{"id":"https://openalex.org/W2958743743","doi":"https://doi.org/10.1109/access.2019.2927047","title":"ELM-Based Improved Layered Ensemble Architecture for Time Series Forecasting","display_name":"ELM-Based Improved Layered Ensemble Architecture for Time Series Forecasting","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2958743743","doi":"https://doi.org/10.1109/access.2019.2927047","mag":"2958743743"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2927047","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2927047","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08760472.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08760472.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102862730","display_name":"Shuming Fan","orcid":"https://orcid.org/0000-0003-2893-9979"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shuming Fan","raw_affiliation_strings":["College of Information Science and Engineering, Xinjiang University, \u00dcr\u00fcmqi, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Xinjiang University, \u00dcr\u00fcmqi, China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101875516","display_name":"Xizhong Qin","orcid":"https://orcid.org/0000-0003-4457-6629"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xizhong Qin","raw_affiliation_strings":["College of Information Science and Engineering, Xinjiang University, \u00dcr\u00fcmqi, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Xinjiang University, \u00dcr\u00fcmqi, China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100684819","display_name":"Zhenhong Jia","orcid":"https://orcid.org/0000-0002-5182-4929"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenhong Jia","raw_affiliation_strings":["College of Information Science and Engineering, Xinjiang University, \u00dcr\u00fcmqi, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Xinjiang University, \u00dcr\u00fcmqi, China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068945987","display_name":"Xinxue Qi","orcid":"https://orcid.org/0000-0001-9047-9550"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinxue Qi","raw_affiliation_strings":["College of Information Science and Engineering, Xinjiang University, \u00dcr\u00fcmqi, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Xinjiang University, \u00dcr\u00fcmqi, China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085396202","display_name":"Mengmeng Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengmeng Lin","raw_affiliation_strings":["College of Information Science and Engineering, Xinjiang University, \u00dcr\u00fcmqi, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Xinjiang University, \u00dcr\u00fcmqi, China","institution_ids":["https://openalex.org/I96908189"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5102862730"],"corresponding_institution_ids":["https://openalex.org/I96908189"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.14,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.55645549,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"7","issue":null,"first_page":"97827","last_page":"97837"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":1.0,"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/T12676","display_name":"Machine Learning and ELM","score":1.0,"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/T10320","display_name":"Neural Networks and Applications","score":0.9987999796867371,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.8105053901672363},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7875031232833862},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7120806574821472},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6242573261260986},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5443089604377747},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5328980088233948},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4992506504058838},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.47327494621276855},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.4651884138584137},{"id":"https://openalex.org/keywords/cluster","display_name":"Cluster (spacecraft)","score":0.44454336166381836},{"id":"https://openalex.org/keywords/inflection-point","display_name":"Inflection point","score":0.4444091320037842},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.44072476029396057},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4119799733161926},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.41129836440086365},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3702547550201416},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33702367544174194},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10777390003204346}],"concepts":[{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.8105053901672363},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7875031232833862},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7120806574821472},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6242573261260986},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5443089604377747},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5328980088233948},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4992506504058838},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.47327494621276855},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.4651884138584137},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.44454336166381836},{"id":"https://openalex.org/C74545648","wikidata":"https://www.wikidata.org/wiki/Q212794","display_name":"Inflection point","level":2,"score":0.4444091320037842},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44072476029396057},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4119799733161926},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.41129836440086365},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3702547550201416},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33702367544174194},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10777390003204346},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"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},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2019.2927047","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2927047","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08760472.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:16640ba847e5454ebcd3740fa8596cbe","is_oa":true,"landing_page_url":"https://doaj.org/article/16640ba847e5454ebcd3740fa8596cbe","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 7, Pp 97827-97837 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2927047","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2927047","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08760472.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320328898","display_name":"Natural Science Foundation of Xinjiang","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2958743743.pdf","grobid_xml":"https://content.openalex.org/works/W2958743743.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W222543348","https://openalex.org/W1024998140","https://openalex.org/W1527315615","https://openalex.org/W1534477342","https://openalex.org/W1553802819","https://openalex.org/W1841961408","https://openalex.org/W1964892656","https://openalex.org/W2009161336","https://openalex.org/W2010680143","https://openalex.org/W2010767552","https://openalex.org/W2030888282","https://openalex.org/W2046356540","https://openalex.org/W2061119986","https://openalex.org/W2075183947","https://openalex.org/W2080819414","https://openalex.org/W2111072639","https://openalex.org/W2116374865","https://openalex.org/W2125464731","https://openalex.org/W2165335390","https://openalex.org/W2165835468","https://openalex.org/W2167055186","https://openalex.org/W2191329106","https://openalex.org/W2262188337","https://openalex.org/W2301541953","https://openalex.org/W2358865919","https://openalex.org/W2405649773","https://openalex.org/W2479365550","https://openalex.org/W2493441348","https://openalex.org/W2647142571","https://openalex.org/W3004732066","https://openalex.org/W4232478844","https://openalex.org/W4299429830","https://openalex.org/W6631480456"],"related_works":["https://openalex.org/W2099182244","https://openalex.org/W1580150424","https://openalex.org/W2325482571","https://openalex.org/W2076543106","https://openalex.org/W4241032203","https://openalex.org/W4285165247","https://openalex.org/W2127236386","https://openalex.org/W2523437662","https://openalex.org/W89844371","https://openalex.org/W2019891950"],"abstract_inverted_index":{"In":[0,115],"this":[1],"paper,":[2],"an":[3,42],"extreme":[4],"learning":[5,91],"machine":[6],"(ELM)-based":[7],"improved":[8,33],"layered":[9,24],"ensemble":[10,25],"architecture":[11,26,101],"(EILEA)":[12],"for":[13],"time":[14,107],"series":[15,108],"forecasting":[16,118],"is":[17,132],"proposed.":[18],"Compared":[19],"with":[20],"multilayer":[21],"perceptron":[22],"(MLP)-based":[23],"(named":[27],"LEA),":[28],"our":[29,129],"proposed":[30,41,100,130],"structure":[31,131],"has":[32,102],"in":[34],"two":[35],"aspects.":[36],"First":[37],"of":[38,63,68,71,88,110,117],"all,":[39],"we":[40],"inflection":[43],"point":[44],"estimation-based":[45],"density":[46],"peaks":[47],"clustering":[48,77],"algorithm":[49,53],"to":[50,93],"replace":[51],"K-means":[52],"used":[54],"by":[55],"LEA,":[56],"which":[57],"can":[58],"automatically":[59],"determine":[60],"the":[61,66,69,76,96,106,122],"number":[62],"clusters":[64],"without":[65],"influence":[67],"choice":[70],"initial":[72],"cluster":[73],"centers":[74],"on":[75,105],"results.":[78],"Second,":[79],"EILEA":[80],"employs":[81],"ELM":[82],"as":[83],"individual":[84],"learners,":[85],"taking":[86],"advantage":[87],"its":[89],"high":[90],"speed":[92],"greatly":[94],"improve":[95],"prediction":[97],"speed.":[98],"The":[99],"been":[103],"tested":[104],"data":[109],"neural":[111],"network":[112],"(NN)3":[113],"competition.":[114],"terms":[116],"accuracy":[119],"and":[120],"speed,":[121],"experimental":[123],"results":[124],"have":[125],"revealed":[126],"clearly":[127],"that":[128],"better":[133],"than":[134],"LEA.":[135]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}