{"id":"https://openalex.org/W4224917918","doi":"https://doi.org/10.1108/k-12-2021-1252","title":"Stock price prediction using improved extreme learning machine methods during the Covid-19 pandemic and selection of\u00a0appropriate prediction method","display_name":"Stock price prediction using improved extreme learning machine methods during the Covid-19 pandemic and selection of\u00a0appropriate prediction method","publication_year":2022,"publication_date":"2022-04-26","ids":{"openalex":"https://openalex.org/W4224917918","doi":"https://doi.org/10.1108/k-12-2021-1252"},"language":"en","primary_location":{"id":"doi:10.1108/k-12-2021-1252","is_oa":false,"landing_page_url":"https://doi.org/10.1108/k-12-2021-1252","pdf_url":null,"source":{"id":"https://openalex.org/S168682784","display_name":"Kybernetes","issn_l":"0368-492X","issn":["0368-492X","1758-7883"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Kybernetes","raw_type":"journal-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/A5017470736","display_name":"Asl\u0131 Boru","orcid":"https://orcid.org/0000-0001-6403-5307"},"institutions":[{"id":"https://openalex.org/I50379099","display_name":"Adana Science and Technology University","ror":"https://ror.org/013z3yn41","country_code":"TR","type":"education","lineage":["https://openalex.org/I50379099"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Asl\u0131 Boru \u0130pek","raw_affiliation_strings":["Adana Alparslan T\u00fcrke\u015f Science and Technology University, Adana, Turkey"],"affiliations":[{"raw_affiliation_string":"Adana Alparslan T\u00fcrke\u015f Science and Technology University, Adana, Turkey","institution_ids":["https://openalex.org/I50379099"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5017470736"],"corresponding_institution_ids":["https://openalex.org/I50379099"],"apc_list":null,"apc_paid":null,"fwci":0.6633,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.73140462,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"52","issue":"10","first_page":"4081","last_page":"4109"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.982200026512146,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9764000177383423,"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":0.6290581226348877},{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.6196320056915283},{"id":"https://openalex.org/keywords/novelty","display_name":"Novelty","score":0.49920225143432617},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49223676323890686},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.45563286542892456},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4553218185901642},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3559384346008301},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.32449039816856384},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2688401937484741},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2167845368385315}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6290581226348877},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.6196320056915283},{"id":"https://openalex.org/C2778738651","wikidata":"https://www.wikidata.org/wiki/Q16546687","display_name":"Novelty","level":2,"score":0.49920225143432617},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49223676323890686},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.45563286542892456},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4553218185901642},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3559384346008301},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.32449039816856384},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2688401937484741},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2167845368385315},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C27206212","wikidata":"https://www.wikidata.org/wiki/Q34178","display_name":"Theology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1108/k-12-2021-1252","is_oa":false,"landing_page_url":"https://doi.org/10.1108/k-12-2021-1252","pdf_url":null,"source":{"id":"https://openalex.org/S168682784","display_name":"Kybernetes","issn_l":"0368-492X","issn":["0368-492X","1758-7883"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Kybernetes","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.46000000834465027,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W1496413023","https://openalex.org/W1815264562","https://openalex.org/W1995382443","https://openalex.org/W2038950936","https://openalex.org/W2052912973","https://openalex.org/W2057160778","https://openalex.org/W2117034207","https://openalex.org/W2121971770","https://openalex.org/W2148551279","https://openalex.org/W2169064301","https://openalex.org/W2290024306","