{"id":"https://openalex.org/W7160525125","doi":"https://doi.org/10.48550/arxiv.2605.04957","title":"Delving into Non-Exchangeability for Conformal Prediction in Graph-Structured Multivariate Time Series","display_name":"Delving into Non-Exchangeability for Conformal Prediction in Graph-Structured Multivariate Time Series","publication_year":2026,"publication_date":"2026-05-06","ids":{"openalex":"https://openalex.org/W7160525125","doi":"https://doi.org/10.48550/arxiv.2605.04957"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.04957","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04957","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":null,"license_id":null,"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.2605.04957","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135539078","display_name":"Ruichao Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Ruichao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016378466","display_name":"Xingyao Han","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Xingyao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135597272","display_name":"Luo Wenshui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenshui, Luo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135550291","display_name":"Zhe Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Zhe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135629251","display_name":"Chen Gong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gong, Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135589387","display_name":"Hesheng Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Hesheng","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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.2567000091075897,"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":0.2567000091075897,"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":0.21969999372959137,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.156700000166893,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.5753999948501587},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5386000275611877},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5333999991416931},{"id":"https://openalex.org/keywords/conformal-map","display_name":"Conformal map","score":0.5121999979019165},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4627000093460083},{"id":"https://openalex.org/keywords/joint-probability-distribution","display_name":"Joint probability distribution","score":0.43560001254081726},{"id":"https://openalex.org/keywords/coupling","display_name":"Coupling (piping)","score":0.4307999908924103}],"concepts":[{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.5753999948501587},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5501999855041504},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5386000275611877},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5333999991416931},{"id":"https://openalex.org/C98214594","wikidata":"https://www.wikidata.org/wiki/Q850275","display_name":"Conformal map","level":2,"score":0.5121999979019165},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4677000045776367},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4627000093460083},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.43560001254081726},{"id":"https://openalex.org/C131584629","wikidata":"https://www.wikidata.org/wiki/Q4308705","display_name":"Coupling (piping)","level":2,"score":0.4307999908924103},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4043999910354614},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.40209999680519104},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3815000057220459},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.3718000054359436},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3287999927997589},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.31679999828338623},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30809998512268066},{"id":"https://openalex.org/C31462909","wikidata":"https://www.wikidata.org/wiki/Q1045782","display_name":"Scatter plot","level":2,"score":0.29660001397132874},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29350000619888306},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.2757999897003174}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.04957","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04957","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.04957","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04957","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":null,"license_id":null,"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":{"Point":[0],"forecasting":[1],"for":[2,15],"graph-structured":[3,49],"multivariate":[4],"time":[5,50],"series":[6],"is":[7,18],"a":[8,28,95,153],"fundamental":[9],"problem,":[10],"but":[11,170],"rigorous":[12],"uncertainty":[13,25],"quantification":[14],"such":[16,72],"predictions":[17],"still":[19],"underexplored.":[20],"Conformal":[21,131],"prediction":[22,132],"(CP)":[23],"offers":[24],"estimation":[26],"with":[27],"solid":[29],"coverage":[30,169],"guarantee":[31],"under":[32,45],"the":[33,38,57,68,83,121,174,178],"exchangeability":[34,58],"assumption,":[35],"which":[36,104],"requires":[37],"joint":[39],"data":[40],"distribution":[41],"to":[42,112,141],"be":[43,80],"unchanged":[44],"permutation.":[46],"However,":[47],"in":[48,75,120],"series,":[51],"inherent":[52],"cross-node":[53],"coupling":[54,73],"can":[55,79],"violate":[56],"condition,":[59],"making":[60],"direct":[61],"application":[62],"of":[63],"CP":[64,119,180],"unreliable.":[65],"Inspired":[66],"by":[67,82],"spectral":[69,122],"graph":[70,139],"theory,":[71],"resides":[74],"global":[76,114],"trends":[77,115],"and":[78,116,145],"characterized":[81],"low-frequency":[84,110,154],"components,":[85],"while":[86],"high-frequency":[87,107,147],"components":[88,108,144],"are":[89],"nearly":[90],"exchangeable.":[91],"Therefore,":[92],"we":[93,127],"propose":[94,129],"novel":[96],"concept":[97],"named":[98],"Spectral":[99,130],"Graph":[100],"Conditional":[101],"Exchangeability":[102],"(SGCE),":[103],"conditions":[105],"exchangeable":[106],"on":[109,125,158],"ones":[111],"preserve":[113],"enable":[117],"effective":[118],"domain.":[123],"Based":[124],"SGCE,":[126],"further":[128],"via":[133,149],"wAveLEt":[134],"transform":[135],"(SCALE).":[136],"SCALE":[137,164],"uses":[138],"wavelets":[140],"decompose":[142],"low/high-frequency":[143],"conformalizes":[146],"residuals":[148],"adaptive":[150],"gating":[151],"over":[152,177],"embedding.":[155],"Experimental":[156],"results":[157],"real-world":[159],"traffic":[160],"datasets":[161],"show":[162],"that":[163],"not":[165],"only":[166],"achieves":[167],"valid":[168],"also":[171],"consistently":[172],"improves":[173],"coverage-efficiency":[175],"trade-off":[176],"state-of-the-art":[179],"methods.":[181]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-08T00:00:00"}
