{"id":"https://openalex.org/W7164330691","doi":"https://doi.org/10.48550/arxiv.2606.11625","title":"TimeRouter: Efficient and Adaptive Routing of Time-Series Foundation Models","display_name":"TimeRouter: Efficient and Adaptive Routing of Time-Series Foundation Models","publication_year":2026,"publication_date":"2026-06-10","ids":{"openalex":"https://openalex.org/W7164330691","doi":"https://doi.org/10.48550/arxiv.2606.11625"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.11625","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.11625","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.2606.11625","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5119174941","display_name":"Kanghui Ning","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ning, Kanghui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138451116","display_name":"Yushan Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Yushan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075846365","display_name":"Kashif Rasul","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rasul, Kashif","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138446029","display_name":"Anderson Schneider","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schneider, Anderson","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081397604","display_name":"Yuriy Nevmyvaka","orcid":"https://orcid.org/0009-0001-3484-7483"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nevmyvaka, Yuriy","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138419628","display_name":"Dongjin Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Dongjin","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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.5580999851226807,"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":0.5580999851226807,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.1428000032901764,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.06949999928474426,"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/modular-design","display_name":"Modular design","score":0.5580000281333923},{"id":"https://openalex.org/keywords/routing","display_name":"Routing (electronic design automation)","score":0.5019999742507935},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4853000044822693},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.4683000147342682},{"id":"https://openalex.org/keywords/complementarity","display_name":"Complementarity (molecular biology)","score":0.4474000036716461},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.44369998574256897},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.3846000134944916},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.35179999470710754},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.34709998965263367}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6862999796867371},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.5580000281333923},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.5019999742507935},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4853000044822693},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46959999203681946},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.4683000147342682},{"id":"https://openalex.org/C202269582","wikidata":"https://www.wikidata.org/wiki/Q2644277","display_name":"Complementarity (molecular biology)","level":2,"score":0.4474000036716461},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.44369998574256897},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4049000144004822},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.3846000134944916},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.35179999470710754},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.34709998965263367},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.33739998936653137},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.3294999897480011},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.3255000114440918},{"id":"https://openalex.org/C143273055","wikidata":"https://www.wikidata.org/wiki/Q2382794","display_name":"Delegate","level":2,"score":0.30070000886917114},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.299699991941452},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.2872999906539917},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C204948658","wikidata":"https://www.wikidata.org/wiki/Q1119410","display_name":"Static routing","level":4,"score":0.28029999136924744},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.2711000144481659},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.26600000262260437},{"id":"https://openalex.org/C141141315","wikidata":"https://www.wikidata.org/wiki/Q2379942","display_name":"Guard (computer science)","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.25600001215934753},{"id":"https://openalex.org/C184896649","wikidata":"https://www.wikidata.org/wiki/Q290066","display_name":"Routing table","level":4,"score":0.25600001215934753},{"id":"https://openalex.org/C104954878","wikidata":"https://www.wikidata.org/wiki/Q1648707","display_name":"Routing protocol","level":3,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.11625","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.11625","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.2606.11625","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.11625","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":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6156760454177856}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Time-series":[0],"foundation":[1],"models":[2],"(TSFMs)":[3],"are":[4],"increasingly":[5],"explored":[6],"as":[7,142],"predictive":[8],"experts":[9],"within":[10],"emerging":[11],"agentic":[12,151],"time-series":[13,152],"systems.":[14],"However,":[15],"TSFMs":[16,65],"exhibit":[17],"heterogeneous":[18],"inductive":[19],"biases,":[20],"and":[21,72,85,133,145],"no":[22],"single":[23],"model":[24],"consistently":[25],"dominates":[26],"across":[27,60],"forecasting":[28],"regimes,":[29],"making":[30],"expert":[31,91],"selection":[32,92],"a":[33,61,78,82,143],"critical":[34],"challenge.":[35],"Existing":[36],"systems":[37,153],"often":[38],"delegate":[39],"this":[40],"decision":[41],"to":[42],"LLM-based":[43],"controllers,":[44],"incurring":[45],"substantial":[46],"inference":[47,98],"overhead.":[48],"We":[49],"present":[50],"TimeRouter,":[51],"an":[52,86,95,109],"efficient":[53],"routing":[54,80,125,147],"framework":[55],"that":[56],"leverages":[57],"empirical":[58,121],"complementarity":[59],"pool":[62,131],"of":[63,112,130],"pretrained":[64],"through":[66],"lightweight":[67,146],"discriminative":[68],"routing,":[69],"selective":[70,83,134],"gating,":[71],"ensemble":[73,87],"fallback.":[74],"Concretely,":[75],"TimeRouter":[76,100,141],"combines":[77],"learned":[79],"head,":[81],"gate,":[84],"fallback,":[88],"enabling":[89],"adaptive":[90],"without":[93],"invoking":[94],"LLM":[96],"at":[97,162],"time.":[99],"achieves":[101],"state-of-the-art":[102],"performance":[103],"on":[104],"the":[105,128],"GIFT-EVAL":[106],"leaderboard,":[107],"with":[108],"LB":[110],"MASE":[111],"0.6765.":[113],"Beyond":[114],"benchmark":[115],"performance,":[116],"our":[117],"ablation":[118],"studies":[119],"provide":[120],"insights":[122],"into":[123],"TSFM":[124],"design,":[126],"highlighting":[127],"importance":[129],"composition":[132],"gating.":[135],"Taken":[136],"together,":[137],"these":[138],"results":[139],"position":[140],"modular":[144],"layer":[148],"for":[149],"future":[150],"built":[154],"upon":[155],"foundation-model":[156],"pools.":[157],"Our":[158],"code":[159],"is":[160],"available":[161],"https://github.com/UConn-DSIS/TimeRouter.":[163]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-12T00:00:00"}
