{"id":"https://openalex.org/W3171925258","doi":"https://doi.org/10.1002/int.22495","title":"Hybrid evolutionary intelligent system and hybrid time series econometric model for stock price forecasting","display_name":"Hybrid evolutionary intelligent system and hybrid time series econometric model for stock price forecasting","publication_year":2021,"publication_date":"2021-05-29","ids":{"openalex":"https://openalex.org/W3171925258","doi":"https://doi.org/10.1002/int.22495","mag":"3171925258"},"language":"en","primary_location":{"id":"doi:10.1002/int.22495","doi":"https://doi.org/10.1002/int.22495","is_oa":false,"landing_page_url":"https://doi.org/10.1002/int.22495","pdf_url":null,"source":{"id":"https://openalex.org/S57950554","display_name":"International Journal of Intelligent Systems","issn_l":"0884-8173","issn":["0884-8173","1098-111X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","ccf-c","cwts-core","dongbi-c","jufo-1","norway-1","poland-100","russia-white-list-1"],"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Intelligent Systems","raw_type":"journal-article"},"volume":null,"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/A5017292695","display_name":"Gourav Kumar","orcid":"https://orcid.org/0000-0001-6167-9430","observed_orcids":["https://orcid.org/0000-0001-6167-9430"]},"institutions":[{"id":"https://openalex.org/I59179055","display_name":"Shri Mata Vaishno Devi University","ror":"https://ror.org/036x6w630","country_code":"IN","type":"education","lineage":["https://openalex.org/I59179055"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Gourav Kumar","raw_affiliation_strings":["Department of Computer Science & Engineering Shri Mata Vaishno Devi University Katra Jammu and Kashmir India","Department of Computer Science & Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India"],"raw_orcid":"https://orcid.org/0000-0001-6167-9430","affiliations":[{"raw_affiliation_string":"Department of Computer Science & Engineering Shri Mata Vaishno Devi University Katra Jammu and Kashmir India","institution_ids":["https://openalex.org/I59179055"]},{"raw_affiliation_string":"Department of Computer Science & Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India","institution_ids":["https://openalex.org/I59179055"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080164556","display_name":"Uday Pratap Singh","orcid":"https://orcid.org/0000-0003-2077-0793","observed_orcids":["https://orcid.org/0000-0003-2077-0793"]},"institutions":[{"id":"https://openalex.org/I59179055","display_name":"Shri Mata Vaishno Devi University","ror":"https://ror.org/036x6w630","country_code":"IN","type":"education","lineage":["https://openalex.org/I59179055"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Uday Pratap Singh","raw_affiliation_strings":["Department of Mathematics, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India"],"raw_orcid":"https://orcid.org/0000-0003-2077-0793","affiliations":[{"raw_affiliation_string":"Department of Mathematics, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India","institution_ids":["https://openalex.org/I59179055"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101467772","display_name":"Sanjeev Jain","orcid":"https://orcid.org/0009-0005-0905-6304","observed_orcids":["https://orcid.org/0009-0005-0905-6304","https://orcid.org/0000-0001-5804-6982"]},"institutions":[{"id":"https://openalex.org/I207223250","display_name":"Indian Institute of Information Technology Design and Manufacturing Jabalpur","ror":"https://ror.org/00gmd7q80","country_code":"IN","type":"education","lineage":["https://openalex.org/I207223250"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sanjeev Jain","raw_affiliation_strings":["Department of Computer Science & Engineering Indian Institute of Information Technology, Design and Manufacturing Jabalpur Madhya Pradesh India","Department of Computer Science & Engineering, Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, Madhya Pradesh, India"],"raw_orcid":"https://orcid.org/0000-0001-5804-6982","affiliations":[{"raw_affiliation_string":"Department of Computer Science & Engineering Indian Institute of Information Technology, Design and Manufacturing Jabalpur Madhya Pradesh India","institution_ids":["https://openalex.org/I207223250"]},{"raw_affiliation_string":"Department of Computer Science & Engineering, Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, Madhya