{"id":"https://openalex.org/W2165444309","doi":"https://doi.org/10.1109/ijcnn.2013.6706740","title":"Crogging (cross-validation aggregation) for forecasting — A novel algorithm of neural network ensembles on time series subsamples","display_name":"Crogging (cross-validation aggregation) for forecasting — A novel algorithm of neural network ensembles on time series subsamples","publication_year":2013,"publication_date":"2013-08-01","ids":{"openalex":"https://openalex.org/W2165444309","doi":"https://doi.org/10.1109/ijcnn.2013.6706740","mag":"2165444309"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2013.6706740","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2013.6706740","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 2013 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5090748216","display_name":"Devon K. Barrow","orcid":"https://orcid.org/0000-0001-8919-1701"},"institutions":[{"id":"https://openalex.org/I67415387","display_name":"Lancaster University","ror":"https://ror.org/04f2nsd36","country_code":"GB","type":"education","lineage":["https://openalex.org/I67415387"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Devon K. Barrow","raw_affiliation_strings":["Lancaster Centre for Forecasting, Lancaster University, Lancaster, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lancaster Centre for Forecasting, Lancaster University, Lancaster, UK","institution_ids":["https://openalex.org/I67415387"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044304248","display_name":"Sven F. Crone","orcid":"https://orcid.org/0000-0003-4952-318X"},"institutions":[{"id":"https://openalex.org/I67415387","display_name":"Lancaster University","ror":"https://ror.org/04f2nsd36","country_code":"GB","type":"education","lineage":["https://openalex.org/I67415387"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Sven F. Crone","raw_affiliation_strings":["Lancaster Centre for Forecasting, Lancaster University, Lancaster, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lancaster Centre for Forecasting, Lancaster University, Lancaster, UK","institution_ids":["https://openalex.org/I67415387"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I67415387"],"apc_list":null,"apc_paid":null,"fwci":1.9076,"has_fulltext":false,"cited_by_count":39,"citation_normalized_percentile":{"value":0.86175824,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9986000061035156,"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":0.9986000061035156,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9984999895095825,"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/T10320","display_name":"Neural Networks and Applications","score":0.9894999861717224,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/bootstrapping","display_name":"Bootstrapping (finance)","score":0.8204233646392822},{"id":"https://openalex.org/keywords/cross-validation","display_name":"Cross-validation","score":0.7321256399154663},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6906573176383972},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6369149684906006},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5190520882606506},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5109133124351501},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.5097024440765381},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5052469372749329},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.48805248737335205},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4822255074977875},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4585539698600769},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.4448591470718384},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4397050738334656},{"id":"https://openalex.org/keywords/bootstrap-aggregating","display_name":"Bootstrap aggregating","score":0.4381653070449829},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2729959785938263},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.18644937872886658},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14502108097076416}],"concepts":[{"id":"https://openalex.org/C207609745","wikidata":"https://www.wikidata.org/wiki/Q4944086","display_name":"Bootstrapping (finance)","level":2,"score":0.8204233646392822},{"id":"https://openalex.org/C27181475","wikidata":"https://www.wikidata.org/wiki/Q541014","display_name":"Cross-validation","level":2,"score":0.7321256399154663},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6906573176383972},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6369149684906006},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5190520882606506},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5109133124351501},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.5097024440765381},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5052469372749329},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.48805248737335205},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4822255074977875},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4585539698600769},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4448591470718384},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4397050738334656},{"id":"https://openalex.org/C162040801","wikidata":"https://www.wikidata.org/wiki/Q799897","display_name":"Bootstrap aggregating","level":2,"score":0.4381653070449829},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2729959785938263},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.18644937872886658},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14502108097076416},{"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":2,"locations":[{"id":"doi:10.1109/ijcnn.2013.6706740","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2013.6706740","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 