https://openalex.org/W2533569512","https://openalex.org/W2573137292","https://openalex.org/W2728943311","https://openalex.org/W2768355866","https://openalex.org/W2889216614","https://openalex.org/W2898812677","https://openalex.org/W2990597424","https://openalex.org/W3000398606","https://openalex.org/W3013138029","https://openalex.org/W3015367197","https://openalex.org/W3016752284","https://openalex.org/W3018069106","https://openalex.org/W3019427697","https://openalex.org/W3019706231","https://openalex.org/W3022951775","https://openalex.org/W3032831854","https://openalex.org/W3035216280","https://openalex.org/W3036522266","https://openalex.org/W3043208891","https://openalex.org/W3043793538","https://openalex.org/W3046209352","https://openalex.org/W3046930072","https://openalex.org/W3052356208","https://openalex.org/W3083777301","https://openalex.org/W3086140062","https://openalex.org/W3100014799","https://openalex.org/W3113046445","https://openalex.org/W3131696979","https://openalex.org/W3131932214","https://openalex.org/W3132698530","https://openalex.org/W3132961639","https://openalex.org/W3134867108","https://openalex.org/W3155820657","https://openalex.org/W3191622657","https://openalex.org/W3213128436","https://openalex.org/W3214241962","https://openalex.org/W4212907713","https://openalex.org/W4214533063","https://openalex.org/W4237237266","https://openalex.org/W4253629159"],"related_works":["https://openalex.org/W2381242807","https://openalex.org/W2067443264","https://openalex.org/W3126131230","https://openalex.org/W2347541121","https://openalex.org/W2080951048","https://openalex.org/W4288804799","https://openalex.org/W3032237421","https://openalex.org/W2390346111","https://openalex.org/W31566076","https://openalex.org/W2969890106"],"abstract_inverted_index":{"Purpose":[0],"Coronavirus":[1],"disease":[2],"(Covid-19)":[3],"has":[4],"created":[5],"uncertainty":[6],"in":[7,14,114,215],"all":[8,212],"countries":[9],"around":[10],"the":[11,22,37,40,93,101,130,138,146,162,174,179,183,204,208,216,239,248,257],"world,":[12],"resulting":[13],"enormous":[15],"human":[16],"suffering":[17],"and":[18,80,96,121,143,185,253,259],"global":[19],"recession.":[20],"Because":[21],"economic":[23],"impact":[24],"of":[25,39,49,100,118,145,169,199,207,235,241],"this":[26,50,91,170,236],"pandemic":[27],"is":[28,52,112,172,238],"still":[29],"unknown,":[30],"it":[31],"would":[32],"be":[33,222],"intriguing":[34],"to":[35,53,128,137,155,221,246],"study":[36,51,171,237],"incorporation":[38],"Covid-19":[41,187,261],"period":[42,188],"into":[43],"stock":[44,86,190],"price":[45,87,191],"prediction.":[46,88,192],"The":[47,109,166,193,233],"goal":[48],"use":[54,240],"an":[55,264],"improved":[56],"extreme":[57],"learning":[58],"machine":[59],"(ELM),":[60],"whose":[61],"parameters":[62],"are":[63],"optimized":[64,104],"by":[65],"four":[66,106,242],"meta-heuristics:":[67],"harmony":[68],"search":[69],"(HS),":[70],"social":[71],"spider":[72],"algorithm":[73,78],"(SSA),":[74],"artificial":[75],"bee":[76],"colony":[77],"(ABCA)":[79],"particle":[81],"swarm":[82],"optimization":[83],"(PSO)":[84],"for":[85,141,189,226,269],"Design/methodology/approach":[89],"In":[90],"study,":[92],"activation":[94],"functions":[95],"hidden":[97],"layer":[98],"neurons":[99],"ELM":[102,244],"were":[103,126,211],"using":[105],"different":[107],"meta-heuristics.":[108],"proposed":[110,147,209],"method":[111,159,201,266],"tested":[113],"five":[115],"sectors.":[116],"Analysis":[117],"variance":[119],"(ANOVA)":[120],"Duncan's":[122,149],"multiple":[123,150],"range":[124,151],"test":[125,139,152],"used":[127,154],"compare":[129],"prediction":[131,180,205,230],"methods.":[132,148],"First,":[133],"ANOVA":[134,163],"was":[135,153,267],"applied":[136],"data":[140],"verification":[142],"validation":[144],"identify":[156],"a":[157,223,228],"suitable":[158],"based":[160],"on":[161],"results.":[164],"Findings":[165],"main":[167],"finding":[168],"that":[173,203],"hybrid":[175,243],"methodology":[176],"can":[177],"improve":[178],"accuracy":[181],"during":[182,256],"pre":[184,258],"post":[186,260],"mean":[194],"absolute":[195],"percent":[196],"error":[197],"value":[198],"each":[200,270],"showed":[202],"errors":[206],"methods":[210,245],"under":[213],"0.13106":[214],"worst":[217],"case,":[218],"which":[219],"appears":[220],"remarkable":[224],"outcome":[225],"such":[227],"difficult":[229],"task.":[231],"Originality/value":[232],"novelty":[234],"evaluate":[247],"automotive,":[249],"technology,":[250],"food,":[251],"construction":[252],"energy":[254],"sectors":[255],"period.":[262],"Additionally,":[263],"appropriate":[265],"determined":[268],"sector.":[271]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}