Pradesh, India","institution_ids":["https://openalex.org/I207223250"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5080164556"],"corresponding_institution_ids":["https://openalex.org/I59179055"],"apc_list":{"value":3600,"currency":"USD","value_usd":3600},"apc_paid":null,"fwci":4.5564,"has_fulltext":false,"cited_by_count":44,"citation_normalized_percentile":{"value":0.94965542,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":99},"biblio":{"volume":"36","issue":"9","first_page":"4902","last_page":"4935"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":1.0,"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":1.0,"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/T11490","display_name":"Hydrological Forecasting Using AI","score":0.0,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/stock-price-prediction","display_name":"stock price prediction","score":0.996999979019165},{"id":"https://openalex.org/keywords/hybrid-intelligent-systems","display_name":"hybrid intelligent systems","score":0.8109999895095825},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"particle swarm optimization","score":0.7990000247955322},{"id":"https://openalex.org/keywords/levenberg-marquardt-algorithm","display_name":"Levenberg-Marquardt algorithm","score":0.9229999780654907},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"principal component analysis","score":0.9279999732971191},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"dimensionality reduction","score":0.8159999847412109},{"id":"https://openalex.org/keywords/feedforward-neural-networks","display_name":"feedforward neural networks","score":0.7099999785423279},{"id":"https://openalex.org/keywords/technical-indicators","display_name":"technical indicators","score":0.9100000262260437},{"id":"https://openalex.org/keywords/nifty-50","display_name":"Nifty 50","score":0.8500000238418579},{"id":"https://openalex.org/keywords/bse-sensex","display_name":"BSE Sensex","score":0.9620000123977661},{"id":"https://openalex.org/keywords/s-p-500","display_name":"S&P 500","score":0.9829999804496765},{"id":"https://openalex.org/keywords/parameter-tuning","display_name":"parameter tuning","score":0.4359999895095825}],"concepts":[{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.6541347503662109},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6501033306121826},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.6444244384765625},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6077849864959717},{"id":"https://openalex.org/C47702885","wikidata":"https://www.wikidata.org/wiki/Q5441227","display_name":"Feedforward neural network","level":3,"score":0.4631798565387726},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.4473186135292053},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44452592730522156},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.4398634433746338},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.43691131472587585},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4175838828086853},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35221680998802185},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32016438245773315}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1002/int.22495","doi":"https://doi.org/10.1002/int.22495","is_oa":false,"landing_page_url":"https://doi.org/10.1002/int.22495","pdf_url":null,"source":{"id":"https://openalex.org/S57950554","display_name":"International Journal of Intelligent Systems","issn_l":"0884-8173","issn":["0884-8173","1098-111X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","ccf-c","cwts-core","dongbi-c","jufo-1","norway-1","poland-100","russia-white-list-1"],"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Intelligent Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":60,"referenced_works":["https://openalex.org/W1534798215","https://openalex.org/W1544360792","https://openalex.org/W1595159159","https://openalex.org/W1815264562","https://openalex.org/W1966676388","https://openalex.org/W1975994995","https://openalex.org/W2003756933","https://openalex.org/W2004463884","https://openalex.org/W2038227460","https://openalex.org/W2039935421","https://openalex.org/W2041723890","https://openalex.org/W2051848379","https://openalex.org/W2059852492","https://openalex.org/W2086961759","https://openalex.org/W2093311990","https://openalex.org/W2119479037","https://openalex.org/W2124290836","https://openalex.org/W2127894084","https://openalex.org/W2152195021","https://openalex.org/W2153851210","https://openalex.org/W2167694888","https://openalex.org/W2169284845","https://openalex.org/W2235688562","https://openalex.org/W2236744271","https://openalex.org/W2281514568","https://openalex.org/W2301106258","https://openalex.org/W2336888920","https://openalex.org/W2345563409","https://openalex.org/W2501851833","https://openalex.org/W2510976010","https://openalex.org/W2523498403","https://openalex.org/W2549882901","https://openalex.org/W2565952817","https://openalex.org/W2572554044","https://openalex.org/W2592849181","https://openalex.org/W2609559763","https://openalex.org/W2728943311","https://openalex.org/W2744174391","https://openalex.org/W2791347599","https://openalex.org/W2791655422","https://openalex.org/W2802435027","https://openalex.org/W2807817605","https://openalex.org/W2808420356","https://openalex.org/W2808626748","https://openalex.org/W2900022802","https://openalex.org/W2900602028","https://openalex.org/W2931437238","https://openalex.org/W2964101383","https://openalex.org/W2974403401","https://openalex.org/W3006466875","https://openalex.org/W3011249019","https://openalex.org/W3037004355","https://openalex.org/W3063913079","https://openalex.org/W3094316335","https://openalex.org/W3135347119","https://openalex.org/W4230715394","https://openalex.org/W4241727697","https://openalex.org/W4241946070","https://openalex.org/W4242688646","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W3196957044","https://openalex.org/W3154177430","https://openalex.org/W4285277857","https://openalex.org/W1815264562","https://openalex.org/W3012260343","https://openalex.org/W2910107358","https://openalex.org/W4408626628","https://openalex.org/W2551194922","https://openalex.org/W4233151161","https://openalex.org/W4200079396"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"a":[3,85,102,124,184],"hybrid":[4,103,175],"evolutionary":[5],"intelligent":[6,104],"system":[7,105],"is":[8,59,123],"proposed":[9,101,121,159,221],"for":[10,80,106,112,143],"dimensionality":[11],"reduction":[12],"and":[13,49,63,93,111,139,171,173,183,213],"tuning":[14,60],"the":[15,26,32,40,43,54,65,73,82,95,108,114,117,145,154,158,179,202,220],"learnable":[16],"parameters":[17,62,110],"of":[18,31,116,126,157,181,205],"artificial":[19],"neural":[20,147],"network":[21],"(ANN)":[22],"that":[23,68,219],"can":[24,69],"forecast":[25],"future":[27],"(1-day-ahead)":[28],"close":[29,203],"price":[30,204],"stock":[33,207],"market":[34],"using":[35],"various":[36],"technical":[37],"indicators.":[38],"Although":[39],"ANN":[41,58,83],"possesses":[42],"ability":[44],"to":[45,87,200],"model":[46,122,160,176,188,222],"highly":[47],"uncertain":[48],"complex":[50],"nonlinear":[51],"data":[52],"but":[53],"key":[55],"challenge":[56],"in":[57,72,90],"its":[61],"minimizing":[64],"feature":[66,118,127],"set":[67],"be":[70],"used":[71,79],"input":[74],"layer.":[75],"The":[76,120,194],"backpropagation":[77],"approach":[78],"training":[81,144],"has":[84,197],"limitation":[86],"get":[88],"trapped":[89],"local":[91],"minima":[92],"overfitting":[94],"data.":[96],"Motivated":[97],"by":[98,178],"this,":[99],"we":[100],"optimizing":[107],"initial":[109],"reducing":[113],"dimensions":[115],"set.":[119],"combination":[125,180],"extraction":[128],"technique,":[129],"namely":[130],"principal":[131],"component":[132],"analysis":[133],"(PCA),":[134],"particle":[135],"swarm":[136],"optimization":[137],"(PSO),":[138],"Levenberg-Marquardt":[140],"(LM)":[141],"algorithm":[142],"feed-forward":[146],"networks":[148],"(FFNN).":[149],"This":[150],"paper":[151],"also":[152],"compares":[153],"forecasting":[155,225],"efficiency":[156],"with":[161,229],"PSO-FFNN,":[162],"regular":[163],"FFNN,":[164],"two":[165],"standard":[166],"benchmark":[167],"approaches":[168],"viz.":[169,189,209],"GA":[170],"DE":[172],"another":[174],"obtained":[177],"PCA":[182],"time":[185],"series":[186],"econometric":[187],"auto-regressive":[190],"distributed":[191],"lag":[192],"model.":[193],"presented":[195],"technique":[196],"been":[198],"tested":[199],"predict":[201],"three":[206],"indices":[208],"Nifty":[210],"50,":[211],"Sensex,":[212],"S&P":[214],"500.":[215],"Simulation":[216],"results":[217],"indicate":[218],"shows":[223],"superior":[224],"accuracy":[226],"as":[227],"compared":[228],"other":[230],"methods.":[231]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":2}],"updated_date":"2026-10-30T12:39:25.321706","created_date":"2021-06-22T00:00:00"}