{"id":"https://openalex.org/W4410536729","doi":"https://doi.org/10.1109/jiot.2025.3571746","title":"Time Series Forecasting via Reinforcement-Learning-Based Model Combination","display_name":"Time Series Forecasting via Reinforcement-Learning-Based Model Combination","publication_year":2025,"publication_date":"2025-05-20","ids":{"openalex":"https://openalex.org/W4410536729","doi":"https://doi.org/10.1109/jiot.2025.3571746"},"language":"en","primary_location":{"id":"doi:10.1109/jiot.2025.3571746","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2025.3571746","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064043533","display_name":"Yuwei Fu","orcid":"https://orcid.org/0009-0006-1753-2179"},"institutions":[{"id":"https://openalex.org/I5023651","display_name":"McGill University","ror":"https://ror.org/01pxwe438","country_code":"CA","type":"education","lineage":["https://openalex.org/I5023651"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Yuwei Fu","raw_affiliation_strings":["Department of Electrical Computer Engineering, McGill University, Montreal, QC, Canada","Department of Electrical Computer Engineering, McGill University, Montreal, Canada"],"raw_orcid":"https://orcid.org/0009-0006-1753-2179","affiliations":[{"raw_affiliation_string":"Department of Electrical Computer Engineering, McGill University, Montreal, QC, Canada","institution_ids":["https://openalex.org/I5023651"]},{"raw_affiliation_string":"Department of Electrical Computer Engineering, McGill University, Montreal, Canada","institution_ids":["https://openalex.org/I5023651"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022267948","display_name":"Di Wu","orcid":"https://orcid.org/0000-0001-7419-9903"},"institutions":[{"id":"https://openalex.org/I5023651","display_name":"McGill University","ror":"https://ror.org/01pxwe438","country_code":"CA","type":"education","lineage":["https://openalex.org/I5023651"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Di Wu","raw_affiliation_strings":["Department of Electrical Computer Engineering, McGill University, Montreal, QC, Canada","Department of Electrical Computer Engineering, McGill University, Montreal, Canada"],"raw_orcid":"https://orcid.org/0000-0001-7419-9903","affiliations":[{"raw_affiliation_string":"Department of Electrical Computer Engineering, McGill University, Montreal, QC, Canada","institution_ids":["https://openalex.org/I5023651"]},{"raw_affiliation_string":"Department of Electrical Computer Engineering, McGill University, Montreal, Canada","institution_ids":["https://openalex.org/I5023651"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081097070","display_name":"Beno\u00eet Boulet","orcid":"https://orcid.org/0000-0002-3191-3967"},"institutions":[{"id":"https://openalex.org/I5023651","display_name":"McGill University","ror":"https://ror.org/01pxwe438","country_code":"CA","type":"education","lineage":["https://openalex.org/I5023651"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Benoit Boulet","raw_affiliation_strings":["Department of Electrical Computer Engineering, McGill University, Montreal, QC, Canada","Department of Electrical Computer Engineering, McGill University, Montreal, Canada"],"raw_orcid":"https://orcid.org/0000-0002-3191-3967","affiliations":[{"raw_affiliation_string":"Department of Electrical Computer Engineering, McGill University, Montreal, QC, Canada","institution_ids":["https://openalex.org/I5023651"]},{"raw_affiliation_string":"Department of Electrical Computer Engineering, McGill University, Montreal, Canada","institution_ids":["https://openalex.org/I5023651"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054179968","display_name":"Arnaud Zinflou","orcid":"https://orcid.org/0009-0003-2716-3889"},"institutions":[{"id":"https://openalex.org/I47099075","display_name":"Hydro-Qu\u00e9bec","ror":"https://ror.org/01nhzsw25","country_code":"CA","type":"government","lineage":["https://openalex.org/I47099075"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Arnaud Zinflou","raw_affiliation_strings":["Data Science Team, Institut de Recherche Hydro Québec, Montreal, QC, Canada","Institut de Recherche Hydro Québec, Montreal, Canada"],"raw_orcid":"https://orcid.org/0009-0003-2716-3889","affiliations":[{"raw_affiliation_string":"Data Science Team, Institut de Recherche Hydro Québec, Montreal, QC, Canada","institution_ids":["https://openalex.org/I47099075"]},{"raw_affiliation_string":"Institut de Recherche Hydro Québec, Montreal, Canada","institution_ids":["https://openalex.org/I47099075"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.10644621,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"12","issue":"16","first_page":"33067","last_page":"33079"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9136000275611877,"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":0.9136000275611877,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9122999906539917,"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/computer-science","display_name":"Computer