{"id":"https://openalex.org/W7138416749","doi":"https://doi.org/10.48550/arxiv.2603.14709","title":"Cross-RAG: Zero-Shot Retrieval-Augmented Time Series Forecasting via Cross-Attention","display_name":"Cross-RAG: Zero-Shot Retrieval-Augmented Time Series Forecasting via Cross-Attention","publication_year":2026,"publication_date":"2026-03-16","ids":{"openalex":"https://openalex.org/W7138416749","doi":"https://doi.org/10.48550/arxiv.2603.14709"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.14709","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14709","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.14709","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5085695757","display_name":"Seunghan Lee","orcid":"https://orcid.org/0000-0001-5191-6332"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Lee, Seunghan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129645915","display_name":"Jaehoon Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Jaehoon","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129738881","display_name":"Jun Seo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Seo, Jun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084039433","display_name":"Sungdong Yoo","orcid":"https://orcid.org/0009-0003-1663-6345"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yoo, Sungdong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129746252","display_name":"Minjae Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Minjae","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128068045","display_name":"Tae Yoon Lim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lim, Tae Yoon","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129680381","display_name":"Dongwan Kang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kang, Dongwan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129738000","display_name":"Hwanil Choi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choi, Hwanil","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107114088","display_name":"Soonyoung Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, SoonYoung","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5082843254","display_name":"Wonbin Ahn","orcid":"https://orcid.org/0000-0002-6012-2750"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ahn, Wonbin","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":10,"corresponding_author_ids":["https://openalex.org/A5085695757"],"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.7328000068664551,"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"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.7328000068664551,"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"}},{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.10029999911785126,"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"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.03500000014901161,"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/generalization","display_name":"Generalization","score":0.6498000025749207},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5758000016212463},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.5683000087738037},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5145999789237976},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5038999915122986}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7555000185966492},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6498000025749207},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5758000016212463},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.5683000087738037},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5145999789237976},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5038999915122986},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4431999921798706},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4429999887943268},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42910000681877136},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.37299999594688416},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.29510000348091125},{"id":"https://openalex.org/C99016210","wikidata":"https://www.wikidata.org/wiki/Q5488129","display_name":"Query expansion","level":2,"score":0.2718000113964081}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.14709","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14709","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.14709","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14709","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":"article"},"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":{"Recent":[0],"advances":[1],"in":[2],"time":[3,17,21],"series":[4,18,22],"foundation":[5],"models":[6,77],"(TSFMs)":[7],"demonstrate":[8,101],"strong":[9],"expressive":[10],"capacity":[11],"through":[12],"large-scale":[13],"pretraining":[14],"across":[15,109,121],"diverse":[16,122],"domains.":[19],"Zero-shot":[20],"forecasting":[23,35,67,107],"with":[24],"TSFMs,":[25],"however,":[26],"exhibits":[27],"limited":[28],"generalization":[29],"to":[30,72],"unseen":[31],"datasets,":[32],"which":[33],"retrieval-augmented":[34,66],"addresses":[36],"by":[37],"leveraging":[38],"an":[39],"external":[40],"knowledge":[41],"base.":[42],"Existing":[43],"approaches":[44],"rely":[45],"on":[46],"a":[47,64],"fixed":[48],"number":[49],"of":[50],"retrieved":[51,74,84,97],"samples":[52,85],"that":[53,69,102],"may":[54],"introduce":[55],"irrelevant":[56],"information.":[57],"To":[58],"this":[59],"end,":[60],"we":[61],"propose":[62],"Cross-RAG,":[63],"zero-shot":[65,106],"framework":[68],"selectively":[70],"attends":[71],"query-relevant":[73],"samples.":[75,98],"Cross-RAG":[76,103],"input-level":[78],"relevance":[79],"between":[80],"the":[81,94],"query":[82,95],"and":[83,96,112,115],"via":[86],"query-retrieval":[87],"cross-attention,":[88],"while":[89],"jointly":[90],"incorporating":[91],"information":[92],"from":[93],"Extensive":[99],"experiments":[100],"consistently":[104],"improves":[105],"performance":[108],"various":[110],"TSFMs":[111],"RAG":[113],"methods,":[114],"additional":[116],"analyses":[117],"confirm":[118],"its":[119],"effectiveness":[120],"retrieval":[123],"scenarios.":[124],"Code":[125],"is":[126],"available":[127],"at":[128],"https://github.com/seunghan96/cross-rag/.":[129]},"counts_by_year":[],"updated_date":"2026-03-18T06:31:55.123368","created_date":"2026-03-18T00:00:00"}
