{"id":"https://openalex.org/W7204775662","doi":"https://doi.org/10.48550/arxiv.2608.28116","title":"Generalized Gibbs Ensemble Weighting for Forecast Combination","display_name":"Generalized Gibbs Ensemble Weighting for Forecast Combination","publication_year":2026,"publication_date":"2026-08-28","ids":{"openalex":"https://openalex.org/W7204775662","doi":"https://doi.org/10.48550/arxiv.2608.28116"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2608.28116","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.28116","pdf_url":"https://arxiv.org/pdf/2608.28116","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,"listed_in":[],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2608.28116","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5147787600","display_name":"Nuthanakaluva Prasen R","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nuthanakaluva, Prasen R.","raw_affiliation_strings":["Independent Researcher Oxford , United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Independent Researcher Oxford , United Kingdom","institution_ids":[]}]},{"author_position":"last","author":{"id":null,"display_name":"Gaddam, Nava K.","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I193662353","display_name":"Utrecht University","ror":"https://ror.org/04pp8hn57","country_code":"NL","type":"education","lineage":["https://openalex.org/I193662353"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Gaddam, Nava K.","raw_affiliation_strings":["Department of Physics Utrecht University Utrecht , Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Physics Utrecht University Utrecht , Netherlands","institution_ids":["https://openalex.org/I193662353"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9980000257492065,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9980000257492065,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.00039999998989515007,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social 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":0.0003000000142492354,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/forecast-combination","display_name":"forecast combination","score":0.9990000128746033},{"id":"https://openalex.org/keywords/ensemble-weighting","display_name":"ensemble weighting","score":0.8140000104904175},{"id":"https://openalex.org/keywords/exponential-weighting","display_name":"exponential weighting","score":0.9350000023841858},{"id":"https://openalex.org/keywords/forecast-disagreement","display_name":"forecast disagreement","score":0.6499999761581421},{"id":"https://openalex.org/keywords/time-series-forecasting","display_name":"time series forecasting","score":0.6489999890327454},{"id":"https://openalex.org/keywords/m4-competition","display_name":"M4 competition","score":0.5419999957084656},{"id":"https://openalex.org/keywords/traffic-forecasting","display_name":"traffic prediction","score":0.5370000004768372},{"id":"https://openalex.org/keywords/electricity-forecasting","display_name":"electricity forecasting","score":0.875},{"id":"https://openalex.org/keywords/solar-power-forecasting","display_name":"photovoltaic power forecasting","score":0.9779999852180481}],"concepts":[{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.7347999811172485},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5590000152587891},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.531499981880188},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.4984000027179718},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.4927999973297119},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.4650000035762787},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4083999991416931},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.4077000021934509},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.398499995470047},{"id":"https://openalex.org/C63002673","wikidata":"https://www.wikidata.org/wiki/Q2260590","display_name":"Scoring rule","level":2,"score":0.39169999957084656},{"id":"https://openalex.org/C170061395","wikidata":"https://www.wikidata.org/wiki/Q5468164","display_name":"Forecast skill","level":2,"score":0.3797000050544739},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3765000104904175},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3718999922275543},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.3580999970436096},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.35760000348091125},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.34470000863075256},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3416999876499176},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3346000015735626},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.32910001277923584},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.3176000118255615},{"id":"https://openalex.org/C151376022","wikidata":"https://www.wikidata.org/wiki/Q168698","display_name":"Exponential function","level":2,"score":0.3156000077724457},{"id":"https://openalex.org/C133710760","wikidata":"https://www.wikidata.org/wiki/Q775837","display_name":"Exponential