{"id":"https://openalex.org/W4412191528","doi":"https://doi.org/10.1186/s40537-025-01240-4","title":"Metal commodity futures price forecasting based on a hybrid secondary decomposition error-corrected model","display_name":"Metal commodity futures price forecasting based on a hybrid secondary decomposition error-corrected model","publication_year":2025,"publication_date":"2025-07-11","ids":{"openalex":"https://openalex.org/W4412191528","doi":"https://doi.org/10.1186/s40537-025-01240-4"},"language":"en","primary_location":{"id":"doi:10.1186/s40537-025-01240-4","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01240-4","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01240-4","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01240-4","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037950955","display_name":"Yuetong Zhang","orcid":"https://orcid.org/0009-0008-2574-1493"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuetong Zhang","raw_affiliation_strings":["School of Mathematics, Shandong University, Jinan, China"],"affiliations":[{"raw_affiliation_string":"School of Mathematics, Shandong University, Jinan, China","institution_ids":["https://openalex.org/I154099455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066098784","display_name":"Ying Peng","orcid":"https://orcid.org/0000-0003-4619-5635"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]},{"id":"https://openalex.org/I4210106409","display_name":"China Institute of Finance and Capital Markets","ror":"https://ror.org/01mp98161","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210106409"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Peng","raw_affiliation_strings":["Zhongtai Securities Institute for Financial Studies, Shandong University, Jinan, China"],"affiliations":[{"raw_affiliation_string":"Zhongtai Securities Institute for Financial Studies, Shandong University, Jinan, China","institution_ids":["https://openalex.org/I4210106409","https://openalex.org/I154099455"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100653854","display_name":"Yuping Song","orcid":"https://orcid.org/0000-0002-7506-1719"},"institutions":[{"id":"https://openalex.org/I21945476","display_name":"Shanghai Normal University","ror":"https://ror.org/01cxqmw89","country_code":"CN","type":"education","lineage":["https://openalex.org/I21945476"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuping Song","raw_affiliation_strings":["School of Finance and Business, Shanghai Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Finance and Business, Shanghai Normal University, Shanghai, China","institution_ids":["https://openalex.org/I21945476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100653854"],"corresponding_institution_ids":["https://openalex.org/I21945476"],"apc_list":{"value":1060,"currency":"GBP","value_usd":1300},"apc_paid":{"value":1060,"currency":"GBP","value_usd":1300},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.24022925,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"12","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11059","display_name":"Market Dynamics and Volatility","score":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11059","display_name":"Market Dynamics and Volatility","score":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12368","display_name":"Grey System Theory Applications","score":0.9919000267982483,"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":0.9915000200271606,"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/futures-contract","display_name":"Futures contract","score":0.860397458076477},{"id":"https://openalex.org/keywords/commodity","display_name":"Commodity","score":0.6679798364639282},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5542435646057129},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.5138678550720215},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4545234739780426},{"id":"https://openalex.org/keywords/computational-science-and-engineering","display_name":"Computational Science and Engineering","score":0.433176726102829},{"id":"https://openalex.org/keywords/financial-economics","display_name":"Financial economics","score":0.19239291548728943},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.16747647523880005},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.15803447365760803},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.1011132001876831},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.08400139212608337}],"concepts":[{"id":"https://openalex.org/C106306483","wikidata":"https://www.wikidata.org/wiki/Q183984","display_name":"Futures contract","level":2,"score":0.860397458076477},{"id":"https://openalex.org/C2779439359","wikidata":"https://www.wikidata.org/wiki/Q317088","display_name":"Commodity","level":2,"score":0.6679798364639282},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5542435646057129},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.5138678550720215},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4545234739780426},{"id":"https://openalex.org/C68597687","wikidata":"https://www.wikidata.org/wiki/Q362601","display_name":"Computational Science and Engineering","level":2,"score":0.433176726102829},{"id":"https://openalex.org/C106159729","wikidata":"https://www.wikidata.org/wiki/Q2294553","display_name":"Financial