{"id":"https://openalex.org/W4395463438","doi":"https://doi.org/10.3390/e26050358","title":"Crude Oil Prices Forecast Based on Mixed-Frequency Deep Learning Approach and Intelligent Optimization Algorithm","display_name":"Crude Oil Prices Forecast Based on Mixed-Frequency Deep Learning Approach and Intelligent Optimization Algorithm","publication_year":2024,"publication_date":"2024-04-24","ids":{"openalex":"https://openalex.org/W4395463438","doi":"https://doi.org/10.3390/e26050358","pmid":"https://pubmed.ncbi.nlm.nih.gov/38785607"},"language":"en","primary_location":{"id":"doi:10.3390/e26050358","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e26050358","pdf_url":"https://www.mdpi.com/1099-4300/26/5/358/pdf?version=1713972158","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1099-4300/26/5/358/pdf?version=1713972158","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076781257","display_name":"Wanbo Lu","orcid":"https://orcid.org/0000-0002-5154-3297"},"institutions":[{"id":"https://openalex.org/I204831749","display_name":"Southwestern University of Finance and Economics","ror":"https://ror.org/04ewct822","country_code":"CN","type":"education","lineage":["https://openalex.org/I204831749"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wanbo Lu","raw_affiliation_strings":["School of Management Science and Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Management Science and Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China","institution_ids":["https://openalex.org/I204831749"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028815739","display_name":"Zhaojie Huang","orcid":"https://orcid.org/0000-0003-0609-8507"},"institutions":[{"id":"https://openalex.org/I204831749","display_name":"Southwestern University of Finance and Economics","ror":"https://ror.org/04ewct822","country_code":"CN","type":"education","lineage":["https://openalex.org/I204831749"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaojie Huang","raw_affiliation_strings":["School of Statistics, Southwestern University of Finance and Economics, Chengdu 611130, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Statistics, Southwestern University of Finance and Economics, Chengdu 611130, China","institution_ids":["https://openalex.org/I204831749"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5076781257"],"corresponding_institution_ids":["https://openalex.org/I204831749"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":18.8696,"has_fulltext":true,"cited_by_count":18,"citation_normalized_percentile":{"value":0.98825169,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"26","issue":"5","first_page":"358","last_page":"358"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11059","display_name":"Market Dynamics and Volatility","score":0.9990000128746033,"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":0.9990000128746033,"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":0.972100019454956,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9599000215530396,"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":0.597759485244751},{"id":"https://openalex.org/keywords/autoregressive-conditional-heteroskedasticity","display_name":"Autoregressive conditional heteroskedasticity","score":0.5467868447303772},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5146316289901733},{"id":"https://openalex.org/keywords/volatility","display_name":"Volatility (finance)","score":0.5070285797119141},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.44699373841285706},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.41344940662384033},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3910125494003296},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3653985261917114},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3559167683124542},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3234383463859558},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20578691363334656}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.597759485244751},{"id":"https://openalex.org/C23922673","wikidata":"https://www.wikidata.org/wiki/Q180752","display_name":"Autoregressive conditional heteroskedasticity","level":3,"score":0.5467868447303772},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5146316289901733},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.5070285797119141},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.44699373841285706},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.41344940662384033},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3910125494003296},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3653985261917114},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3559167683124542},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3234383463859558},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20578691363334656},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3390/e26050358","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e26050358","pdf_url":"https://www.mdpi.com/1099-4300/26/5/358/pdf?version=1713972158","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},{"id":"pmid:38785607","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/38785607","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:11120460","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11120460","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11120460/pdf/entropy-26-00358.