{"id":"https://openalex.org/W2782626305","doi":"https://doi.org/10.1109/bigdata.2017.8258441","title":"An enhanced LGSA-SVM for S&P 500 index forecast","display_name":"An enhanced LGSA-SVM for S&P 500 index forecast","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2782626305","doi":"https://doi.org/10.1109/bigdata.2017.8258441","mag":"2782626305"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2017.8258441","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8258441","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Big Data (Big Data)","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/A5100376129","display_name":"Jinxin Wang","orcid":"https://orcid.org/0000-0003-4205-7673"},"institutions":[{"id":"https://openalex.org/I4210120485","display_name":"Academy of Mathematics and Systems Science","ror":"https://ror.org/02jkmyk67","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210120485"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jinxin Wang","raw_affiliation_strings":["Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210120485","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115604897","display_name":"Wei Shang","orcid":"https://orcid.org/0000-0001-6039-6041"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210120485","display_name":"Academy of Mathematics and Systems Science","ror":"https://ror.org/02jkmyk67","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210120485"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Shang","raw_affiliation_strings":["Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210120485","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101597240","display_name":"Zhengyang Liu","orcid":"https://orcid.org/0000-0002-9858-108X"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210120485","display_name":"Academy of Mathematics and Systems Science","ror":"https://ror.org/02jkmyk67","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210120485"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhengyang Liu","raw_affiliation_strings":["Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210120485","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078558986","display_name":"Shouyang Wang","orcid":"https://orcid.org/0000-0001-5773-998X"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210120485","display_name":"Academy of Mathematics and Systems Science","ror":"https://ror.org/02jkmyk67","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210120485"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shouyang Wang","raw_affiliation_strings":["Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210120485","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100376129"],"corresponding_institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210120485"],"apc_list":null,"apc_paid":null,"fwci":0.5167,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.73090255,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"3","issue":null,"first_page":"4176","last_page":"4183"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9991000294685364,"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":0.9991000294685364,"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":0.9836999773979187,"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"}},{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9836000204086304,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.8584856390953064},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7798784971237183},{"id":"https://openalex.org/keywords/index","display_name":"Index (typography)","score":0.7326067686080933},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6342278718948364},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6111458539962769},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5529648065567017},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4844512939453125},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.47487518191337585},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4609847962856293},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4152772128582001},{"id":"https://openalex.org/keywords/financial-market","display_name":"Financial market","score":0.41307711601257324},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.24568423628807068},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.1257888376712799}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.8584856390953064},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7798784971237183},{"id":"https://openalex.org/C2777382242","wikidata":"https://www.wikidata.org/wiki/Q6017816","display_name":"Index (typography)","level":2,"score":0.7326067686080933},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6342278718948364},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6111458539962769},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5529648065567017},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4844512939453125},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.47487518191337585},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4609847962856293},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4152772128582001},{"id":"https://openalex.org/C19244329","wikidata":"https://www.wikidata.org/wiki/Q208697","display_name":"Financial market","level":2,"score":0.41307711601257324},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.24568423628807068},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.1257888376712799},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2017.8258441","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8258441","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320336876","display_name":"National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences","ror":"https://ror.org/00s97k668"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W181010784","https://openalex.org/W1965806369","https://openalex.org/W1970094400","https://openalex.org/W1976306760","https://openalex.org/W1978775764","https://openalex.org/W1980836123","https://openalex.org/W1988518729","https://openalex.org/W1988610730","https://openalex.org/W2000303290","https://openalex.org/W2005424446","https://openalex.org/W2008953851","https://openalex.org/W2012079387","https://openalex.org/W2024804972","https://openalex.org/W2030005958","https://openalex.org/W2055631528","https://openalex.org/W2068527816","https://openalex.org/W2070181657","https://openalex.org/W2072955302","https://openalex.org/W2082754320","https://openalex.org/W2099124121","https://openalex.org/W2128281627","https://openalex.org/W2163828179","https://openalex.org/W2164401990","https://openalex.org/W2270937275","https://openalex.org/W2560568795","https://openalex.org/W2625540161","https://openalex.org/W3121163171","https://openalex.org/W3124185353","https://openalex.org/W3214074647","https://openalex.org/W4238301492"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W972276598","https://openalex.org/W4246352526","https://openalex.org/W2121910908","https://openalex.org/W915438175"],"abstract_inverted_index":{"The":[0,64],"S&P":[1,28,59],"500":[2,29,60],"index":[3,61],"is":[4,13,20],"an":[5,40],"important":[6],"representative":[7],"of":[8,27,54,70],"worlds'":[9],"financial":[10,90],"market":[11],"and":[12,51,77],"influenced":[14],"by":[15],"various":[16],"economic":[17],"factors.":[18],"There":[19],"a":[21],"call":[22],"for":[23,57,88],"automatically":[24],"select":[25],"antecedents":[26],"index's":[30],"change":[31],"in":[32],"the":[33,48,58,68,81],"fast-changing":[34],"world":[35],"economy.":[36],"This":[37],"paper":[38],"proposes":[39],"enhanced":[41],"GSA":[42],"model":[43,72],"named":[44],"LGSA":[45],"to":[46],"solve":[47],"feature":[49],"selection":[50],"parameter":[52],"optimization":[53],"SVM":[55],"models":[56],"movement":[62],"prediction.":[63],"results":[65],"show":[66],"that":[67],"accuracy":[69],"LGSA-SVM":[71],"surpasses":[73],"benchmark":[74],"SVM,":[75],"PSO-SVM":[76],"GA-SVM":[78],"model.":[79],"And":[80],"proposed":[82],"approach":[83],"could":[84],"hopefully":[85],"be":[86],"adopted":[87],"other":[89],"data":[91],"series":[92],"automatic":[93],"forecasting.":[94]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}