{"id":"https://openalex.org/W7082971844","doi":"https://doi.org/10.1109/access.2025.3613336","title":"An Attention-Guided Improved Decomposition-Reconstruction Model for Stock Market Prediction","display_name":"An Attention-Guided Improved Decomposition-Reconstruction Model for Stock Market Prediction","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W7082971844","doi":"https://doi.org/10.1109/access.2025.3613336"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3613336","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3613336","pdf_url":null,"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://doi.org/10.1109/access.2025.3613336","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Yi Li","orcid":"https://orcid.org/0000-0002-1658-1044"},"institutions":[{"id":"https://openalex.org/I66883779","display_name":"Weifang University","ror":"https://ror.org/01frp7483","country_code":"CN","type":"education","lineage":["https://openalex.org/I66883779"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Li","raw_affiliation_strings":["School of Economics and Management, Weifang University, Weifang, China"],"raw_orcid":"https://orcid.org/0000-0002-1658-1044","affiliations":[{"raw_affiliation_string":"School of Economics and Management, Weifang University, Weifang, China","institution_ids":["https://openalex.org/I66883779"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Haipeng Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I66883779","display_name":"Weifang University","ror":"https://ror.org/01frp7483","country_code":"CN","type":"education","lineage":["https://openalex.org/I66883779"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haipeng Wu","raw_affiliation_strings":["School of Economics and Management, Weifang University, Weifang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Economics and Management, Weifang University, Weifang, China","institution_ids":["https://openalex.org/I66883779"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Cuijing Liang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136047","display_name":"Weifang University of Science and Technology","ror":"https://ror.org/04ha2bb10","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136047"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cuijing Liang","raw_affiliation_strings":["School of Economics and Management, Weifang University of Science and Technology, Weifang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Economics and Management, Weifang University of Science and Technology, Weifang, China","institution_ids":["https://openalex.org/I4210136047"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Caixia Bu","orcid":null},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Caixia Bu","raw_affiliation_strings":["College of Economics and Management, Shandong University of Science and Technology, Qingdao, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Economics and Management, Shandong University of Science and Technology, Qingdao, China","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Huawang Jin","orcid":null},"institutions":[{"id":"https://openalex.org/I66883779","display_name":"Weifang University","ror":"https://ror.org/01frp7483","country_code":"CN","type":"education","lineage":["https://openalex.org/I66883779"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huawang Jin","raw_affiliation_strings":["School of Economics and Management, Weifang University, Weifang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Economics and Management, Weifang University, Weifang, China","institution_ids":["https://openalex.org/I66883779"]}]},{"author_position":"last","author":{"id":null,"display_name":"Lei Chen","orcid":"https://orcid.org/0009-0007-0646-4833"},"institutions":[{"id":"https://openalex.org/I66883779","display_name":"Weifang University","ror":"https://ror.org/01frp7483","country_code":"CN","type":"education","lineage":["https://openalex.org/I66883779"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Chen","raw_affiliation_strings":["School of Economics and Management, Weifang University, Weifang, China"],"raw_orcid":"https://orcid.org/0009-0007-0646-4833","affiliations":[{"raw_affiliation_string":"School of Economics and Management, Weifang University, Weifang, China","institution_ids":["https://openalex.org/I66883779"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.7588,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.90882691,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"13","issue":null,"first_page":"167893","last_page":"167911"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.7067000269889832,"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/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.7067000269889832,"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/T13067","display_name":"Geological