{"id":"https://openalex.org/W4399117116","doi":"https://doi.org/10.1145/3659211.3659336","title":"Quantitative Analysis of A-share Historical Data Based on RF-LSTM","display_name":"Quantitative Analysis of A-share Historical Data Based on RF-LSTM","publication_year":2023,"publication_date":"2023-12-08","ids":{"openalex":"https://openalex.org/W4399117116","doi":"https://doi.org/10.1145/3659211.3659336"},"language":"en","primary_location":{"id":"doi:10.1145/3659211.3659336","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3659211.3659336","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 4th International Conference on Big Data Economy and Information Management","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/A5075637896","display_name":"Xiaolong Chai","orcid":"https://orcid.org/0009-0002-4405-7909"},"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":"Xiaolong Chai","raw_affiliation_strings":["School of Statistics and Mathematics, Guangdong University of Finance and Economics, China"],"raw_orcid":"https://orcid.org/0009-0002-4405-7909","affiliations":[{"raw_affiliation_string":"School of Statistics and Mathematics, 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/A5035489770","display_name":"Zhaohong Chen","orcid":"https://orcid.org/0009-0004-6653-0386"},"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":"Zhaohong Chen","raw_affiliation_strings":["School of Statistics and Mathematics, Guangdong University of Finance and Economics, China"],"raw_orcid":"https://orcid.org/0009-0004-6653-0386","affiliations":[{"raw_affiliation_string":"School of Statistics and Mathematics, 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":2,"corresponding_author_ids":["https://openalex.org/A5075637896"],"corresponding_institution_ids":["https://openalex.org/I201459984","https://openalex.org/I4210149618"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.2768917,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"726","last_page":"730"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9983999729156494,"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":0.9983999729156494,"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":0.9787999987602234,"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":0.9728000164031982,"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/random-forest","display_name":"Random forest","score":0.8030852675437927},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6509451866149902},{"id":"https://openalex.org/keywords/personalization","display_name":"Personalization","score":0.582878053188324},{"id":"https://openalex.org/keywords/radio-frequency","display_name":"Radio frequency","score":0.5146445035934448},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4670468866825104},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4488884210586548},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.44466108083724976},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4334322214126587},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4236499071121216},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38024619221687317},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.16978085041046143},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.14360976219177246},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.0784527063369751}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.8030852675437927},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6509451866149902},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.582878053188324},{"id":"https://openalex.org/C74064498","wikidata":"https://www.wikidata.org/wiki/Q3396184","display_name":"Radio frequency","level":2,"score":0.5146445035934448},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4670468866825104},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4488884210586548},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.44466108083724976},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4334322214126587},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4236499071121216},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38024619221687317},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.16978085041046143},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.14360976219177246},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0784527063369751},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3659211.3659336","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3659211.3659336","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 4th International Conference on Big Data Economy and Information Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W2029056003","https://openalex.org/W2209610041","https://openalex.org/W2912036663","https://openalex.org/W3123671626","https://openalex.org/W6649284764","https://openalex.org/W6819279986"],"related_works":["https://openalex.org/W2109940557","https://openalex.org/W2466832359","https://openalex.org/W3193043704","https://openalex.org/W4386259002","https://openalex.org/W1546989560","https://openalex.org/W3171520305","https://openalex.org/W4391210591","https://openalex.org/W1582019636","https://openalex.org/W3161989282","https://openalex.org/W3128438030"],"abstract_inverted_index":{"Based":[0],"on":[1],"the":[2,21,30,34,38,49,52,69,76,80,92,105,109,117],"analysis":[3],"of":[4,10,51,79,87,97,108],"historical":[5],"data":[6],"and":[7,27,66,94],"customized":[8,114],"indicators":[9,31],"A-share":[11],"stocks,":[12],"this":[13,55,88],"article":[14,89],"establishes":[15],"a":[16,58,64],"Random":[17],"Forest":[18],"model,":[19,54,111],"uses":[20],"RF":[22,35],"model":[23,36,40,62,71],"to":[24,41,47],"screen":[25],"indicators,":[26],"then":[28],"passes":[29],"selected":[32],"by":[33],"into":[37],"RF-LSTM":[39,53,70,110],"achieve":[42],"prediction":[43,77,106],"function.":[44],"In":[45],"order":[46],"demonstrate":[48],"applicability":[50],"paper":[56],"introduces":[57],"custom":[59],"AR-RF":[60],"integrated":[61],"as":[63],"comparison":[65],"finds":[67],"that":[68],"has":[72],"better":[73],"performance":[74],"in":[75,91],"results":[78,107],"final":[81],"test":[82],"set.":[83],"The":[84],"main":[85],"innovation":[86],"lies":[90],"customization":[93],"secondary":[95],"quantification":[96],"individual":[98],"stock":[99],"support":[100],"pressure":[101],"indicators.":[102],"After":[103],"obtaining":[104],"combined":[112],"with":[113],"formulas,":[115],"quantify":[116],"future":[118],"trend.":[119]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
