{"id":"https://openalex.org/W2017583315","doi":"https://doi.org/10.1109/nabic.2011.6089464","title":"Enhanced accuracy of fuzzy time series predictor using genetic algorithm","display_name":"Enhanced accuracy of fuzzy time series predictor using genetic algorithm","publication_year":2011,"publication_date":"2011-10-01","ids":{"openalex":"https://openalex.org/W2017583315","doi":"https://doi.org/10.1109/nabic.2011.6089464","mag":"2017583315"},"language":"en","primary_location":{"id":"doi:10.1109/nabic.2011.6089464","is_oa":false,"landing_page_url":"https://doi.org/10.1109/nabic.2011.6089464","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 Third World Congress on Nature and Biologically Inspired Computing","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/A5042018954","display_name":"Bindu Garg","orcid":"https://orcid.org/0000-0002-8212-0633"},"institutions":[{"id":"https://openalex.org/I59475050","display_name":"Jamia Millia Islamia","ror":"https://ror.org/00pnhhv55","country_code":"IN","type":"education","lineage":["https://openalex.org/I59475050"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Bindu Garg","raw_affiliation_strings":["Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India","Department of Computer Eng., Jamia Millia Islamia, New Delhi-110025, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India","institution_ids":["https://openalex.org/I59475050"]},{"raw_affiliation_string":"Department of Computer Eng., Jamia Millia Islamia, New Delhi-110025, India","institution_ids":["https://openalex.org/I59475050"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055656153","display_name":"M. M. Sufyan Beg","orcid":"https://orcid.org/0000-0002-6665-2584"},"institutions":[{"id":"https://openalex.org/I59475050","display_name":"Jamia Millia Islamia","ror":"https://ror.org/00pnhhv55","country_code":"IN","type":"education","lineage":["https://openalex.org/I59475050"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"M.M. Sufyan Beg","raw_affiliation_strings":["Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India","Department of Computer Eng., Jamia Millia Islamia, New Delhi-110025, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India","institution_ids":["https://openalex.org/I59475050"]},{"raw_affiliation_string":"Department of Computer Eng., Jamia Millia Islamia, New Delhi-110025, India","institution_ids":["https://openalex.org/I59475050"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029931471","display_name":"Abdul Quaiyum Ansari","orcid":"https://orcid.org/0000-0003-0153-4381"},"institutions":[{"id":"https://openalex.org/I59475050","display_name":"Jamia Millia Islamia","ror":"https://ror.org/00pnhhv55","country_code":"IN","type":"education","lineage":["https://openalex.org/I59475050"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"A.Q. Ansari","raw_affiliation_strings":["Department of Electrical Engineering, Jamia Millia Islamia, New Delhi, India","Department of Electrical Engg., Jamia Millia Islamia, New Delhi - 110025, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Jamia Millia Islamia, New Delhi, India","institution_ids":["https://openalex.org/I59475050"]},{"raw_affiliation_string":"Department of Electrical Engg., Jamia Millia Islamia, New Delhi - 110025, India","institution_ids":["https://openalex.org/I59475050"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4233,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.67567931,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"5","issue":null,"first_page":"273","last_page":"278"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9994999766349792,"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":0.9994999766349792,"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/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9704999923706055,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9682000279426575,"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/series","display_name":"Series (stratigraphy)","score":0.591193675994873},{"id":"https://openalex.org/keywords/fitness-function","display_name":"Fitness function","score":0.5903968811035156},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5812749862670898},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5667431950569153},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5571286678314209},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5558098554611206},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.5434499382972717},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.49622803926467896},{"id":"https://openalex.org/keywords/chromosome","display_name":"Chromosome","score":0.49495309591293335},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.4843481779098511},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4776671826839447},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.41591647267341614},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3693024516105652},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35667508840560913},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.35006415843963623},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.30762872099876404},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.24024972319602966},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08432245254516602}],"concepts":[{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.591193675994873},{"id":"https://openalex.org/C176066374","wikidata":"https://www.wikidata.org/wiki/Q629118","display_name":"Fitness