{"id":"https://openalex.org/W4401335533","doi":"https://doi.org/10.1109/fuzz-ieee60900.2024.10611905","title":"A Type-2 Fuzzy Time Series Classification System with Optimized Time Period Selection","display_name":"A Type-2 Fuzzy Time Series Classification System with Optimized Time Period Selection","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4401335533","doi":"https://doi.org/10.1109/fuzz-ieee60900.2024.10611905"},"language":"en","primary_location":{"id":"doi:10.1109/fuzz-ieee60900.2024.10611905","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/fuzz-ieee60900.2024.10611905","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","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/A5051648925","display_name":"Ashish C. Bhatia","orcid":"https://orcid.org/0000-0003-2937-3129"},"institutions":[{"id":"https://openalex.org/I110002522","display_name":"University of Essex","ror":"https://ror.org/02nkf1q06","country_code":"GB","type":"education","lineage":["https://openalex.org/I110002522"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Ashish Bhatia","raw_affiliation_strings":["School of Computer Science and Electronic Engineering, University of Essex,Colchester,UK"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Electronic Engineering, University of Essex,Colchester,UK","institution_ids":["https://openalex.org/I110002522"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041176153","display_name":"Hani Hagras","orcid":"https://orcid.org/0000-0002-2818-5292"},"institutions":[{"id":"https://openalex.org/I110002522","display_name":"University of Essex","ror":"https://ror.org/02nkf1q06","country_code":"GB","type":"education","lineage":["https://openalex.org/I110002522"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Hani Hagras","raw_affiliation_strings":["School of Computer Science and Electronic Engineering, University of Essex,Colchester,UK"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Electronic Engineering, University of Essex,Colchester,UK","institution_ids":["https://openalex.org/I110002522"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5051648925"],"corresponding_institution_ids":["https://openalex.org/I110002522"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13465039,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9994999766349792,"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/T10820","display_name":"Fuzzy Logic and Control Systems","score":0.9793000221252441,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9771999716758728,"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/selection","display_name":"Selection (genetic algorithm)","score":0.6738622784614563},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5885323286056519},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5660082101821899},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5602415800094604},{"id":"https://openalex.org/keywords/period","display_name":"Period (music)","score":0.5027422904968262},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.4471493065357208},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34401828050613403},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2732943594455719},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.07891324162483215}],"concepts":[{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.6738622784614563},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5885323286056519},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5660082101821899},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5602415800094604},{"id":"https://openalex.org/C2781291010","wikidata":"https://www.wikidata.org/wiki/Q178580","display_name":"Period (music)","level":2,"score":0.5027422904968262},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.4471493065357208},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34401828050613403},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2732943594455719},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.07891324162483215},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fuzz-ieee60900.2024.10611905","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/fuzz-ieee60900.2024.10611905","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.699999988079071}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1976526581","https://openalex.org/W1984606274","https://openalex.org/W2000976145","https://openalex.org/W2015523368","https://openalex.org/W2019207321","https://openalex.org/W2071271882","https://openalex.org/W2079325629","https://openalex.org/W2091510499","https://openalex.org/W2094788183","https://openalex.org/W2125631815","https://openalex.org/W2155976821","https://openalex.org/W2168577773","https://openalex.org/W2410506558","https://openalex.org/W2555077524","https://openalex.org/W2735519414","https://openalex.org/W2811507150","https://openalex.org/W2911509307","https://openalex.org/W2944288969","https://openalex.org/W2988244882","https://openalex.org/W2998010409","https://openalex.org/W3030160217","https://openalex.org/W3092409589","https://openalex.org/W3115948762","https://openalex.org/W4250843460","https://openalex.org/W4254931243","https://openalex.org/W4295767967","https://openalex.org/W4394957446","https://openalex.org/W6778699280"],"related_works":["https://openalex.org/W2590211375","https://openalex.org/W2581822552","https://openalex.org/W2579643896","https://openalex.org/W2361078629","https://openalex.org/W4251508416","https://openalex.org/W4399606178","https://openalex.org/W2119012848","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660"],"abstract_inverted_index":{"Time-series":[0],"analysis":[1],"plays":[2],"a":[3,47,51,106],"crucial":[4],"role":[5],"in":[6,32,126,132],"numerous":[7],"real-world":[8],"applications,":[9],"ranging":[10],"from":[11,105],"financial":[12],"forecasting":[13],"to":[14,24,62,87],"environmental":[15],"monitoring":[16],"and":[17,29,59,92],"beyond.":[18],"Traditional":[19],"classification":[20],"techniques":[21],"often":[22],"struggle":[23],"effectively":[25,91],"handle":[26,88],"the":[27,57,81,116,120],"uncertainties":[28,58],"imprecision":[30],"inherent":[31],"time":[33,41],"series":[34,42],"data.":[35],"To":[36],"address":[37],"this":[38,66],"challenge,":[39],"fuzzy":[40,85],"models":[43],"have":[44,98],"emerged":[45],"as":[46],"promising":[48],"alternative,":[49],"offering":[50],"flexible":[52],"framework":[53],"capable":[54],"of":[55,83,109],"capturing":[56],"vagueness":[60],"intrinsic":[61],"temporal":[63,89],"patterns.":[64],"In":[65],"paper,":[67],"we":[68],"propose":[69],"an":[70],"Optimized":[71],"Time":[72],"Series":[73],"Interval":[74],"Valued":[75],"Fuzzy":[76],"System":[77],"(OTS-IVFS)":[78],"that":[79],"leverages":[80],"power":[82],"type-2":[84],"logic":[86],"data":[90,103],"provide":[93],"fully":[94],"explainable":[95],"models.":[96],"We":[97],"performed":[99],"experiments":[100],"on":[101],"five":[102],"sets":[104],"diverse":[107],"set":[108],"use":[110],"cases.":[111],"Our":[112],"system":[113],"significantly":[114],"outperforms":[115],"best-in-class":[117],"algorithms":[118],"for":[119],"Earthquake":[121],"dataset":[122],"by":[123],"16.67%":[124],"increase":[125],"accuracy":[127],"while":[128],"giving":[129],"comparable":[130],"results":[131],"3":[133],"other":[134],"datasets,":[135],"whilst":[136],"maintaining":[137],"full":[138],"interpretability.":[139]},"counts_by_year":[],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}