{"id":"https://openalex.org/W7134042625","doi":"https://doi.org/10.48550/arxiv.2603.04873","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","display_name":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms","publication_year":2026,"publication_date":"2026-03-05","ids":{"openalex":"https://openalex.org/W7134042625","doi":"https://doi.org/10.48550/arxiv.2603.04873"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2603.04873","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","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":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128252407","display_name":"Longkun Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Longkun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128259267","display_name":"Xiaochun Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Xiaochun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128256638","display_name":"Qiantu Tuo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tuo, Qiantu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128263794","display_name":"Rui Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Rui","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/T11326","display_name":"Stock Market Forecasting Methods","score":0.20829999446868896,"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"}},"topics":[{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.20829999446868896,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.14100000262260437,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.13439999520778656,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.6445000171661377},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5257999897003174},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5101000070571899},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.49480000138282776},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4368000030517578},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.4307999908924103},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.42800000309944153},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.4187999963760376}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7161999940872192},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.6445000171661377},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5257999897003174},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5101000070571899},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.49480000138282776},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4498000144958496},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4368000030517578},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.4307999908924103},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.42800000309944153},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.4187999963760376},{"id":"https://openalex.org/C133162039","wikidata":"https://www.wikidata.org/wiki/Q1061077","display_name":"Code generation","level":3,"score":0.38109999895095825},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.3544999957084656},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.34950000047683716},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.33730000257492065},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3312999904155731},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3285999894142151},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.3158999979496002},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28760001063346863},{"id":"https://openalex.org/C125583679","wikidata":"https://www.wikidata.org/wiki/Q755673","display_name":"Search algorithm","level":2,"score":0.2689000070095062},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.2662999927997589}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2603.04873","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2603.04873","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.04873","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":"pmh:doi:10.48550/arxiv.2603.04873","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","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":{"Accurate":[0],"time":[1],"series":[2],"forecasting":[3,39],"underpins":[4],"decision-making":[5],"in":[6],"many":[7],"domains,":[8],"yetconventional":[9],"ML":[10],"development":[11],"often":[12],"faces":[13],"data":[14],"scarcity,":[15],"distribution":[16],"shift,":[17],"anddiminishing":[18],"returns":[19],"from":[20],"manual":[21],"iteration.":[22],"We":[23],"propose":[24],"Self-Evolving":[25],"Agent":[26],"forTime":[27],"Series":[28],"Algorithms":[29],"(SEATS),":[30],"a":[31,59],"framework":[32],"that":[33],"autonomously":[34],"generates,":[35],"val-idates,":[36],"and":[37,80,89,101,114,127],"optimizes":[38],"algorithm":[40],"code":[41,68],"through":[42],"an":[43],"iterative":[44],"self-evolutionloop.":[45],"Our":[46],"design":[47],"combines":[48],"three":[49],"mechanisms:":[50],"(1)":[51],"Metric-Advantage":[52],"MCTS(MA-MCTS),":[53],"which":[54],"replaces":[55],"fixed":[56],"rewards":[57],"with":[58,70],"statistically":[60],"normalized":[61],"advan-tage":[62],"score":[63],"for":[64,86],"search":[65],"guidance,":[66],"(2)":[67],"review":[69],"running":[71,82],"prompt":[72,83],"refinement,so":[73],"every":[74],"successfully":[75],"executed":[76],"solution":[77],"is":[78],"reviewed":[79],"the":[81],"encodescorrective":[84],"patterns":[85],"later":[87],"iterations,":[88],"(3)":[90],"global":[91,99],"steerable":[92],"reasoning,":[93],"whichcompares":[94],"each":[95],"evaluated":[96],"node":[97],"to":[98],"best-":[100],"worst-performing":[102],"solutions":[103],"forcross-trajectory":[104],"transfer.":[105],"A":[106],"MAP-Elites":[107],"archive":[108],"maintains":[109],"architectural":[110],"diversity.Across":[111],"four":[112],"datasets":[113],"two":[115],"metrics,":[116],"SEATS":[117],"wins":[118],"seven":[119],"of":[120],"eight":[121],"comparisonsagainst":[122],"strong":[123],"baselines":[124],"TimeMixer,":[125],"Timer,":[126],"SEMixer":[128]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-07T00:00:00"}