{"id":"https://openalex.org/W2089117487","doi":"https://doi.org/10.1109/bicta.2010.5645218","title":"The combining prediction of the RMB exchange rate series based on diverse architectural artificial neural network ensemble methodology","display_name":"The combining prediction of the RMB exchange rate series based on diverse architectural artificial neural network ensemble methodology","publication_year":2010,"publication_date":"2010-09-01","ids":{"openalex":"https://openalex.org/W2089117487","doi":"https://doi.org/10.1109/bicta.2010.5645218","mag":"2089117487"},"language":"en","primary_location":{"id":"doi:10.1109/bicta.2010.5645218","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bicta.2010.5645218","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","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/A5072703232","display_name":"Bo Sun","orcid":"https://orcid.org/0000-0001-9346-6157"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Sun","raw_affiliation_strings":["School of Business Management, Hunan University, Changsha, Hunan, China","School of Business Management, Hunan University, 410082 Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Business Management, Hunan University, Changsha, Hunan, China","institution_ids":["https://openalex.org/I16609230"]},{"raw_affiliation_string":"School of Business Management, Hunan University, 410082 Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063247720","display_name":"Chi Xie","orcid":"https://orcid.org/0000-0003-3862-0224"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chi Xie","raw_affiliation_strings":["School of Business Management, Hunan University, Changsha, Hunan, China","School of Business Management, Hunan University, 410082 Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Business Management, Hunan University, Changsha, Hunan, China","institution_ids":["https://openalex.org/I16609230"]},{"raw_affiliation_string":"School of Business Management, Hunan University, 410082 Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081288824","display_name":"Gang\u2010Jin Wang","orcid":"https://orcid.org/0000-0002-2813-4356"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gangjin Wang","raw_affiliation_strings":["School of Computer and Communication, Hunan University, Changsha, Hunan, China","School of Computer and Communication, Hunan University, 410082 Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer and Communication, Hunan University, Changsha, Hunan, China","institution_ids":["https://openalex.org/I16609230"]},{"raw_affiliation_string":"School of Computer and Communication, Hunan University, 410082 Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020132154","display_name":"Juan Zhang","orcid":"https://orcid.org/0000-0002-1161-1072"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Juan Zhang","raw_affiliation_strings":["School of Business Management, Hunan University, Changsha, Hunan, China","School of Business Management, Hunan University, 410082 Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Business Management, Hunan University, Changsha, Hunan, China","institution_ids":["https://openalex.org/I16609230"]},{"raw_affiliation_string":"School of Business Management, Hunan University, 410082 Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I16609230"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"17","issue":null,"first_page":"743","last_page":"749"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9890999794006348,"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":0.9890999794006348,"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":0.9693999886512756,"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":0.9527000188827515,"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/computer-science","display_name":"Computer science","score":0.7594141364097595},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7130608558654785},{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.5589795112609863},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.54730623960495},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.5470553040504456},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.5218079090118408},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5118774175643921},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.49877405166625977},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.49756816029548645},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.47396132349967957},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.46464109420776367},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4289611577987671},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.42617955803871155},{"id":"https://openalex.org/keywords/exchange-rate","display_name":"Exchange rate","score":0.4149511754512787},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08274424076080322}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7594141364097595},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7130608558654785},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.5589795112609863},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.54730623960495},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.5470553040504456},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.5218079090118408},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5118774175643921},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.49877405166625977},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49756816029548645},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.47396132349967957},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.46464109420776367},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4289611577987671},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.42617955803871155},{"id":"https://openalex.org/C2776988154","wikidata":"https://www.wikidata.org/wiki/Q66100","display_name":"Exchange rate","level":2,"score":0.4149511754512787},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08274424076080322},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C139719470","wikidata":"https://www.wikidata.org/wiki/Q39680","display_name":"Macroeconomics","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bicta.2010.5645218","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bicta.2010.5645218","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1485505078","https://openalex.org/W1534851233","https://openalex.org/W1607735679","https://openalex.org/W1622484071","https://openalex.org/W1905316980","https://openalex.org/W1964796919","https://openalex.org/W1966106237","https://openalex.org/W1983636075","https://openalex.org/W2015666727","https://openalex.org/W2031762450","https://openalex.org/W2037671496","https://openalex.org/W2038705170","https://openalex.org/W2040255839","https://openalex.org/W2045979066","https://openalex.org/W2046356540","https://openalex.org/W2056213886","https://openalex.org/W2091010684","https://openalex.org/W2092436092","https://openalex.org/W2098160455","https://openalex.org/W2120452721","https://openalex.org/W2134918899","https://openalex.org/W2165085526","https://openalex.org/W2179356764","https://openalex.org/W2357477633","https://openalex.org/W3122467650","https://openalex.org/W3123743130","https://openalex.org/W4206419526","https://openalex.org/W4212817194","https://openalex.org/W4238717354","https://openalex.org/W4248582375","https://openalex.org/W6658309285","https://openalex.org/W7037772040"],"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/W2127236386","https://openalex.org/W2523437662","https://openalex.org/W89844371","https://openalex.org/W2019891950","https://openalex.org/W2294552197"],"abstract_inverted_index":{"Motivated":[0],"by":[1],"the":[2,22,26,31,51,55,58,72,82,88,92],"neural":[3,15,46],"network":[4,16],"ensemble":[5,18],"approach,":[6],"this":[7],"paper":[8],"puts":[9],"forward":[10],"a":[11],"diverse":[12],"architectural":[13],"artificial":[14],"(ANN)":[17],"method":[19,84],"to":[20,49,65],"optimize":[21],"combining":[23,67],"prediction":[24],"of":[25,36,74,91],"RMB":[27,102],"exchange":[28,103],"rates.":[29],"On":[30,54],"one":[32],"hand,":[33,57],"four":[34],"types":[35],"architectures":[37],"are":[38],"adopted":[39],"here":[40],"including":[41],"multilayer":[42],"perceptron":[43],"(MLP),":[44],"recurrent":[45],"networks":[47],"(RNNs)":[48],"diversify":[50],"learning":[52],"mechanism.":[53],"other":[56],"nonparametric":[59],"kernel":[60],"smoothing":[61],"technique":[62],"is":[63],"applied":[64],"make":[66],"forecasts,":[68],"which":[69],"can":[70],"overcome":[71],"drawbacks":[73],"traditional":[75],"methods.":[76],"The":[77],"empirical":[78],"results":[79],"show":[80],"that":[81],"proposed":[83],"has":[85],"significantly":[86],"improved":[87],"forecasting":[89],"performance":[90],"optimal":[93],"single":[94],"ANNs":[95],"and":[96],"random":[97],"walk":[98],"model,":[99],"especially":[100],"in":[101],"rate":[104],"series":[105],"forecasting.":[106]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2012,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}