{"id":"https://openalex.org/W2121324589","doi":"https://doi.org/10.1109/ijcnn.2010.5596636","title":"Naive Support Vector Regression and Multilayer Perceptron benchmarks for the 2010 neural network grand competition (NNGC) on time series prediction","display_name":"Naive Support Vector Regression and Multilayer Perceptron benchmarks for the 2010 neural network grand competition (NNGC) on time series prediction","publication_year":2010,"publication_date":"2010-07-01","ids":{"openalex":"https://openalex.org/W2121324589","doi":"https://doi.org/10.1109/ijcnn.2010.5596636","mag":"2121324589"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2010.5596636","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2010.5596636","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 2010 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/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":["Department of Management Science and the Research Centre for Forecasting, Lancaster University Management School, Lancaster, UK","Department of Management Science and the Research Centre for Forecasting, Lancaster University Management School, LA1 4YX, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Management Science and the Research Centre for Forecasting, Lancaster University Management School, Lancaster, UK","institution_ids":["https://openalex.org/I67415387"]},{"raw_affiliation_string":"Department of Management Science and the Research Centre for Forecasting, Lancaster University Management School, LA1 4YX, United Kingdom","institution_ids":["https://openalex.org/I67415387"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064340262","display_name":"Nikolaos Kourentzes","orcid":"https://orcid.org/0000-0003-0211-5218"},"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":"Nikolaos Kourentzes","raw_affiliation_strings":["Department of Management Science and the Research Centre for Forecasting, Lancaster University Management School, Lancaster, UK","Department of Management Science and the Research Centre for Forecasting, Lancaster University Management School, LA1 4YX, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Management Science and the Research Centre for Forecasting, Lancaster University Management School, Lancaster, UK","institution_ids":["https://openalex.org/I67415387"]},{"raw_affiliation_string":"Department of Management Science and the Research Centre for Forecasting, Lancaster University Management School, LA1 4YX, United Kingdom","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":0.5449,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.65361722,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"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/T10320","display_name":"Neural Networks and Applications","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9994999766349792,"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":"Stock Market Forecasting Methods","score":0.9970999956130981,"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":0.9944000244140625,"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/benchmark","display_name":"Benchmark (surveying)","score":0.7745394706726074},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7366698980331421},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7215474247932434},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6850517988204956},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6442532539367676},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6012556552886963},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.516228199005127},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5152223706245422},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.47755345702171326},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.4519665837287903},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.4128956198692322},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.4118478298187256},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38919442892074585},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1233479380607605}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7745394706726074},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7366698980331421},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7215474247932434},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6850517988204956},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6442532539367676},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6012556552886963},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.516228199005127},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5152223706245422},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.47755345702171326},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.4519665837287903},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.4128956198692322},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.4118478298187256},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38919442892074585},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1233479380607605},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","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":3,"locations":[{"id":"doi:10.1109/ijcnn.2010.5596636","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2010.5596636","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 