{"id":"https://openalex.org/W4416250086","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228892","title":"Adaptive GCRA-CNN-LSTM: An Enhanced Model for Stock Price Prediction","display_name":"Adaptive GCRA-CNN-LSTM: An Enhanced Model for Stock Price Prediction","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416250086","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228892"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228892","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228892","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","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/A5088536058","display_name":"Xinpeng Xu","orcid":"https://orcid.org/0000-0001-9856-2762"},"institutions":[{"id":"https://openalex.org/I78757542","display_name":"University of Newcastle Australia","ror":"https://ror.org/00eae9z71","country_code":"AU","type":"education","lineage":["https://openalex.org/I78757542"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Xinpeng Xu","raw_affiliation_strings":["The University of Newcastle,Newcastle,Australia"],"affiliations":[{"raw_affiliation_string":"The University of Newcastle,Newcastle,Australia","institution_ids":["https://openalex.org/I78757542"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075732919","display_name":"Yukai Du","orcid":null},"institutions":[{"id":"https://openalex.org/I78757542","display_name":"University of Newcastle Australia","ror":"https://ror.org/00eae9z71","country_code":"AU","type":"education","lineage":["https://openalex.org/I78757542"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Yukai Du","raw_affiliation_strings":["The University of Newcastle,Newcastle,Australia"],"affiliations":[{"raw_affiliation_string":"The University of Newcastle,Newcastle,Australia","institution_ids":["https://openalex.org/I78757542"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5088536058"],"corresponding_institution_ids":["https://openalex.org/I78757542"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.41903172,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9764999747276306,"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":0.9764999747276306,"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":0.0019000000320374966,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.0015999999595806003,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5996999740600586},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.5403000116348267},{"id":"https://openalex.org/keywords/stock-price","display_name":"Stock price","score":0.5117999911308289},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.4677000045776367},{"id":"https://openalex.org/keywords/predictive-power","display_name":"Predictive power","score":0.34700000286102295},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.3391000032424927},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.32690000534057617}],"concepts":[{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.6291000247001648},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5996999740600586},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.558899998664856},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.5403000116348267},{"id":"https://openalex.org/C2988984586","wikidata":"https://www.wikidata.org/wiki/Q1020013","display_name":"Stock price","level":3,"score":0.5117999911308289},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.4677000045776367},{"id":"https://openalex.org/C2778136018","wikidata":"https://www.wikidata.org/wiki/Q10350689","display_name":"Predictive power","level":2,"score":0.34700000286102295},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.3391000032424927},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.32690000534057617},{"id":"https://openalex.org/C2781140086","wikidata":"https://www.wikidata.org/wiki/Q557945","display_name":"Confusion","level":2,"score":0.3255999982357025},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.3125999867916107},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3046000003814697},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3025999963283539},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2897999882698059},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C138602881","wikidata":"https://www.wikidata.org/wiki/Q2709591","display_name":"Confusion matrix","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.26440000534057617},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26100000739097595},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228892","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228892","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W876282063","https://openalex.org/W2064105265","https://openalex.org/W2209610041","https://openalex.org/W2291961022","https://openalex.org/W2424778531","https://openalex.org/W2553915786","https://openalex.org/W2587128043","https://openalex.org/W2774513877","https://openalex.org/W2913816820","https://openalex.org/W2914506313","https://openalex.org/W2953030092","https://openalex.org/W2991240488","https://openalex.org/W3015568951","https://openalex.org/W3025537884","https://openalex.org/W3040694753","https://openalex.org/W3044432665","https://openalex.org/W3048631361","https://openalex.org/W3083667980","https://openalex.org/W3088162569","https://openalex.org/W3090283437","https://openalex.org/W3105750503","https://openalex.org/W3115276530","https://openalex.org/W3154435685","https://openalex.org/W3168997536","https://openalex.org/W3178756396","https://openalex.org/W3209027345","https://openalex.org/W4220793756","https://openalex.org/W4390421853","https://openalex.org/W4393241212","https://openalex.org/W4398780590"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"stock":[1,22,59,91,191],"price":[2,92,96,192],"prediction":[3,146,198],"is":[4,184],"an":[5],"important":[6],"but":[7],"difficult":[8],"undertaking":[9],"in":[10,21,62,89,174,197],"the":[11,16,69,77,160,163],"financial":[12],"sector":[13],"because":[14,32],"to":[15,36,85,123],"complexity":[17],"and":[18,41,110,137,200],"noise":[19],"inherent":[20],"market":[23],"data.":[24,93],"Traditional":[25],"models":[26],"often":[27],"have":[28],"lower":[29],"forecast":[30],"accuracy":[31,118,199],"they":[33],"are":[34],"unable":[35],"incorporate":[37],"both":[38],"long-term":[39],"dependence":[40],"local":[42],"developments.":[43],"Based":[44],"on":[45],"a":[46,57,116,149,185],"hybrid":[47],"Convolutional":[48],"Neural":[49],"Network-Long":[50],"Short-Term":[51],"Memory":[52],"architecture":[53],"(AGCL),":[54],"we":[55],"submit":[56],"sophisticated":[58],"forecasting":[60],"model":[61,67,105],"this":[63],"paper.":[64],"The":[65,153],"AGCL":[66,143,183],"preserves":[68],"temporal":[70],"connections":[71],"necessary":[72],"for":[73,98,189],"sequential":[74],"forecasting,":[75,193],"while":[76,167],"LSTM":[78],"layers":[79],"use":[80],"CNN\u2019s":[81],"feature":[82],"extraction":[83],"capabilities":[84],"identify":[86],"spatial":[87],"links":[88],"daily":[90,95,164],"Using":[94],"movements":[97],"Apple":[99],"Inc.":[100],"(AAPL)":[101],"into":[102],"2023,":[103],"our":[104],"demonstrated":[106,171],"remarkable":[107],"precision,":[108],"recall,":[109],"F1":[111],"scores,":[112],"as":[113,115],"well":[114],"test":[117],"of":[119,162],"83.33%.":[120],"In":[121],"addition":[122],"other":[124],"assessment":[125],"measures":[126],"including":[127],"Mean":[128,133],"Squared":[129,134],"Error":[130,135],"(MSE),":[131],"Root":[132],"(RMSE),":[136],"R-squared":[138],"(R<sup":[139],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[140],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>)":[141],"score,":[142],"showed":[144],"strong":[145],"ability":[147],"via":[148],"specific":[150],"optimization":[151],"process.":[152],"model\u2019s":[154],"practical":[155],"application":[156],"was":[157],"underlined":[158],"by":[159,180],"results":[161],"trend":[165],"forecast,":[166],"confusion":[168],"matrix":[169],"analysis":[170],"its":[172],"power":[173],"lowering":[175],"false":[176],"negatives.":[177],"As":[178],"shown":[179],"these":[181],"findings,":[182],"very":[186],"successful":[187],"framework":[188],"short-term":[190],"providing":[194],"significant":[195],"gains":[196],"dependability.":[201]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}
