{"id":"https://openalex.org/W4376852283","doi":"https://doi.org/10.1145/3573942.3574019","title":"Attention-based BiLSTM model for stock price prediction","display_name":"Attention-based BiLSTM model for stock price prediction","publication_year":2022,"publication_date":"2022-09-23","ids":{"openalex":"https://openalex.org/W4376852283","doi":"https://doi.org/10.1145/3573942.3574019"},"language":"en","primary_location":{"id":"doi:10.1145/3573942.3574019","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3573942.3574019","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","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/A5042516078","display_name":"Weiran Liu","orcid":"https://orcid.org/0000-0002-5606-173X"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Weiran Liu","raw_affiliation_strings":["School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0002-5606-173X","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100343319","display_name":"Yong Zhang","orcid":"https://orcid.org/0000-0001-9253-4330"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Zhang","raw_affiliation_strings":["School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0001-9253-4330","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"last","author":{"id":null,"display_name":"Yumei Liu","orcid":"https://orcid.org/0000-0002-1049-963X"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yumei Liu","raw_affiliation_strings":["School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0002-1049-963X","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5042516078"],"corresponding_institution_ids":["https://openalex.org/I4210136859"],"apc_list":null,"apc_paid":null,"fwci":0.678,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.74113977,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"257","last_page":"263"},"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":0.9954000115394592,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9904000163078308,"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":0.7287271618843079},{"id":"https://openalex.org/keywords/stock-price","display_name":"Stock price","score":0.7067471742630005},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.604036271572113},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5980866551399231},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5427653193473816},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.5331515669822693},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.46587416529655457},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45931321382522583},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4498792886734009},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4483920633792877},{"id":"https://openalex.org/keywords/mean-squared-prediction-error","display_name":"Mean squared prediction error","score":0.42827731370925903},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.42580434679985046},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.17570003867149353},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.129208505153656},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.08823919296264648}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7287271618843079},{"id":"https://openalex.org/C2988984586","wikidata":"https://www.wikidata.org/wiki/Q1020013","display_name":"Stock price","level":3,"score":0.7067471742630005},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.604036271572113},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5980866551399231},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5427653193473816},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.5331515669822693},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.46587416529655457},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45931321382522583},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4498792886734009},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4483920633792877},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.42827731370925903},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42580434679985046},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.17570003867149353},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.129208505153656},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.08823919296264648},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"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:10.1145/3573942.3574019","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3573942.3574019","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","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":9,"referenced_works":["https://openalex.org/W2025053102","https://openalex.org/W2040395995","https://openalex.org/W2064675550","https://openalex.org/W2172064003","https://openalex.org/W2342352817","https://openalex.org/W2602977295","https://openalex.org/W2624385633","https://openalex.org/W2909877301","https://openalex.org/W4205851139"],"related_works":["https://openalex.org/W155406958","https://openalex.org/W2811187992","https://openalex.org/W803509314","https://openalex.org/W2132491819","https://openalex.org/W3042921537","https://openalex.org/W4360615906","https://openalex.org/W4288388567","https://openalex.org/W2359189099","https://openalex.org/W335743615","https://openalex.org/W4282583532"],"abstract_inverted_index":{"Abstract:":[0],"Stock":[1],"price":[2,23,74,151],"prediction":[3,75,102,124,130,134,140],"is":[4,81],"a":[5,69],"hot":[6],"issue":[7],"in":[8,54,117],"the":[9,18,41,63,88,95,101,105,109,114,118,126,138],"field":[10],"of":[11,21,43,56,66],"quantitative":[12],"finance.":[13],"Investors":[14],"hope":[15],"to":[16,35,111],"discover":[17],"objective":[19],"laws":[20],"stock":[22,67,73,119,150],"fluctuations":[24],"from":[25],"historical":[26],"data,":[27,68],"optimize":[28],"their":[29],"investment":[30,38],"strategies,":[31],"and":[32,104,132,136],"avoid":[33],"risks":[34],"obtain":[36],"better":[37,112],"returns.":[39],"With":[40],"development":[42],"deep":[44],"learning":[45],"technology,":[46],"neural":[47],"networks":[48],"have":[49],"shown":[50],"good":[51],"forecasting":[52],"effects":[53],"task":[55],"time":[57],"series":[58],"data":[59,98],"forecasting.":[60],"Aiming":[61],"at":[62],"temporal":[64],"correlation":[65,96],"bidirectional":[70,89],"LSTM":[71,90],"network":[72,91],"model":[76,110,127],"fused":[77],"with":[78,122],"attention":[79,106],"mechanism":[80],"proposed.":[82],"The":[83],"experimental":[84],"results":[85],"show":[86],"that":[87],"can":[92,146],"effectively":[93],"learn":[94],"between":[97],"when":[99],"performing":[100],"task,":[103],"module":[107],"helps":[108],"capture":[113],"key":[115],"information":[116],"data.":[120],"Compared":[121],"other":[123],"networks,":[125],"has":[128],"higher":[129],"accuracy":[131],"lower":[133],"error,":[135],"achieves":[137],"best":[139],"performance":[141],"on":[142],"different":[143],"datasets,":[144],"which":[145],"provide":[147],"help":[148],"for":[149],"prediction.":[152]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
