{"id":"https://openalex.org/W4400947855","doi":"https://doi.org/10.1145/3672919.3673004","title":"TLACP: A Hybrid Deep Learning Model for Stock Market Prediction","display_name":"TLACP: A Hybrid Deep Learning Model for Stock Market Prediction","publication_year":2024,"publication_date":"2024-03-01","ids":{"openalex":"https://openalex.org/W4400947855","doi":"https://doi.org/10.1145/3672919.3673004"},"language":"en","primary_location":{"id":"doi:10.1145/3672919.3673004","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3672919.3673004","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 3rd International Conference on Cyber Security, Artificial Intelligence and Digital Economy","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":null,"display_name":"Zechen Li","orcid":"https://orcid.org/0009-0004-4080-4908"},"institutions":[{"id":"https://openalex.org/I146773158","display_name":"Qingdao Agricultural University","ror":"https://ror.org/051qwcj72","country_code":"CN","type":"education","lineage":["https://openalex.org/I146773158"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zechen Li","raw_affiliation_strings":["Qingdao Agricultural University, China"],"raw_orcid":"https://orcid.org/0009-0004-4080-4908","affiliations":[{"raw_affiliation_string":"Qingdao Agricultural University, China","institution_ids":["https://openalex.org/I146773158"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I146773158"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13631732,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"469","last_page":"479"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9997000098228455,"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":0.9997000098228455,"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":0.983299970626831,"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":0.9801999926567078,"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":0.5971888303756714},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.5646874308586121},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5352671146392822},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5220789313316345},{"id":"https://openalex.org/keywords/stock-market-prediction","display_name":"Stock market prediction","score":0.49256497621536255},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4245946407318115},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.4112775921821594},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08853352069854736}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5971888303756714},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.5646874308586121},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5352671146392822},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5220789313316345},{"id":"https://openalex.org/C2776256503","wikidata":"https://www.wikidata.org/wiki/Q7617906","display_name":"Stock market prediction","level":4,"score":0.49256497621536255},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4245946407318115},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.4112775921821594},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08853352069854736},{"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},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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:10.1145/3672919.3673004","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3672919.3673004","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 3rd International Conference on Cyber Security, Artificial Intelligence and Digital Economy","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":8,"referenced_works":["https://openalex.org/W1981276685","https://openalex.org/W2194775991","https://openalex.org/W3031376202","https://openalex.org/W4205404495","https://openalex.org/W4229980196","https://openalex.org/W4236773035","https://openalex.org/W4248502591","https://openalex.org/W4298316462"],"related_works":["https://openalex.org/W4380075502","https://openalex.org/W2586556113","https://openalex.org/W2370669686","https://openalex.org/W3135178882","https://openalex.org/W2542516223","https://openalex.org/W2624043242","https://openalex.org/W3209908847","https://openalex.org/W2110351804","https://openalex.org/W3023530306","https://openalex.org/W2086993069"],"abstract_inverted_index":{"Complexity,":[0],"non-linearity,":[1],"and":[2,16,22,50,79,93,106,126,148],"extreme":[3],"volatility":[4],"make":[5],"stock":[6,52,63,143],"market":[7,53,64],"prediction":[8],"difficult.":[9],"Using":[10],"traditional":[11],"methods":[12],"like":[13,102],"SVR,":[14],"MLP,":[15],"LSTM":[17],"for":[18,28],"time":[19],"series":[20],"analysis":[21],"machine":[23],"learning":[24,42,136],"can":[25,138],"be":[26],"challenging":[27],"handling":[29,61],"high-dimensional,":[30],"non-linear":[31],"data.":[32],"The":[33,55],"Transformer-LSTM-Attention-CNN":[34],"with":[35,114,145],"Learnable":[36],"Positional":[37],"Embedding":[38],"(TLACP)":[39],"hybrid":[40,134],"deep":[41,135],"model":[43,57,96,123,137],"was":[44,89],"proposed":[45],"to":[46,91,97],"address":[47],"these":[48],"challenges":[49],"improve":[51],"predictions.":[54],"TLACP":[56,116,122,133],"enhances":[58],"performance":[59,110],"in":[60,111],"complex":[62],"data":[65,74,88],"by":[66],"combining":[67],"Transformers'":[68],"global":[69],"attention":[70],"mechanism,":[71],"LSTM's":[72],"temporal":[73],"processing,":[75],"CNN's":[76],"feature":[77],"extraction,":[78],"learnable":[80],"positional":[81],"embedding":[82],"techniques.":[83],"Daily":[84],"Chinese":[85],"A-share":[86],"trade":[87],"used":[90],"test":[92],"compare":[94],"the":[95,115,132],"standard":[98],"models.":[99],"Statistical":[100],"metrics":[101],"RMSE,":[103],"MAE,":[104],"MAPE,":[105],"R\u00b2":[107],"showed":[108],"superior":[109],"all":[112],"experiments":[113,119],"model.":[117],"Ablation":[118],"verified":[120],"each":[121],"component's":[124],"efficacy":[125],"requirement.":[127],"This":[128],"study":[129],"shows":[130],"that":[131],"forecast":[139],"financial":[140],"data,":[141],"providing":[142],"finance":[144],"fresh":[146],"insights":[147],"solutions.":[149]},"counts_by_year":[],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}