function","level":3,"score":0.5903968811035156},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5812749862670898},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5667431950569153},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5571286678314209},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5558098554611206},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.5434499382972717},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.49622803926467896},{"id":"https://openalex.org/C30481170","wikidata":"https://www.wikidata.org/wiki/Q37748","display_name":"Chromosome","level":3,"score":0.49495309591293335},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4843481779098511},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4776671826839447},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.41591647267341614},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3693024516105652},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35667508840560913},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.35006415843963623},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30762872099876404},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.24024972319602966},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08432245254516602},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/nabic.2011.6089464","is_oa":false,"landing_page_url":"https://doi.org/10.1109/nabic.2011.6089464","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 Third World Congress on Nature and Biologically Inspired Computing","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":25,"referenced_works":["https://openalex.org/W23132399","https://openalex.org/W158623576","https://openalex.org/W1496557148","https://openalex.org/W1586003742","https://openalex.org/W1596687047","https://openalex.org/W1971869067","https://openalex.org/W1977326290","https://openalex.org/W1992096620","https://openalex.org/W2004157188","https://openalex.org/W2022408418","https://openalex.org/W2036156437","https://openalex.org/W2041723679","https://openalex.org/W2047777882","https://openalex.org/W2109118191","https://openalex.org/W2122108166","https://openalex.org/W2130257114","https://openalex.org/W2156019969","https://openalex.org/W2167563355","https://openalex.org/W2168087105","https://openalex.org/W2534978607","https://openalex.org/W3023540311","https://openalex.org/W4234244098","https://openalex.org/W6634940605","https://openalex.org/W6679209133","https://openalex.org/W6684932513"],"related_works":["https://openalex.org/W2375437568","https://openalex.org/W2106492215","https://openalex.org/W2326694407","https://openalex.org/W2082859007","https://openalex.org/W2164831575","https://openalex.org/W2378719652","https://openalex.org/W2115729582","https://openalex.org/W2094658154","https://openalex.org/W2370837632","https://openalex.org/W2353187647"],"abstract_inverted_index":{"Accuracy":[0],"is":[1,14,25,34,51,82,112,120],"one":[2],"of":[3,11,20,48,56,89,100,106,126,128,138,149],"the":[4,9,42,98,135],"most":[5],"important":[6,35],"aspects":[7],"in":[8],"domain":[10],"forecasting.":[12],"It":[13,140],"very":[15],"difficult":[16],"to":[17,52,68,132],"improve":[18,69],"accuracy":[19,33,71],"prediction":[21,24,70],"system":[22],"where":[23],"based":[26],"only":[27],"on":[28,97],"large":[29],"historical":[30],"values":[31],"and":[32,66,157],"for":[36,72,91,114],"each":[37,73],"predicted":[38],"value":[39,103],"along":[40,77],"with":[41,78],"whole":[43,79],"system.":[44,80],"The":[45],"main":[46],"objective":[47],"this":[49],"research":[50],"optimize":[53],"dominant":[54],"factors":[55],"fuzzy":[57],"time":[58,74,95,110,129],"series":[59,75,130],"predictor":[60],"(FTSP)":[61],"using":[62],"genetic":[63],"algorithm":[64],"(GA)":[65],"further":[67],"variable":[76],"This":[81],"obtained":[83],"by":[84,123],"(a)":[85],"generating":[86],"wide":[87],"range":[88],"parameters":[90],"membership":[92],"function":[93],"at":[94,109],"t":[96,111],"basis":[99],"their":[101],"base":[102],"(b)":[104],"subset":[105],"population":[107],"generated":[108],"used":[113],"fitness":[115],"checking.":[116],"Additionally,":[117],"GA":[118,150],"complexity":[119],"also":[121],"reduced":[122,152],"utilizing":[124],"rate":[125,161],"change":[127],"data":[131],"cut":[133],"down":[134],"bit":[136],"size":[137],"chromosome.":[139],"can":[141],"be":[142],"observed":[143],"from":[144],"comparative":[145],"study":[146],"that":[147],"use":[148],"considerably":[151],"mean":[153],"square":[154],"error":[155,160],"(MSE)":[156],"average":[158],"forecasting":[159],"(AFER).":[162]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