smoothing","level":2,"score":0.305400013923645},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.30300000309944153},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.3010999858379364},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29989999532699585},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.28839999437332153},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.27570000290870667},{"id":"https://openalex.org/C103402496","wikidata":"https://www.wikidata.org/wiki/Q1106171","display_name":"Prediction interval","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.27160000801086426},{"id":"https://openalex.org/C70136482","wikidata":"https://www.wikidata.org/wiki/Q13583781","display_name":"A-weighting","level":3,"score":0.27149999141693115},{"id":"https://openalex.org/C35405484","wikidata":"https://www.wikidata.org/wiki/Q4967066","display_name":"Brier score","level":2,"score":0.2711000144481659},{"id":"https://openalex.org/C166851805","wikidata":"https://www.wikidata.org/wiki/Q5468165","display_name":"Forecast verification","level":3,"score":0.26649999618530273},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.2533000111579895}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2608.28116","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.28116","pdf_url":"https://arxiv.org/pdf/2608.28116","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,"listed_in":[],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2608.28116","doi":"https://doi.org/10.48550/arxiv.2608.28116","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2608.28116","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,"listed_in":[],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2608.28116","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.28116","pdf_url":"https://arxiv.org/pdf/2608.28116","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,"listed_in":[],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204775662.pdf","grobid_xml":"https://content.openalex.org/works/W7204775662.grobid-xml"},"referenced_works_count":2,"referenced_works":["https://openalex.org/W2604847698","https://openalex.org/W3110351998"],"related_works":["https://openalex.org/W7080134442","https://openalex.org/W2148655113","https://openalex.org/W7047988668","https://openalex.org/W2108942494","https://openalex.org/W2952787578","https://openalex.org/W6903984459","https://openalex.org/W3153972656","https://openalex.org/W4214597517","https://openalex.org/W4413219950","https://openalex.org/W2125438877"],"abstract_inverted_index":{"Forecast":[0],"combination":[1,243],"is":[2,196,223,244],"a":[3,61,75,102,139,197,225,230],"reliable":[4],"way":[5],"to":[6,146],"improve":[7],"predictive":[8,81],"performance":[9,38,209],"when":[10,239],"several":[11,203],"forecasting":[12,66],"models":[13,67],"are":[14,31],"available.":[15],"Simple":[16],"aggregation":[17],"rules":[18],"such":[19],"as":[20,68],"the":[21,126,130,148,158],"mean,":[22,25],"median,":[23],"trimmed":[24],"inverse-loss":[26],"weighting,":[27,110],"and":[28,47,70,96,113,122,153,175,186,199,217,232],"exponential":[29,77,131],"weighting":[30,88,132,195],"often":[32],"strong":[33],"baselines,":[34],"but":[35,229],"their":[36],"relative":[37,208],"can":[39],"vary":[40],"across":[41,202,211],"datasets,":[42,212],"forecast":[43,173,213,218,242],"horizons,":[44,214],"deployment":[45,178,215],"settings,":[46,205],"levels":[48],"of":[49,79,104,238],"disagreement":[50,219],"among":[51],"base":[52],"forecasters.":[53],"We":[54,166],"develop":[55],"Generalized":[56],"Gibbs":[57,109,154],"Ensemble":[58],"Weighting":[59],"(GGEW),":[60],"probabilistic":[62],"framework":[63,84,231],"that":[64,192],"treats":[65],"experts":[69],"assigns":[71],"ensemble":[72],"weights":[73],"using":[74,180],"Gibbs-style":[76,193,241],"transformation":[78],"normalized":[80],"loss.":[82],"The":[83,221],"extends":[85],"this":[86],"basic":[87],"rule":[89],"through":[90],"numerical":[91],"stabilization,":[92],"diversity-aware":[93],"score":[94,127],"corrections,":[95],"online":[97,144],"hyperparameter":[98,160],"adaptation.":[99],"GGEW":[100,168],"produces":[101],"family":[103],"related":[105],"methods,":[106],"including":[107],"Stable":[108],"Directional":[111],"Gibbs-NCL,":[112],"Symmetric":[114],"Gibbs-NCL.":[115],"These":[116],"variants":[117],"share":[118],"one":[119],"core":[120],"algorithm":[121],"differ":[123],"only":[124],"in":[125],"used":[128],"inside":[129],"rule.":[133],"For":[134],"sequential":[135],"deployment,":[136],"we":[137],"adopt":[138],"UCB-style":[140],"bandit":[141],"mechanism,":[142],"called":[143],"Local-UCB,":[145],"adapt":[147],"learning":[149],"rate,":[150],"diversity":[151],"strength,":[152],"variant":[155],"without":[156],"evaluating":[157],"full":[159],"grid":[161],"at":[162],"every":[163],"prediction":[164],"step.":[165],"evaluate":[167],"on":[169],"official":[170],"M4":[171],"competition":[172],"submissions":[174],"external":[176],"rolling-origin":[177],"experiments":[179],"Monash":[181],"Traffic":[182],"Hourly,":[183,185],"Electricity":[184],"Solar":[187],"Weekly":[188],"datasets.":[189],"Results":[190],"suggest":[191],"adaptive":[194,240],"useful":[198],"competitive":[200],"tool":[201],"benchmark":[204],"although":[206],"its":[207],"varies":[210],"protocols,":[216],"groups.":[220],"contribution":[222],"not":[224],"universal":[226],"dominance":[227],"claim,":[228],"empirical":[233],"study":[234],"motivating":[235],"further":[236],"investigation":[237],"useful.":[245]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-09-01T00:00:00"}