2010 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.642.7966","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.642.7966","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.sven-crone.com/papers/Crone%2C Kourentzes (2010) Naive Support Vector Regression and Multilayer Perceptron benchmarks for the 2010 neural network grand competition (NNGC) on time series prediction 05596636.pdf","raw_type":"text"},{"id":"pmh:oai:eprints.lancs.ac.uk:56119","is_oa":false,"landing_page_url":"https://eprints.lancs.ac.uk/id/eprint/56119/","pdf_url":null,"source":{"id":"https://openalex.org/S4306401916","display_name":"Lancaster EPrints (Lancaster University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67415387","host_organization_name":"Lancaster University","host_organization_lineage":["https://openalex.org/I67415387"],"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":"Contribution in Book/Report/Proceedings"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8100000023841858,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1563088657","https://openalex.org/W1580637085","https://openalex.org/W1584535387","https://openalex.org/W1586335931","https://openalex.org/W1589413298","https://openalex.org/W1590744426","https://openalex.org/W1964357740","https://openalex.org/W1981407057","https://openalex.org/W2013407702","https://openalex.org/W2015393497","https://openalex.org/W2016210396","https://openalex.org/W2017337590","https://openalex.org/W2029803196","https://openalex.org/W2042506099","https://openalex.org/W2050099778","https://openalex.org/W2084175761","https://openalex.org/W2088897322","https://openalex.org/W2090216516","https://openalex.org/W2100805904","https://openalex.org/W2106701503","https://openalex.org/W2109943925","https://openalex.org/W2112650069","https://openalex.org/W2115694019","https://openalex.org/W2124776405","https://openalex.org/W2130765257","https://openalex.org/W2139212933","https://openalex.org/W2149298154","https://openalex.org/W2158994553","https://openalex.org/W2313953460","https://openalex.org/W2592023122","https://openalex.org/W4236355931","https://openalex.org/W6635154370","https://openalex.org/W6635224427","https://openalex.org/W6654744057"],"related_works":["https://openalex.org/W2076543106","https://openalex.org/W2019891950","https://openalex.org/W2085842814","https://openalex.org/W2523437662","https://openalex.org/W4387048144","https://openalex.org/W2492135063","https://openalex.org/W2362514456","https://openalex.org/W2766585573","https://openalex.org/W4387490624","https://openalex.org/W4286643620"],"abstract_inverted_index":{"In":[0,115,190],"recent":[1],"forecasting":[2],"competitions,":[3],"algorithms":[4,97,179],"of":[5,17,27,48,64,84,93,96,108,122,132,154,172,186,211],"Support":[6],"Vector":[7],"Regression":[8],"(SVR)":[9],"and":[10,43,66,102,112,127,213],"Neural":[11],"Networks":[12],"(NN)":[13],"have":[14,69],"provided":[15],"some":[16,26],"the":[18,28,91,130,155,182,187,199],"most":[19,200],"accurate":[20,30],"time":[21,184,216],"series":[22,185,217],"predictions,":[23],"but":[24],"also":[25],"least":[29],"contenders":[31],"failing":[32],"to":[33,74,89,104,117,141,151,195,198,208],"outperform":[34],"even":[35],"simple":[36],"statistical":[37],"benchmark":[38,143,176],"methods.":[39],"As":[40],"both":[41,125,178],"SVR":[42,126],"NN":[44,128],"offer":[45],"substantial":[46,78],"degrees":[47],"freedom":[49],"in":[50,80,129,158,180,203,215],"model":[51,159,209],"building":[52],"(e.g.":[53],"selecting":[54],"input":[55],"variables,":[56],"kernel":[57],"or":[58],"activation":[59],"functions,":[60],"etc.),":[61],"a":[62,94,99,119,146,167,173],"myriad":[63],"heuristics":[65],"ad-hoc":[67],"rules":[68],"emerged":[70],"which":[71,149],"may":[72],"lead":[73,207],"different":[75],"models":[76],"with":[77],"differences":[79],"performance.":[81],"The":[82,161],"heterogeneity":[83],"results":[85,144],"impairs":[86],"our":[87],"ability":[88],"compare":[90],"adequacy":[92],"class":[95,175],"for":[98,124,177],"given":[100],"dataset,":[101],"fails":[103],"develop":[105],"an":[106,133],"understanding":[107],"their":[109,192],"presumed":[110],"nonlinear":[111],"non-parametric":[113],"capabilities.":[114],"order":[116],"determine":[118],"generalized":[120],"estimate":[121],"performance":[123],"absence":[131],"accepted":[134],"`best":[135],"practice'":[136],"methodology,":[137],"this":[138],"paper":[139],"seeks":[140],"compute":[142],"employing":[145],"na\u00efve":[147],"methodology":[148],"attempts":[150],"mimic":[152],"many":[153],"common":[156,201],"mistakes":[157,202],"building.":[160],"naive":[162],"methodologies":[163],"serve":[164],"primarily":[165],"as":[166],"lower":[168],"error":[169],"bound,":[170],"representative":[171],"within":[174],"predicting":[181],"66":[183],"NNGC":[188],"Competition.":[189],"addition,":[191],"discussion":[193],"aims":[194],"draw":[196],"attention":[197],"modelling":[204],"that":[205],"regularly":[206],"misspecification":[210],"MLPs":[212],"SVRs":[214],"forecasting.":[218]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}