{"id":"https://openalex.org/W4386521109","doi":"https://doi.org/10.1145/3587716.3587813","title":"A CNN-Relstm model based on a hybrid architecture of unidirectional and bidirectional LSTM for predicting stock prices","display_name":"A CNN-Relstm model based on a hybrid architecture of unidirectional and bidirectional LSTM for predicting stock prices","publication_year":2023,"publication_date":"2023-02-17","ids":{"openalex":"https://openalex.org/W4386521109","doi":"https://doi.org/10.1145/3587716.3587813"},"language":"en","primary_location":{"id":"doi:10.1145/3587716.3587813","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3587716.3587813","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 15th International Conference on Machine Learning and Computing","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/A5101711011","display_name":"Jie He","orcid":"https://orcid.org/0000-0002-9433-8023"},"institutions":[{"id":"https://openalex.org/I201459984","display_name":"Guangdong University Of Finances and Economics","ror":"https://ror.org/0459pv085","country_code":"CN","type":"education","lineage":["https://openalex.org/I201459984"]},{"id":"https://openalex.org/I4210149618","display_name":"Guangdong University of Finance","ror":"https://ror.org/04grzdh47","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210149618"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jie He","raw_affiliation_strings":["Guangdong University of Finance and Economics, China"],"raw_orcid":"https://orcid.org/0000-0002-9433-8023","affiliations":[{"raw_affiliation_string":"Guangdong University of Finance and Economics, China","institution_ids":["https://openalex.org/I201459984","https://openalex.org/I4210149618"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007111614","display_name":"Bo Xu","orcid":"https://orcid.org/0000-0003-4763-5321"},"institutions":[{"id":"https://openalex.org/I201459984","display_name":"Guangdong University Of Finances and Economics","ror":"https://ror.org/0459pv085","country_code":"CN","type":"education","lineage":["https://openalex.org/I201459984"]},{"id":"https://openalex.org/I4210149618","display_name":"Guangdong University of Finance","ror":"https://ror.org/04grzdh47","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210149618"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Xu","raw_affiliation_strings":["Guangdong University of Finance and Economics, China"],"raw_orcid":"https://orcid.org/0000-0003-4763-5321","affiliations":[{"raw_affiliation_string":"Guangdong University of Finance and Economics, China","institution_ids":["https://openalex.org/I201459984","https://openalex.org/I4210149618"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090868680","display_name":"Xinpu Su","orcid":"https://orcid.org/0000-0003-2473-0393"},"institutions":[{"id":"https://openalex.org/I201459984","display_name":"Guangdong University Of Finances and Economics","ror":"https://ror.org/0459pv085","country_code":"CN","type":"education","lineage":["https://openalex.org/I201459984"]},{"id":"https://openalex.org/I4210149618","display_name":"Guangdong University of Finance","ror":"https://ror.org/04grzdh47","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210149618"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinpu Su","raw_affiliation_strings":["Guangdong University of Finance and Economics, China"],"raw_orcid":"https://orcid.org/0000-0003-2473-0393","affiliations":[{"raw_affiliation_string":"Guangdong University of Finance and Economics, China","institution_ids":["https://openalex.org/I201459984","https://openalex.org/I4210149618"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011433804","display_name":"Cuier Tan","orcid":"https://orcid.org/0000-0002-4737-6246"},"institutions":[{"id":"https://openalex.org/I201459984","display_name":"Guangdong University Of Finances and Economics","ror":"https://ror.org/0459pv085","country_code":"CN","type":"education","lineage":["https://openalex.org/I201459984"]},{"id":"https://openalex.org/I4210149618","display_name":"Guangdong University of Finance","ror":"https://ror.org/04grzdh47","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210149618"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cuier Tan","raw_affiliation_strings":["Guangdong University of Finance and Economics, China"],"raw_orcid":"https://orcid.org/0000-0002-4737-6246","affiliations":[{"raw_affiliation_string":"Guangdong University of Finance and Economics, China","institution_ids":["https://openalex.org/I201459984","https://openalex.org/I4210149618"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101711011"],"corresponding_institution_ids":["https://openalex.org/I201459984","https://openalex.org/I4210149618"],"apc_list":null,"apc_paid":null,"fwci":0.4931,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.68792317,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"589","last_page":"596"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9955000281333923,"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"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9902999997138977,"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/computer-science","display_name":"Computer science","score":0.7561917901039124},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.6893630027770996},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5377538204193115},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5192368626594543},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.45254844427108765},{"id":"https://openalex.org/keywords/composite-index","display_name":"Composite index","score":0.42471373081207275},{"id":"https://openalex.org/keywords/stock-market-index","display_name":"Stock market index","score":0.41623297333717346},{"id":"https://openalex.org/keywords/memory-model","display_name":"Memory