{"id":"https://openalex.org/W2592534188","doi":"https://doi.org/10.1109/icmlc.2016.7872967","title":"A novel method for forecastng the taiex based on optimal intervals and similarity measures between the subscripts of fuzzy sets","display_name":"A novel method for forecastng the taiex based on optimal intervals and similarity measures between the subscripts of fuzzy sets","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2592534188","doi":"https://doi.org/10.1109/icmlc.2016.7872967","mag":"2592534188"},"language":"en","primary_location":{"id":"doi:10.1109/icmlc.2016.7872967","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlc.2016.7872967","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Conference on Machine Learning and Cybernetics (ICMLC)","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/A5061831201","display_name":"Shyi\u2010Ming Chen","orcid":"https://orcid.org/0000-0001-8648-631X"},"institutions":[{"id":"https://openalex.org/I154864474","display_name":"National Taiwan University of Science and Technology","ror":"https://ror.org/00q09pe49","country_code":"TW","type":"education","lineage":["https://openalex.org/I154864474"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Shyi-Ming Chen","raw_affiliation_strings":["Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan","institution_ids":["https://openalex.org/I154864474"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034997897","display_name":"Shou-Hsiung Cheng","orcid":null},"institutions":[{"id":"https://openalex.org/I5971364","display_name":"Chienkuo Technology University","ror":"https://ror.org/04ahb7r80","country_code":"TW","type":"education","lineage":["https://openalex.org/I5971364"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Shou-Hsiung Cheng","raw_affiliation_strings":["Department of Kinesiology Health Leisure Studies, Chienkuo Technology University, Changhua, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Kinesiology Health Leisure Studies, Chienkuo Technology University, Changhua, Taiwan","institution_ids":["https://openalex.org/I5971364"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021476517","display_name":"Wen-Shan Jian","orcid":null},"institutions":[{"id":"https://openalex.org/I154864474","display_name":"National Taiwan University of Science and Technology","ror":"https://ror.org/00q09pe49","country_code":"TW","type":"education","lineage":["https://openalex.org/I154864474"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Wen-Shan Jian","raw_affiliation_strings":["Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan","institution_ids":["https://openalex.org/I154864474"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5061831201"],"corresponding_institution_ids":["https://openalex.org/I154864474"],"apc_list":null,"apc_paid":null,"fwci":0.3732,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.73963453,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"34","issue":null,"first_page":"666","last_page":"670"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9976999759674072,"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":0.9976999759674072,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9797999858856201,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T10820","display_name":"Fuzzy Logic and Control Systems","score":0.9724000096321106,"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/fuzzy-logic","display_name":"Fuzzy logic","score":0.6179155111312866},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5654091238975525},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.5164061188697815},{"id":"https://openalex.org/keywords/fuzzy-set","display_name":"Fuzzy set","score":0.5017859935760498},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.4987952709197998},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.491901159286499},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.48762908577919006},{"id":"https://openalex.org/keywords/similarity-measure","display_name":"Similarity measure","score":0.4854596257209778},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.47513678669929504},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4277225732803345},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.38981327414512634},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2486802637577057}],"concepts":[{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.6179155111312866},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5654091238975525},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.5164061188697815},{"id":"https://openalex.org/C42011625","wikidata":"https://www.wikidata.org/wiki/Q1055058","display_name":"Fuzzy set","level":3,"score":0.5017859935760498},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.4987952709197998},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.491901159286499},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48762908577919006},{"id":"https://openalex.org/C2776517306","wikidata":"https://www.wikidata.org/wiki/Q29017317","display_name":"Similarity measure","level":2,"score":0.4854596257209778},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.47513678669929504},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4277225732803345},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.38981327414512634},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2486802637577057},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icmlc.2016.7872967","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlc.2016.7872967","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Conference on Machine Learning and Cybernetics (ICMLC)","raw_type":"proceedings-article"},{"id":"mag:3048559183","is_oa":false,"landing_page_url":"https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=201902236840090192","pdf_url":null,"source":{"id":"https://openalex.org/S4306512817","display_name":"IEEE Conference Proceedings","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"IEEE Conference Proceedings","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1125761772","https://openalex.org/W1875348914","https://openalex.org/W1969574035","https://openalex.org/W1970947727","https://openalex.org/W1971869067","https://openalex.org/W1992096620","https://openalex.org/W1998382295","https://openalex.org/W2085752583","https://openalex.org/W2103347496","https://openalex.org/W2105113966","https://openalex.org/W2127817155","https://openalex.org/W2130519559","https://openalex.org/W2131453387","https://openalex.org/W2136103712","https://openalex.org/W2152195021","https://openalex.org/W2168577773","https://openalex.org/W2394226702","https://openalex.org/W4211007335","https://openalex.org/W4241443503","https://openalex.org/W6649967988"],"related_works":["https://openalex.org/W2319693127","https://openalex.org/W2072263576","https://openalex.org/W2474567666","https://openalex.org/W1940044583","https://openalex.org/W2056226831","https://openalex.org/W2806903871","https://openalex.org/W4320802053","https://openalex.org/W2112090263","https://openalex.org/W2315413568","https://openalex.org/W2083145701"],"abstract_inverted_index":{"This":[0],"paper":[1,133],"proposes":[2],"a":[3,17,34,104,115],"novel":[4,116],"fuzzy":[5,24,111,127],"forecasting":[6,9,122,128,145],"method":[7,117,129],"for":[8,141],"the":[10,21,66,72,78,94,108,121,124,138,144,147],"TAIEX":[11,76],"based":[12],"on":[13,91],"optimal":[14,63,92],"intervals":[15,64],"and":[16,31,33,48,77,103],"similarity":[18,105],"measure":[19,106],"between":[20,107],"subscripts":[22,109],"of":[23,68,71,110,123,146],"sets,":[25,112],"where":[26,83],"two":[27],"threshold":[28],"values":[29],"\u03b1":[30,40],"\u03b2":[32,44],"weighting":[35],"constant":[36],"\u03b3":[37,49],"are":[38],"used,":[39],"\u03f5":[41,45,50,85],"[0,":[42,46,51],"1],":[43],"1]":[47],"1].":[52],"First,":[53],"we":[54,113],"use":[55],"particle":[56],"swarm":[57],"optimization":[58],"(PSO)":[59],"techniques":[60],"to":[61,118],"obtain":[62],"in":[65,131],"universe":[67],"discourses":[69],"(UODs)":[70],"main":[73],"factor":[74,80],"(MF)":[75],"secondary":[79],"(SF),":[81],"respectively,":[82],"SF":[84],"{Dow":[86],"Jones,":[87],"NASDAQ,":[88],"M1B}.":[89],"Based":[90],"intervals,":[93],"constructed":[95],"two-factors":[96],"second-order":[97],"fuzzy-trend":[98],"logical":[99],"relationship":[100],"groups":[101],"(TSFTLRGs),":[102],"propose":[114],"deal":[119],"with":[120,143],"TAIEX.":[125,148],"The":[126],"presented":[130],"this":[132],"gets":[134],"better":[135],"performance":[136],"than":[137],"existing":[139],"methods":[140],"dealing":[142]},"counts_by_year":[{"year":2016,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
