{"id":"https://openalex.org/W4378499348","doi":"https://doi.org/10.48550/arxiv.2305.15786","title":"Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting","display_name":"Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4378499348","doi":"https://doi.org/10.48550/arxiv.2305.15786"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2305.15786","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":["Cornell University"],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/2305.15786","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001711983","display_name":"Hilaf Hasson","orcid":"https://orcid.org/0000-0001-5266-0199"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hasson, Hilaf","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077131548","display_name":"Danielle C. Maddix","orcid":"https://orcid.org/0000-0002-2317-4068"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Maddix, Danielle C.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100409330","display_name":"Yuyang Wang","orcid":"https://orcid.org/0000-0003-0242-8935"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yuyang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100690498","display_name":"Gaurav Gupta","orcid":"https://orcid.org/0000-0001-7941-0229"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gupta, Gaurav","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5007175636","display_name":"Youngsuk Park","orcid":"https://orcid.org/0000-0002-0970-9214"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Park, Youngsuk","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"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":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":69},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11918","display_name":"Time Series Forecasting Methods","score":0.9863,"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":"Time Series Forecasting Methods","score":0.9863,"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/T12535","display_name":"Learning with Noisy Labels in Machine Learning","score":0.96,"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":"Predicting Stock Market Trends and Movements","score":0.9558,"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/robust-learning","display_name":"Robust Learning","score":0.545979},{"id":"https://openalex.org/keywords/forecasting","display_name":"Forecasting","score":0.538673},{"id":"https://openalex.org/keywords/forecasting-models","display_name":"Forecasting Models","score":0.526071},{"id":"https://openalex.org/keywords/support-vector-machines","display_name":"Support Vector Machines","score":0.522827},{"id":"https://openalex.org/keywords/time-series-forecasting","display_name":"Time Series Forecasting","score":0.51771},{"id":"https://openalex.org/keywords/base","display_name":"Base (topology)","score":0.51638633},{"id":"https://openalex.org/keywords/quantile","display_name":"Quantile","score":0.45392793},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.44326314}],"concepts":[{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.8471888},{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.72214943},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7098503},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.59964955},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5847534},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.576843},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5270498},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.51638633},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5111223},{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.45392793},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.44326314},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.42352417},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19923148},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.18213782},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2305.15786","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":["Cornell University"],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":false,"landing_page_url":"https://api.datacite.org/dois/10.48550/arxiv.2305.15786","pdf_url":null,"source":{"id":"https://openalex.org/S4393179698","display_name":"DataCite API","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210145204","host_organization_name":"DataCite","host_organization_lineage":["https://openalex.org/I4210145204"],"host_organization_lineage_names":["DataCite"],"type":"metadata"},"license":null,"license_id":null,"version":null}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2305.15786","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":["Cornell University"],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[{"score":0.49,"display_name":"Quality education","id":"https://metadata.un.org/sdg/4"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4390690393","https://openalex.org/W4293365552","https://openalex.org/W4288586621","https://openalex.org/W3132003399","https://openalex.org/W3124946120","https://openalex.org/W2585269888","https://openalex.org/W2047938026","https://openalex.org/W2044551864","https://openalex.org/W2005776175","https://openalex.org/W1572557500"],"abstract_inverted_index":{"Ensembling":[0],"is":[1],"among":[2],"the":[3,31,43,46,76,99,108,119,132,146,157,165,169],"most":[4],"popular":[5],"tools":[6],"in":[7,15,57,110,131,156],"machine":[8],"learning":[9],"(ML)":[10],"due":[11],"to":[12,151],"its":[13,59],"effectiveness":[14],"minimizing":[16],"variance":[17],"and":[18,105,160],"thus":[19],"improving":[20],"generalization.":[21],"Most":[22],"ensembling":[23],"methods":[24],"for":[25,143],"black-box":[26],"base":[27,47],"learners":[28,48],"fall":[29],"under":[30],"umbrella":[32],"of":[33,86,128,134,168],"\"stacked":[34],"generalization,\"":[35],"namely":[36],"training":[37],"an":[38],"ML":[39],"algorithm":[40],"that":[41,74],"takes":[42],"inferences":[44],"from":[45,80],"as":[49],"input.":[50],"While":[51],"stacking":[52],"has":[53],"been":[54],"widely":[55],"applied":[56],"practice,":[58],"theoretical":[60,120],"properties":[61],"are":[62,149],"poorly":[63],"understood.":[64],"In":[65],"this":[66],"paper,":[67],"we":[68,122],"prove":[69],"a":[70,81,125,140],"novel":[71],"result,":[72],"showing":[73],"choosing":[75],"best":[77],"stacked":[78,87,129],"generalization":[79],"(finite":[82],"or":[83],"finite-dimensional)":[84],"family":[85,127],"generalizations":[88,130],"based":[89],"on":[90],"cross-validated":[91],"performance":[92,166],"does":[93],"not":[94],"perform":[95],"\"much":[96],"worse\"":[97],"than":[98],"oracle":[100],"best.":[101],"Our":[102],"result":[103],"strengthens":[104],"significantly":[106],"extends":[107],"results":[109,163],"Van":[111],"der":[112],"Laan":[113],"et":[114],"al.":[115],"(2007).":[116],"Inspired":[117],"by":[118],"analysis,":[121],"further":[123],"propose":[124],"particular":[126],"context":[133],"probabilistic":[135],"forecasting,":[136],"each":[137],"one":[138],"with":[139],"different":[141],"sensitivity":[142],"how":[144],"much":[145],"ensemble":[147],"weights":[148],"allowed":[150],"vary":[152],"across":[153],"items,":[154],"timestamps":[155],"forecast":[158],"horizon,":[159],"quantiles.":[161],"Experimental":[162],"demonstrate":[164],"gain":[167],"proposed":[170],"method.":[171]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4378499348","counts_by_year":[],"updated_date":"2024-11-15T21:33:54.964342","created_date":"2023-05-27"}