model","score":0.4141438603401184},{"id":"https://openalex.org/keywords/volatility","display_name":"Volatility (finance)","score":0.41265636682510376},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40465471148490906},{"id":"https://openalex.org/keywords/stock-exchange","display_name":"Stock exchange","score":0.3839108347892761},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3063480854034424},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.26162707805633545},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.15801173448562622},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10210847854614258},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.0714120864868164},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.06932961940765381}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7561917901039124},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.6893630027770996},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5377538204193115},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5192368626594543},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.45254844427108765},{"id":"https://openalex.org/C2778098375","wikidata":"https://www.wikidata.org/wiki/Q19596433","display_name":"Composite index","level":3,"score":0.42471373081207275},{"id":"https://openalex.org/C88389905","wikidata":"https://www.wikidata.org/wiki/Q223371","display_name":"Stock market index","level":4,"score":0.41623297333717346},{"id":"https://openalex.org/C12186640","wikidata":"https://www.wikidata.org/wiki/Q6815743","display_name":"Memory model","level":3,"score":0.4141438603401184},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.41265636682510376},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40465471148490906},{"id":"https://openalex.org/C200870193","wikidata":"https://www.wikidata.org/wiki/Q11691","display_name":"Stock exchange","level":2,"score":0.3839108347892761},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3063480854034424},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.26162707805633545},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.15801173448562622},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10210847854614258},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.0714120864868164},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.06932961940765381},{"id":"https://openalex.org/C133875982","wikidata":"https://www.wikidata.org/wiki/Q764810","display_name":"Shared memory","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C2780762169","wikidata":"https://www.wikidata.org/wiki/Q5905368","display_name":"Horse","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3587716.3587813","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3587716.3587813","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 15th International Conference on Machine Learning and Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.47999998927116394}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2064675550","https://openalex.org/W2079735306","https://openalex.org/W2112796928","https://openalex.org/W2172073485","https://openalex.org/W2603235440","https://openalex.org/W2922995703","https://openalex.org/W2958225319","https://openalex.org/W2965017818","https://openalex.org/W2971724044","https://openalex.org/W2990597424","https://openalex.org/W3007075806","https://openalex.org/W3024761859","https://openalex.org/W3041279471","https://openalex.org/W3048911119","https://openalex.org/W3110378470","https://openalex.org/W3117894835","https://openalex.org/W3130456109","https://openalex.org/W3179758271","https://openalex.org/W3201419990","https://openalex.org/W6797963707"],"related_works":["https://openalex.org/W2391550732","https://openalex.org/W2057927238","https://openalex.org/W2905338803","https://openalex.org/W3123252458","https://openalex.org/W4389200257","https://openalex.org/W4386639254","https://openalex.org/W1971081312","https://openalex.org/W2992367150","https://openalex.org/W3010928425","https://openalex.org/W2969940294"],"abstract_inverted_index":{"Due":[0],"to":[1,17,35,75,110,120],"the":[2,25,36,50,69,80,84,91,98,113,117,130,133,141,149,155,161,165],"strong":[3],"noise,":[4],"nonlinearity":[5],"and":[6,32,54,61,104,116,124,137],"volatility":[7],"of":[8,39,49,79,164],"time":[9],"series":[10],"data":[11],"for":[12],"stock":[13,22,166],"prices,":[14],"in":[15,21,160],"order":[16],"provide":[18],"better":[19,153],"results":[20,146],"price":[23],"forecasting,":[24],"paper":[26],"brings":[27],"up":[28],"a":[29,55],"CNN-Relstm":[30],"model":[31,47,122,131,151],"applies":[33],"it":[34],"prediction":[37,163],"research":[38],"SSE":[40,142],"(Shanghai":[41],"Stock":[42],"Exchange)":[43],"Composite":[44,143],"Index.":[45,144],"The":[46],"consists":[48],"convolutional":[51,70],"neural":[52,66,71],"network":[53,72],"hybrid":[56],"architecture":[57],"based":[58],"on":[59,140],"bidirectional":[60,99],"unidirectional":[62,92],"long":[63,93,100],"short-term":[64,94,101],"memory":[65,95,102],"networks.":[67],"First,":[68],"is":[73],"used":[74,109],"extract":[76],"valid":[77],"features":[78,86],"input":[81],"data.":[82],"Then":[83],"temporal":[85],"are":[87,108],"extracted":[88],"by":[89],"combining":[90],"layer":[96],"with":[97],"layer,":[103],"two":[105],"callback":[106],"functions":[107],"adaptively":[111],"adjust":[112],"learning":[114],"rate":[115],"training":[118],"epochs":[119],"promote":[121],"convergence":[123],"prevent":[125],"overfitting.":[126],"Finally,":[127],"we":[128],"validated":[129],"through":[132],"parameter":[134],"tuning":[135],"experiments":[136,139],"comparison":[138,158],"Experimental":[145],"showed":[147],"that":[148],"proposed":[150],"performs":[152],"than":[154],"remaining":[156],"five":[157],"models":[159],"time-series":[162],"price.":[167]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}