science","score":0.7822871804237366},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7688009738922119},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6156584620475769},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5822604894638062},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5199238657951355},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41344061493873596}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7822871804237366},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7688009738922119},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6156584620475769},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5822604894638062},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5199238657951355},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41344061493873596},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jiot.2025.3571746","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2025.3571746","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":65,"referenced_works":["https://openalex.org/W1521209343","https://openalex.org/W1986078433","https://openalex.org/W1996063690","https://openalex.org/W2064675550","https://openalex.org/W2079560958","https://openalex.org/W2100483895","https://openalex.org/W2117014758","https://openalex.org/W2141408223","https://openalex.org/W2143323261","https://openalex.org/W2145680191","https://openalex.org/W2165799067","https://openalex.org/W2344249856","https://openalex.org/W2550143307","https://openalex.org/W2604847698","https://openalex.org/W2783209257","https://openalex.org/W2808800115","https://openalex.org/W2892035503","https://openalex.org/W2909973217","https://openalex.org/W2968728157","https://openalex.org/W2971126087","https://openalex.org/W3017158082","https://openalex.org/W3022643593","https://openalex.org/W3043193240","https://openalex.org/W3048036752","https://openalex.org/W3110846353","https://openalex.org/W3123547113","https://openalex.org/W3124296095","https://openalex.org/W3177318507","https://openalex.org/W4205222700","https://openalex.org/W4212883601","https://openalex.org/W4213251304","https://openalex.org/W4230410911","https://openalex.org/W4230428500","https://openalex.org/W4231057675","https://openalex.org/W4283318673","https://openalex.org/W4283807683","https://openalex.org/W4296704829","https://openalex.org/W4312085358","https://openalex.org/W4385245566","https://openalex.org/W4385763767","https://openalex.org/W4395680634","https://openalex.org/W6627932998","https://openalex.org/W6637967152","https://openalex.org/W6681651645","https://openalex.org/W6684205842","https://openalex.org/W6684921986","https://openalex.org/W6734502236","https://openalex.org/W6736456187","https://openalex.org/W6747473740","https://openalex.org/W6748839928","https://openalex.org/W6750254146","https://openalex.org/W6752881527","https://openalex.org/W6754779804","https://openalex.org/W6763309814","https://openalex.org/W6767098714","https://openalex.org/W6769196924","https://openalex.org/W6778883912","https://openalex.org/W6796289742","https://openalex.org/W6796456916","https://openalex.org/W6797155008","https://openalex.org/W6809885388","https://openalex.org/W6810637551","https://openalex.org/W6810853030","https://openalex.org/W6846825190","https://openalex.org/W6857279128"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Time":[0],"series":[1,35,95],"data":[2,41],"permeates":[3],"our":[4,76,116,125],"daily":[5],"existence":[6],"and":[7,23,43,87],"has":[8],"been":[9],"recognized":[10],"as":[11,19,29],"of":[12,49,93,115],"significant":[13],"importance":[14],"for":[15,53,101],"many":[16],"sectors,":[17],"such":[18],"energy,":[20],"transportation,":[21],"telecommunication,":[22],"health":[24],"care.":[25],"Ensemble":[26],"learning":[27],"stands":[28],"a":[30,66,91],"prevalent":[31],"technique":[32],"in":[33],"time":[34,94],"forecasting,":[36],"adept":[37],"at":[38],"handling":[39],"dynamic":[40],"distributions":[42],"enhancing":[44],"robustness.":[45],"Nevertheless,":[46],"the":[47,81,85,113],"determination":[48],"model":[50],"combination":[51],"weights":[52],"computing":[54],"ensemble":[55,86],"outputs":[56],"remains":[57,78],"an":[58,129],"unresolved":[59],"issue.":[60],"In":[61,107],"this":[62,73,108],"endeavour,":[63],"we":[64,110],"introduce":[65],"comprehensive":[67],"reinforcement":[68],"learning-based":[69],"approach":[70],"to":[71,80],"tackle":[72],"challenge.":[74],"Moreover,":[75],"method":[77,117,127],"agnostic":[79],"base":[82],"models":[83],"within":[84],"exhibits":[88],"versatility":[89],"across":[90,103],"spectrum":[92],"forecasting":[96,132],"tasks,":[97],"thereby":[98],"holding":[99],"promise":[100],"application":[102],"diverse":[104],"real-world":[105,119],"scenarios.":[106],"work,":[109],"extensively":[111],"test":[112],"performance":[114],"on":[118],"datasets.":[120],"Experimental":[121],"results":[122],"show":[123],"that":[124],"proposed":[126],"achieves":[128],"average":[130],"6.2%":[131],"accuracy":[133],"improvement":[134],"over":[135],"all":[136],"other":[137],"single":[138],"baseline":[139],"methods.":[140]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}