataset":[123],"CMU-MOSI":[126],"CMU-MOSEI":[128],"sentiment":[132],"analysis.":[133],"Across":[134],"prediction":[139],"tasks":[140],"missing-modality":[142],"consistently":[145],"outperforms":[146],"prior":[147],"fusion":[149],"approaches,":[150],"demonstrating":[151],"improved":[152],"robustness":[153],"severe":[155],"more":[159],"reliable":[160]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-06T00:00:00"}