2013 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:openaire_cris_publications/be0b63aa-dc37-4f59-8218-d873e8fcc8de","is_oa":false,"landing_page_url":"https://research.birmingham.ac.uk/en/publications/be0b63aa-dc37-4f59-8218-d873e8fcc8de","pdf_url":null,"source":{"id":"https://openalex.org/S4306402634","display_name":"University of Birmingham Research Portal (University of Birmingham)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79619799","host_organization_name":"University of Birmingham","host_organization_lineage":["https://openalex.org/I79619799"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Barrow, D K & Crone, S F 2013, Crogging (cross-validation aggregation) for forecasting - A novel algorithm of neural network ensembles on time series subsamples. in 2013 International Joint Conference on Neural Networks, IJCNN 2013., 6706740, 2013 International Joint Conference on Neural Networks, IJCNN 2013, Dallas, TX, United States, 4/08/13. https://doi.org/10.1109/IJCNN.2013.6706740","raw_type":"contributionToPeriodical"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8100000023841858,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W27682692","https://openalex.org/W1586335931","https://openalex.org/W1975659328","https://openalex.org/W1981552604","https://openalex.org/W1986528915","https://openalex.org/W1988115241","https://openalex.org/W2001507422","https://openalex.org/W2016210396","https://openalex.org/W2029897006","https://openalex.org/W2038705170","https://openalex.org/W2040615655","https://openalex.org/W2042506099","https://openalex.org/W2046356540","https://openalex.org/W2047694274","https://openalex.org/W2048665112","https://openalex.org/W2073738917","https://openalex.org/W2117897510","https://openalex.org/W2127314075","https://openalex.org/W2128071598","https://openalex.org/W2135293965","https://openalex.org/W2138151720","https://openalex.org/W2153787847","https://openalex.org/W2154326182","https://openalex.org/W2179386986","https://openalex.org/W2509512661","https://openalex.org/W2912934387","https://openalex.org/W3014970703","https://openalex.org/W3104887532","https://openalex.org/W3106889297","https://openalex.org/W3123649847","https://openalex.org/W3124203304","https://openalex.org/W3124989725","https://openalex.org/W3125613485","https://openalex.org/W4212883601","https://openalex.org/W4243356819","https://openalex.org/W4243562335","https://openalex.org/W4250236131"],"related_works":["https://openalex.org/W2923680075","https://openalex.org/W2049162494","https://openalex.org/W1998367823","https://openalex.org/W1485482273","https://openalex.org/W2021608985","https://openalex.org/W2295801330","https://openalex.org/W4255881871","https://openalex.org/W3147828720","https://openalex.org/W3125245799","https://openalex.org/W2341042976"],"abstract_inverted_index":{"In":[0,127],"classification,":[1],"regression":[2],"and":[3,97,139,156,160,180],"time":[4,172,192],"series":[5,173,193],"prediction":[6,174],"alike,":[7],"cross-validation":[8,117,138],"is":[9],"widely":[10],"employed":[11],"to":[12,35,61,70,87,118,133,164,205],"estimate":[13],"the":[14,30,37,41,47,55,91,135,190,195],"expected":[15,42],"accuracy":[16,43,201],"of":[17,29,39,54,63,90,101,116,137,148,168,188,194,211],"a":[18,151],"predictive":[19,23],"algorithm":[20],"by":[21,44],"averaging":[22],"errors":[24],"across":[25],"mutually":[26],"exclusive":[27],"subsamples":[28],"data.":[31,56],"Similarly,":[32],"bootstrapping":[33,82],"aims":[34],"increase":[36],"validity":[38],"estimating":[40],"repeatedly":[45],"sub-sampling":[46],"data":[48,178],"with":[49,129],"replacement,":[50],"creating":[51],"overlapping":[52],"samples":[53],"Estimates":[57],"are":[58,77],"then":[59],"used":[60],"anticipate":[62],"future":[64],"risk":[65],"in":[66],"decision":[67],"making,":[68],"or":[69,111,207],"guide":[71],"model":[72,209],"selection":[73,210],"where":[74],"multiple":[75],"candidates":[76],"feasible.":[78],"Beyond":[79],"error":[80],"estimation,":[81,96],"has":[83],"recently":[84],"been":[85,125],"extended":[86],"combine":[88,134],"each":[89,100],"diverse":[92,120,166],"models":[93,122],"created":[94],"for":[95,171],"aggregating":[98],"over":[99],"their":[102,106,182],"predictions":[103],"(rather":[104],"than":[105],"errors),":[107],"coined":[108],"bootstrap":[109],"aggregation":[110],"bagging.":[112],"However,":[113],"similar":[114],"extensions":[115],"create":[119,165],"forecasting":[121,189],"have":[123],"not":[124],"considered.":[126],"accordance":[128],"bagging,":[130],"we":[131],"propose":[132],"benefits":[136],"forecast":[140],"aggregation,":[141],"i.e.":[142],"crogging.":[143],"We":[144],"assesses":[145],"different":[146,177],"levels":[147],"cross-validation,":[149],"including":[150],"(single-fold)":[152],"hold-out":[153],"approach,":[154],"2-fold":[155],"10-fold":[157],"cross":[158,162],"validation":[159],"Monte-Carlos":[161],"validation,":[163],"base-models":[167],"neural":[169,212],"networks":[170],"trained":[175],"on":[176],"subsets,":[179],"average":[181],"individual":[183,208],"multiple-step":[184],"ahead":[185],"predictions.":[186],"Results":[187],"111":[191],"NN3":[196],"competition":[197],"indicate":[198],"significant":[199],"improvements":[200],"through":[202],"Crogging":[203],"relative":[204],"Bagging":[206],"networks.":[213]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":4}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}