{"id":"https://openalex.org/W7166830166","doi":"https://doi.org/10.48550/arxiv.2606.31804","title":"Relational and Sequential Conformal Inference for Energy Time Series over Graphs via Foundation Models","display_name":"Relational and Sequential Conformal Inference for Energy Time Series over Graphs via Foundation Models","publication_year":2026,"publication_date":"2026-06-30","ids":{"openalex":"https://openalex.org/W7166830166","doi":"https://doi.org/10.48550/arxiv.2606.31804"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.31804","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.31804","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2606.31804","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5016547975","display_name":"Keivan Faghih Niresi","orcid":"https://orcid.org/0000-0003-0311-5877"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Niresi, Keivan Faghih","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139721237","display_name":"Alice Cicirello","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cicirello, Alice","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139770090","display_name":"Olga Fink","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fink, Olga","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/T11052","display_name":"Energy Load and Power Forecasting","score":0.4074999988079071,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.4074999988079071,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.28790000081062317,"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/T11185","display_name":"Integrated Energy Systems Optimization","score":0.08340000361204147,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6122000217437744},{"id":"https://openalex.org/keywords/conformal-map","display_name":"Conformal map","score":0.5458999872207642},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.4903999865055084},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.47269999980926514},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.44359999895095825},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4309000074863434},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.3725999891757965},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.3702999949455261}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6122000217437744},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6097999811172485},{"id":"https://openalex.org/C98214594","wikidata":"https://www.wikidata.org/wiki/Q850275","display_name":"Conformal map","level":2,"score":0.5458999872207642},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.4903999865055084},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4772000014781952},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.47269999980926514},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.44359999895095825},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4309000074863434},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3993000090122223},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.384799987077713},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3725999891757965},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.3702999949455261},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.3684000074863434},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36719998717308044},{"id":"https://openalex.org/C41426520","wikidata":"https://www.wikidata.org/wiki/Q1192065","display_name":"Point estimation","level":2,"score":0.3668000102043152},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.36399999260902405},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.3249000012874603},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.32170000672340393},{"id":"https://openalex.org/C206658404","wikidata":"https://www.wikidata.org/wiki/Q12725","display_name":"Electricity","level":2,"score":0.3199000060558319},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2906999886035919},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.2892000079154968},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.2703000009059906},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2635999917984009},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.2632000148296356}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.31804","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.31804","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2606.31804","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.31804","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"energy":[1,15,40,45,92,109,220],"demand":[2],"forecasting":[3,29,131],"is":[4],"essential":[5],"for":[6,79,90,159,217],"the":[7,104,133],"reliable":[8,57,212],"operation":[9],"and":[10,35,66,76,149,178,196,213],"planning":[11,68],"of":[12,108,137],"modern":[13],"sustainable":[14],"systems.":[16,110],"Spatial-temporal":[17],"graph":[18],"neural":[19],"networks":[20],"(STGNNs)":[21],"have":[22],"recently":[23],"achieved":[24],"strong":[25],"performance":[26],"in":[27,43],"point":[28,48,144],"by":[30],"jointly":[31],"modeling":[32],"temporal":[33],"dynamics":[34],"relational":[36,179],"dependencies":[37],"across":[38],"interconnected":[39],"nodes.":[41],"However,":[42,94],"real-world":[44,194],"systems,":[46],"accurate":[47],"forecasts":[49,145],"alone":[50],"are":[51],"insufficient,":[52],"as":[53,191,193],"operators":[54],"also":[55],"require":[56],"uncertainty":[58,80,215],"estimates":[59,216],"to":[60,101],"support":[61],"risk-aware":[62],"decision-making,":[63],"grid":[64],"stability,":[65],"operational":[67],"under":[69],"uncertainty.":[70],"Conformal":[71,120],"prediction":[72,97,169,208],"provides":[73],"a":[74,125,155,163],"principled":[75],"model-agnostic":[77],"framework":[78,127],"quantification":[81],"with":[82,122,132],"statistical":[83],"coverage":[84],"guarantees,":[85],"making":[86],"it":[87],"particularly":[88],"attractive":[89],"safety-critical":[91],"applications.":[93],"existing":[95,206],"conformal":[96,207],"approaches":[98],"often":[99],"fail":[100],"fully":[102],"capture":[103],"complex":[105,218],"spatial-temporal":[106,152],"structure":[107],"To":[111],"address":[112],"these":[113],"limitations,":[114],"we":[115],"propose":[116],"STOIC":[117,141,167,183,203],"(Spatial-Temporal":[118],"Graph":[119],"Prediction":[121],"In-Context":[123],"Learning),":[124],"novel":[126],"that":[128],"integrates":[129],"graph-based":[130],"zero-shot":[134],"calibration":[135],"capabilities":[136],"tabular":[138,156,164],"foundation":[139,165],"models.":[140],"first":[142],"generates":[143],"using":[146],"an":[147],"STGNN":[148],"subsequently":[150],"reformulates":[151],"residuals":[153],"into":[154],"representation":[157],"suitable":[158],"in-context":[160],"learning.":[161],"Leveraging":[162],"model,":[166],"calibrates":[168],"intervals":[170],"without":[171],"task-specific":[172],"retraining,":[173],"effectively":[174],"capturing":[175],"both":[176],"sequential":[177],"dependencies.":[180],"We":[181],"evaluate":[182],"on":[184],"five":[185],"diverse":[186],"benchmarks,":[187],"including":[188],"synthetic":[189],"simulations":[190],"well":[192],"electricity":[195],"district":[197],"heating":[198],"networks.":[199],"Across":[200],"all":[201],"datasets,":[202],"consistently":[204],"outperforms":[205],"baselines,":[209],"delivering":[210],"more":[211],"robust":[214],"graph-structured":[219],"time":[221],"series.":[222]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-02T00:00:00"}