Modeling and Analysis","score":0.02810000069439411,"subfield":{"id":"https://openalex.org/subfields/1906","display_name":"Geochemistry and Petrology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14311","display_name":"Electrical and Electromagnetic Research","score":0.015799999237060547,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.6814000010490417},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.5056999921798706},{"id":"https://openalex.org/keywords/stock-market-index","display_name":"Stock market index","score":0.4108999967575073},{"id":"https://openalex.org/keywords/stock-market-prediction","display_name":"Stock market prediction","score":0.4065999984741211},{"id":"https://openalex.org/keywords/stock-exchange","display_name":"Stock exchange","score":0.3880999982357025},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.38519999384880066},{"id":"https://openalex.org/keywords/concatenation","display_name":"Concatenation (mathematics)","score":0.3571999967098236},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.35179999470710754}],"concepts":[{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.6814000010490417},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6693999767303467},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5450000166893005},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.5056999921798706},{"id":"https://openalex.org/C88389905","wikidata":"https://www.wikidata.org/wiki/Q223371","display_name":"Stock market index","level":4,"score":0.4108999967575073},{"id":"https://openalex.org/C2776256503","wikidata":"https://www.wikidata.org/wiki/Q7617906","display_name":"Stock market prediction","level":4,"score":0.4065999984741211},{"id":"https://openalex.org/C200870193","wikidata":"https://www.wikidata.org/wiki/Q11691","display_name":"Stock exchange","level":2,"score":0.3880999982357025},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.38519999384880066},{"id":"https://openalex.org/C87619178","wikidata":"https://www.wikidata.org/wiki/Q126002","display_name":"Concatenation (mathematics)","level":2,"score":0.3571999967098236},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.35179999470710754},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.3418000042438507},{"id":"https://openalex.org/C25570617","wikidata":"https://www.wikidata.org/wiki/Q1006462","display_name":"Hilbert\u2013Huang transform","level":3,"score":0.31450000405311584},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3061000108718872},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29030001163482666},{"id":"https://openalex.org/C163068380","wikidata":"https://www.wikidata.org/wiki/Q3409313","display_name":"Economic forecasting","level":2,"score":0.2896000146865845},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.2851000130176544},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.2800000011920929},{"id":"https://openalex.org/C19244329","wikidata":"https://www.wikidata.org/wiki/Q208697","display_name":"Financial market","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C12747933","wikidata":"https://www.wikidata.org/wiki/Q13405082","display_name":"Stock market bubble","level":4,"score":0.266400009393692},{"id":"https://openalex.org/C202353208","wikidata":"https://www.wikidata.org/wiki/Q1167393","display_name":"Economic indicator","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.2558000087738037},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2547999918460846},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.25450000166893005}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3613336","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3613336","pdf_url":null,"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:92ecf651c37d42f9a663b94341eed089","is_oa":true,"landing_page_url":"https://doaj.org/article/92ecf651c37d42f9a663b94341eed089","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":"IEEE Access, Vol 13, Pp 167893-167911 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3613336","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3613336","pdf_url":null,"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":[{"id":"https://openalex.org/G4173798820","display_name":null,"funder_award_id":"2022BS48","funder_id":"https://openalex.org/F4320329228","funder_display_name":"Science Research Foundation of Xijing University"},{"id":"https://openalex.org/G7699897586","display_name":null,"funder_award_id":"23AJY026","funder_id":"https://openalex.org/F4320335869","funder_display_name":"National Social Science Fund of China"},{"id":"https://openalex.org/G8596620158","display_name":null,"funder_award_id":"ZR2021QG063","funder_id":"https://openalex.org/F4320324174","funder_display_name":"Natural