{"id":"https://openalex.org/W4403918650","doi":"https://doi.org/10.1109/ic3ina64086.2024.10732245","title":"Predicting Stock Market using CNN and BiLSTM Model","display_name":"Predicting Stock Market using CNN and BiLSTM Model","publication_year":2024,"publication_date":"2024-10-09","ids":{"openalex":"https://openalex.org/W4403918650","doi":"https://doi.org/10.1109/ic3ina64086.2024.10732245"},"language":"en","primary_location":{"id":"doi:10.1109/ic3ina64086.2024.10732245","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ic3ina64086.2024.10732245","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Conference on Computer, Control, Informatics and its Applications (IC3INA)","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/A5093648953","display_name":"Mohammad Tyas Pawitra","orcid":null},"institutions":[{"id":"https://openalex.org/I862893732","display_name":"Telkom University","ror":"https://ror.org/0004wsx81","country_code":"ID","type":"education","lineage":["https://openalex.org/I862893732"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Mohammad Tyas Pawitra","raw_affiliation_strings":["Telkom University,School of Industrial Engineering,Bandung,Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Telkom University,School of Industrial Engineering,Bandung,Indonesia","institution_ids":["https://openalex.org/I862893732"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004438607","display_name":"Hanif Fakhrurroja","orcid":"https://orcid.org/0000-0002-2483-251X"},"institutions":[{"id":"https://openalex.org/I4387154144","display_name":"National Research and Innovation Agency","ror":"https://ror.org/02hmjzt55","country_code":"ID","type":"government","lineage":["https://openalex.org/I4387154144"]},{"id":"https://openalex.org/I862893732","display_name":"Telkom University","ror":"https://ror.org/0004wsx81","country_code":"ID","type":"education","lineage":["https://openalex.org/I862893732"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Hanif Fakhrurroja","raw_affiliation_strings":["Telkom University National Research and Innovation Agency,School of Industrial Engineering,Bandung,Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Telkom University National Research and Innovation Agency,School of Industrial Engineering,Bandung,Indonesia","institution_ids":["https://openalex.org/I4387154144","https://openalex.org/I862893732"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050197264","display_name":"Lukman Abdurrahman","orcid":"https://orcid.org/0000-0001-7288-0455"},"institutions":[{"id":"https://openalex.org/I862893732","display_name":"Telkom University","ror":"https://ror.org/0004wsx81","country_code":"ID","type":"education","lineage":["https://openalex.org/I862893732"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Lukman Abdurrahman","raw_affiliation_strings":["Telkom University,School of Industrial Engineering,Bandung,Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Telkom University,School of Industrial Engineering,Bandung,Indonesia","institution_ids":["https://openalex.org/I862893732"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.0687,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.95097805,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"267","last_page":"272"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.8884000182151794,"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":0.8884000182151794,"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/computer-science","display_name":"Computer science","score":0.6027486324310303},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5430346131324768},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.515914797782898},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.43218907713890076},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3252149224281311},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08768782019615173},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.06289836764335632}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6027486324310303},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5430346131324768},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.515914797782898},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.43218907713890076},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3252149224281311},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08768782019615173},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.06289836764335632},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ic3ina64086.2024.10732245","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ic3ina64086.2024.10732245","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Conference on Computer, Control, Informatics and its Applications (IC3INA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","score":0.41999998688697815,"display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2890684297","https://openalex.org/W2956992481","https://openalex.org/W2994069988","https://openalex.org/W3015644358","https://openalex.org/W3017051726","https://openalex.org/W3105750503","https://openalex.org/W3123007418","https://openalex.org/W3130456109","https://openalex.org/W3155398915","https://openalex.org/W3162612648","https://openalex.org/W3179758271","https://openalex.org/W4296753750","https://openalex.org/W4401718527"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"This":[0],"study":[1],"explores":[2],"the":[3,40,70,87,96,100,124,146,152,187,212,238,242,250,260],"efficacy":[4],"of":[5,39,82,240,244,262],"two":[6],"deep":[7,16],"learning":[8,17,156],"models,":[9],"Convolutional":[10],"Neural":[11],"Network":[12],"(CNN)":[13],"is":[14,35],"a":[15,79,128,201],"model":[18,105,126,184],"primarily":[19],"designed":[20,46],"for":[21,135,232],"recognizing":[22],"patterns":[23,223],"in":[24,51,217,249],"data":[25,50,59,88],"through":[26],"convolutional":[27,109],"layers.":[28,122],"and":[29,74,99,114,149,179,196,200,221,264],"Bidirectional":[30],"Long":[31,41],"Short-Term":[32,42],"Memory":[33,43],"(BiLSTM)":[34],"an":[36,92],"advanced":[37],"variant":[38],"(LSTM)":[44],"network,":[45],"to":[47,66,116],"handle":[48],"sequential":[49],"predicting":[52],"stock":[53,57,225,245],"prices.":[54],"Utilizing":[55],"historical":[56],"price":[58,226,246],"from":[60],"NVIDIA":[61],"spanning":[62],"January":[63],"1,":[64],"2013,":[65],"December":[67],"31,":[68],"2023,":[69],"dataset":[71],"was":[72,89,160],"normalized":[73],"structured":[75],"into":[76,254],"sequences":[77],"with":[78,111,132],"time":[80,233],"step":[81],"10":[83],"days.":[84],"After":[85],"that,":[86],"divided":[90],"using":[91,151,162],"80/20":[93],"split":[94],"between":[95],"training":[97],"set":[98],"testing":[101],"set.":[102],"The":[103,182],"CNN":[104,188,263],"architecture":[106],"included":[107],"multiple":[108],"layers":[110,134],"batch":[112],"normalization":[113],"dropout":[115],"prevent":[117],"overfitting,":[118],"followed":[119],"by":[120],"dense":[121,133],"Conversely,":[123],"BiLSTM":[125,183,213],"comprised":[127],"bidirectional":[129],"LSTM":[130],"layer":[131],"output.":[136],"Both":[137],"models":[138,257],"were":[139],"use":[140],"Mean":[141,167,171,175],"Squared":[142,168,176],"Error":[143,169,173,177],"(MSE)":[144],"as":[145],"loss":[147],"function":[148],"trained":[150],"Adam":[153],"optimizer":[154],"0.001":[155],"rate.":[157],"Model":[158],"performance":[159],"evaluated":[161],"evaluation":[163],"metrics":[164],"including":[165],"Root":[166],"(RMSE),":[170],"Absolute":[172],"(MAE),":[174],"(MSE),":[178],"R-squared":[180],"(R2).":[181],"significantly":[185],"outperformed":[186],"model,":[189],"achieving":[190],"lower":[191],"MAE":[192],"(1.675),":[193],"RMSE":[194],"(2.237),":[195],"MSE":[197],"(5.008)":[198],"values,":[199],"higher":[202],"R2value":[203],"(0.963),":[204],"indicating":[205],"superior":[206],"predictive":[207],"accuracy.":[208],"These":[209],"findings":[210],"underscore":[211],"model's":[214],"enhanced":[215],"capability":[216],"capturing":[218],"temporal":[219],"dependencies":[220],"intricate":[222],"within":[224],"data,":[227],"suggesting":[228],"its":[229],"greater":[230],"effectiveness":[231],"series":[234],"forecasting":[235],"tasks.":[236],"For":[237],"purpose":[239],"improving":[241],"result":[243],"predictions,":[247],"studies":[248],"future":[251],"should":[252],"take":[253],"consideration":[255],"hybrid":[256],"that":[258],"combine":[259],"advantages":[261],"BiLSTM.":[265]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