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy (Basel)","raw_type":"Text"},{"id":"pmh:oai:doaj.org/article:b3da49a1f46a4ba79b88cd1736a1db00","is_oa":true,"landing_page_url":"https://doaj.org/article/b3da49a1f46a4ba79b88cd1736a1db00","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy, Vol 26, Iss 5, p 358 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/e26050358","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e26050358","pdf_url":"https://www.mdpi.com/1099-4300/26/5/358/pdf?version=1713972158","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1942083779","display_name":null,"funder_award_id":"71771187","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5505587735","display_name":null,"funder_award_id":"72011530149","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8562682562","display_name":null,"funder_award_id":"72163029","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4395463438.pdf"},"referenced_works_count":55,"referenced_works":["https://openalex.org/W1520841927","https://openalex.org/W1661370443","https://openalex.org/W1993887071","https://openalex.org/W1997522936","https://openalex.org/W1999743657","https://openalex.org/W2000982976","https://openalex.org/W2007306174","https://openalex.org/W2021606320","https://openalex.org/W2041578667","https://openalex.org/W2064675550","https://openalex.org/W2065794328","https://openalex.org/W2066017952","https://openalex.org/W2079746936","https://openalex.org/W2087936470","https://openalex.org/W2125536334","https://openalex.org/W2164104048","https://openalex.org/W2590661630","https://openalex.org/W2758620441","https://openalex.org/W2766900003","https://openalex.org/W2800356887","https://openalex.org/W2887255427","https://openalex.org/W2896747518","https://openalex.org/W2896761929","https://openalex.org/W2998553334","https://openalex.org/W3016293336","https://openalex.org/W3021299754","https://openalex.org/W3022628943","https://openalex.org/W3117321808","https://openalex.org/W3122018694","https://openalex.org/W3124142770","https://openalex.org/W3125080556","https://openalex.org/W3158809276","https://openalex.org/W3166219252","https://openalex.org/W3197215082","https://openalex.org/W3197352768","https://openalex.org/W3203757401","https://openalex.org/W3214092996","https://openalex.org/W4200351803","https://openalex.org/W4220736439","https://openalex.org/W4220910858","https://openalex.org/W4229051643","https://openalex.org/W4280600230","https://openalex.org/W4280650517","https://openalex.org/W4283805989","https://openalex.org/W4284962957","https://openalex.org/W4291327732","https://openalex.org/W4293147413","https://openalex.org/W4294496175","https://openalex.org/W4295844937","https://openalex.org/W4296905779","https://openalex.org/W4297105462","https://openalex.org/W4306894664","https://openalex.org/W4308509827","https://openalex.org/W4367047141","https://openalex.org/W4386385389"],"related_works":["https://openalex.org/W2171218219","https://openalex.org/W1972271943","https://openalex.org/W2150410159","https://openalex.org/W4327525404","https://openalex.org/W4287185323","https://openalex.org/W3150905897","https://openalex.org/W1520183331","https://openalex.org/W2734842993","https://openalex.org/W4386802740","https://openalex.org/W1986811129"],"abstract_inverted_index":{"Precisely":[0],"forecasting":[1,128],"the":[2,27,31,36,64,87,98,115,120,131,140,145,172],"price":[3],"of":[4,14,81,100,144,161],"crude":[5,168],"oil":[6,169],"is":[7,84,136,149],"challenging":[8],"due":[9],"to":[10,61,126,138],"its":[11],"fundamental":[12],"properties":[13],"nonlinearity,":[15],"volatility,":[16],"and":[17,47,56,66,107,119,166,181,184,193],"stochasticity.":[18],"This":[19,188],"paper":[20],"introduces":[21],"a":[22],"novel":[23],"hybrid":[24],"model,":[25,29,147],"namely,":[26],"KV-MFSCBA-G":[28],"within":[30],"decomposition-integration":[32],"paradigm.":[33],"It":[34],"combines":[35],"mixed-frequency":[37],"convolutional":[38],"neural":[39],"network-bidirectional":[40],"long":[41],"short-term":[42],"memory":[43],"network-attention":[44],"mechanism":[45],"(MFCBA)":[46],"generalized":[48],"autoregressive":[49],"conditional":[50],"heteroskedasticity":[51],"(GARCH)":[52],"models.":[53],"The":[54,79,158],"MFCBA":[55,146],"GARCH":[57],"models":[58,110,177],"are":[59,124],"employed":[60,137],"respectively":[62],"forecast":[63],"low-frequency":[65,121],"high-frequency":[67],"components":[68,83],"decomposed":[69],"through":[70],"variational":[71],"mode":[72],"decomposition":[73],"optimized":[74],"by":[75],"Kullback-Leibler":[76],"divergence":[77],"(KL-VMD).":[78],"classification":[80],"these":[82],"performed":[85],"using":[86],"fuzzy":[88],"entropy":[89],"(FE)":[90],"algorithm.":[91],"Therefore,":[92],"this":[93],"model":[94,189],"can":[95,190],"fully":[96],"exploit":[97],"advantages":[99],"deep":[101],"learning":[102],"networks":[103],"in":[104,111,178,196],"fitting":[105],"nonlinearities":[106],"traditional":[108],"econometric":[109],"capturing":[112],"volatilities.":[113],"Furthermore,":[114],"intelligent":[116],"optimization":[117],"algorithm":[118,134],"economic":[122,154],"variable":[123],"introduced":[125],"improve":[127],"performance.":[129],"Specifically,":[130],"sparrow":[132],"search":[133],"(SSA)":[135],"determine":[139],"optimal":[141],"parameter":[142],"combination":[143],"which":[148],"incorporated":[150],"with":[151],"monthly":[152],"global":[153],"conditions":[155],"(GECON)":[156],"data.":[157],"empirical":[159],"findings":[160],"West":[162],"Texas":[163],"Intermediate":[164],"(WTI)":[165],"Brent":[167],"indicate":[170],"that":[171],"proposed":[173],"approach":[174],"outperforms":[175],"other":[176],"evaluation":[179],"indicators":[180],"statistical":[182],"tests":[183],"has":[185],"good":[186],"robustness.":[187],"assist":[191],"investors":[192],"market":[194],"regulators":[195],"making":[197],"decisions.":[198]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