economics","level":1,"score":0.19239291548728943},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.16747647523880005},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.15803447365760803},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.1011132001876831},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.08400139212608337},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s40537-025-01240-4","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01240-4","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01240-4","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:87f6c2886eb7403eb362e779769ca64a","is_oa":true,"landing_page_url":"https://doaj.org/article/87f6c2886eb7403eb362e779769ca64a","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","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Journal of Big Data, Vol 12, Iss 1, Pp 1-60 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s40537-025-01240-4","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01240-4","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01240-4","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8","score":0.41999998688697815}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412191528.pdf","grobid_xml":"https://content.openalex.org/works/W4412191528.grobid-xml"},"referenced_works_count":58,"referenced_works":["https://openalex.org/W1976944686","https://openalex.org/W1988790447","https://openalex.org/W1999996900","https://openalex.org/W2000982976","https://openalex.org/W2006816577","https://openalex.org/W2006991759","https://openalex.org/W2007221293","https://openalex.org/W2039935421","https://openalex.org/W2070493638","https://openalex.org/W2091511840","https://openalex.org/W2110485445","https://openalex.org/W2120390927","https://openalex.org/W2125056386","https://openalex.org/W2125142831","https://openalex.org/W2327731887","https://openalex.org/W2593740144","https://openalex.org/W2614878903","https://openalex.org/W2624385633","https://openalex.org/W2792913188","https://openalex.org/W2799635155","https://openalex.org/W2806777472","https://openalex.org/W2894821558","https://openalex.org/W2897658618","https://openalex.org/W2900453322","https://openalex.org/W2900478665","https://openalex.org/W2911964244","https://openalex.org/W2936236242","https://openalex.org/W2956431040","https://openalex.org/W2979028505","https://openalex.org/W3008982768","https://openalex.org/W3013981602","https://openalex.org/W3029632564","https://openalex.org/W3037801011","https://openalex.org/W3092453270","https://openalex.org/W3094704314","https://openalex.org/W3124165360","https://openalex.org/W3165089818","https://openalex.org/W3199315834","https://openalex.org/W4206409835","https://openalex.org/W4210603800","https://openalex.org/W4220794015","https://openalex.org/W4220865511","https://openalex.org/W4229333499","https://openalex.org/W4232474910","https://openalex.org/W4281756923","https://openalex.org/W4283326523","https://openalex.org/W4291327732","https://openalex.org/W4292483811","https://openalex.org/W4309048297","https://openalex.org/W4313462169","https://openalex.org/W4322631283","https://openalex.org/W4324046743","https://openalex.org/W4380682598","https://openalex.org/W4389726876","https://openalex.org/W4390640387","https://openalex.org/W4396675864","https://openalex.org/W6677096361","https://openalex.org/W7055446726"],"related_works":["https://openalex.org/W2200638682","https://openalex.org/W2494020708","https://openalex.org/W2080194917","https://openalex.org/W2230611034","https://openalex.org/W4387762087","https://openalex.org/W2069474937","https://openalex.org/W3124189739","https://openalex.org/W1539109379","https://openalex.org/W3124292700","https://openalex.org/W2100954101"],"abstract_inverted_index":{"Although":[0],"the":[1,10,18,22,25,29,36,64,74,89,93,97,103,109,123,130,133,136,142,153,157,161,189,195,199,214],"existing":[2],"hybrid":[3,46],"model":[4,50,112,166,193,197,210],"based":[5,128],"on":[6,129,205],"decomposition":[7,48,155,163,191],"can":[8,211],"improve":[9],"prediction":[11,40,138,144,158],"performance":[12,202],"for":[13,51,113,221],"financial":[14],"data,":[15],"it":[16],"ignores":[17],"effective":[19],"information":[20,38],"of":[21,39,132,160],"error":[23,90,164],"between":[24,92],"real":[26,98],"value":[27,37],"and":[28,60,77,96,106,127,173,183,203,217],"predicted":[30,82,94,117],"value.":[31],"In":[32,187],"order":[33],"to":[34,72,121,152],"explore":[35],"errors,":[41],"this":[42],"paper":[43],"constructs":[44],"a":[45],"secondary":[47,162,190],"error-corrected":[49,192],"four":[52],"commodities":[53],"prices":[54,216],"such":[55],"as":[56],"gold,":[57],"aluminum,":[58],"whorl,":[59],"iron":[61],"mine":[62],"in":[63],"metal":[65],"futures":[66,215],"market.":[67],"Firstly,":[68],"VMD":[69],"is":[70,81,100,139,167],"used":[71],"decompose":[73],"original":[75],"price,":[76],"then":[78],"each":[79],"component":[80],"using":[83,102,141,194],"11":[84],"machine":[85,178,181],"learning":[86,111,185],"models.":[87,186],"Secondly,":[88],"sequence":[91],"results":[95,148],"price":[99],"decomposed":[101],"CEEMDAN":[104],"method":[105],"combined":[107],"with":[108],"deep":[110,184],"prediction.":[114],"Finally,":[115],"two":[116],"sequences":[118],"are":[119],"reconstructed":[120],"obtain":[122],"final":[124],"one-step":[125,134],"result,":[126],"result":[131],"prediction,":[135],"two-step":[137],"made":[140],"sliding":[143],"method.":[145],"The":[146,207],"empirical":[147],"show":[149],"that":[150],"compared":[151],"one-time":[154],"model,":[156],"accuracy":[159],"correction":[165],"improved":[168],"by":[169],"about":[170],"32%,":[171],"33%,":[172],"22%":[174],"respectively":[175],"under":[176],"single":[177],"learning,":[179,182],"ensemble":[180],"addition,":[188],"GRU":[196],"has":[198],"best":[200],"predictive":[201],"return":[204],"investment.":[206],"proposed":[208],"VMD-GRU-CEEMDAN-GRU":[209],"accurately":[212],"predict":[213],"provide":[218],"valuable":[219],"guidance":[220],"practitioners.":[222]},"counts_by_year":[],"updated_date":"2026-03-17T17:19:04.345684","created_date":"2025-10-10T00:00:00"}