Science Foundation of Shandong Province"}],"funders":[{"id":"https://openalex.org/F4320324174","display_name":"Natural Science Foundation of Shandong Province","ror":null},{"id":"https://openalex.org/F4320329228","display_name":"Science Research Foundation of Xijing University","ror":null},{"id":"https://openalex.org/F4320335869","display_name":"National Social Science Fund of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W2000982976","https://openalex.org/W2120390927","https://openalex.org/W2125056386","https://openalex.org/W2303916163","https://openalex.org/W2515598829","https://openalex.org/W2550553598","https://openalex.org/W2572501969","https://openalex.org/W2604842920","https://openalex.org/W2606878197","https://openalex.org/W2607582014","https://openalex.org/W2752782242","https://openalex.org/W2907301123","https://openalex.org/W2949202718","https://openalex.org/W3009650506","https://openalex.org/W3015228848","https://openalex.org/W3018758730","https://openalex.org/W3093310271","https://openalex.org/W3157059523","https://openalex.org/W3159338921","https://openalex.org/W3177318507","https://openalex.org/W3181448069","https://openalex.org/W3196636059","https://openalex.org/W4283756589","https://openalex.org/W4289752563","https://openalex.org/W4300773007","https://openalex.org/W4313886477","https://openalex.org/W4367397399","https://openalex.org/W4385245566","https://openalex.org/W4385360916","https://openalex.org/W4386189827","https://openalex.org/W4387035631","https://openalex.org/W4389117490","https://openalex.org/W4390505615","https://openalex.org/W4393864986","https://openalex.org/W4394978275","https://openalex.org/W4396773831","https://openalex.org/W4400394714","https://openalex.org/W4400418245","https://openalex.org/W4400571096","https://openalex.org/W4401305795","https://openalex.org/W4402436873","https://openalex.org/W4402534263","https://openalex.org/W4402895136","https://openalex.org/W4403360639","https://openalex.org/W4403903907","https://openalex.org/W4405372063","https://openalex.org/W4407360717","https://openalex.org/W4409494094"],"related_works":[],"abstract_inverted_index":{"The":[0,99,123],"stock":[1,11,46,73,82,186],"market":[2,12,35,74],"plays":[3],"a":[4,61],"pivotal":[5],"role":[6],"in":[7,18],"global":[8],"economy.":[9],"Accurate":[10],"prediction":[13,129],"not":[14],"only":[15],"assists":[16],"governments":[17],"formulating":[19],"macroeconomic":[20],"policies":[21],"but":[22],"also":[23],"helps":[24],"investors":[25],"to":[26,32,51,94,128,152],"make":[27],"informed":[28],"decisions,":[29],"thereby":[30],"contributing":[31],"economic":[33],"and":[34,43,105,163],"stability.":[36],"However,":[37],"the":[38,77,80,113,132,138,143,149,168,190,194],"inherent":[39,97],"high":[40],"volatility,":[41],"nonlinearity,":[42],"nonstationarity":[44],"of":[45,125,137,179,196],"data":[47],"pose":[48],"significant":[49],"challenges":[50],"accurate":[52,72],"forecasting.":[53],"To":[54],"address":[55],"these":[56],"challenges,":[57],"this":[58],"study":[59,192],"proposes":[60],"novel":[62],"hybrid":[63],"model,":[64],"named":[65],"Attention-guided":[66],"Improved":[67],"Decomposition-Reconstruction":[68],"Model":[69],"(AIDRM),":[70],"for":[71],"prediction.":[75],"First,":[76],"model":[78,114,151,170],"decomposes":[79],"raw":[81],"indices":[83,159],"using":[84],"Complete":[85],"Ensemble":[86],"Empirical":[87],"Mode":[88],"Decomposition":[89],"with":[90],"Adaptive":[91],"Noise":[92],"(CEEMDAN)":[93],"capture":[95],"their":[96],"complexity.":[98],"decomposed":[100],"sub-series":[101,126],"are":[102,146],"then":[103],"concatenated":[104],"adaptively":[106],"weighted":[107],"through":[108],"an":[109],"attention":[110],"mechanism,":[111],"guiding":[112],"focus":[115],"on":[116,156],"key":[117],"features":[118,145],"that":[119,167],"affect":[120],"forecasting":[121],"accuracy.":[122],"concatenation":[124],"prior":[127],"can":[130],"mitigate":[131],"typical":[133],"error":[134],"accumulation":[135],"problem":[136],"traditional":[139],"decomposition-reconstruction":[140],"frameworks.":[141],"Finally,":[142],"attention-refined":[144],"fed":[147],"into":[148],"Informer":[150],"generate":[153],"predictions.":[154],"Experiments":[155],"four":[157],"major":[158],"(SSEC,":[160],"SZI,":[161],"STI,":[162],"S&P":[164],"500)":[165],"demonstrate":[166],"proposed":[169],"outperforms":[171],"state-of-the-art":[172],"deep":[173],"learning":[174],"benchmarks,":[175],"achieving":[176],"RMSE":[177],"reductions":[178],"17.6":[180],"\u2013":[181],"32.5%":[182],"across":[183],"all":[184],"tested":[185],"indices.":[187],"In":[188],"addition,":[189],"ablation":[191],"verified":[193],"effectiveness":[195],"each":[197],"module.":[198]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}