{"id":"https://openalex.org/W4414116976","doi":"https://doi.org/10.1109/fuzz62266.2025.11152170","title":"e-AutoMFIS2: A Multi-Objective Fuzzy Model for Interpretable Multivariate Time Series Forecasting","display_name":"e-AutoMFIS2: A Multi-Objective Fuzzy Model for Interpretable Multivariate Time Series Forecasting","publication_year":2025,"publication_date":"2025-07-06","ids":{"openalex":"https://openalex.org/W4414116976","doi":"https://doi.org/10.1109/fuzz62266.2025.11152170"},"language":"en","primary_location":{"id":"doi:10.1109/fuzz62266.2025.11152170","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz62266.2025.11152170","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Fuzzy Systems (FUZZ)","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/A5082030658","display_name":"Diego Nunes da Silva","orcid":"https://orcid.org/0000-0002-6952-1088"},"institutions":[{"id":"https://openalex.org/I2699952","display_name":"Pontifical Catholic University of Rio de Janeiro","ror":"https://ror.org/01dg47b60","country_code":"BR","type":"education","lineage":["https://openalex.org/I2699952"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Diego Silva","raw_affiliation_strings":["PUC-Rio,Electrical Engineering Department,Rio de Janeiro,Brazil"],"affiliations":[{"raw_affiliation_string":"PUC-Rio,Electrical Engineering Department,Rio de Janeiro,Brazil","institution_ids":["https://openalex.org/I2699952"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085598532","display_name":"Marley Vellasco","orcid":"https://orcid.org/0000-0002-9790-1328"},"institutions":[{"id":"https://openalex.org/I2699952","display_name":"Pontifical Catholic University of Rio de Janeiro","ror":"https://ror.org/01dg47b60","country_code":"BR","type":"education","lineage":["https://openalex.org/I2699952"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Marley Vellasco","raw_affiliation_strings":["PUC-Rio,Electrical Engineering Department,Rio de Janeiro,Brazil"],"affiliations":[{"raw_affiliation_string":"PUC-Rio,Electrical Engineering Department,Rio de Janeiro,Brazil","institution_ids":["https://openalex.org/I2699952"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047394750","display_name":"T. Carvalho","orcid":"https://orcid.org/0009-0006-5841-5526"},"institutions":[{"id":"https://openalex.org/I40034438","display_name":"Universidade do Estado do Rio de Janeiro","ror":"https://ror.org/0198v2949","country_code":"BR","type":"education","lineage":["https://openalex.org/I40034438"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Thiago Medeiros Carvalho","raw_affiliation_strings":["UERJ,Department of Systems Eng. and Computation,Rio de Janeiro,Brazil"],"affiliations":[{"raw_affiliation_string":"UERJ,Department of Systems Eng. and Computation,Rio de Janeiro,Brazil","institution_ids":["https://openalex.org/I40034438"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5082030658"],"corresponding_institution_ids":["https://openalex.org/I2699952"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.29851034,"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":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9984999895095825,"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":0.9984999895095825,"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/T10820","display_name":"Fuzzy Logic and Control Systems","score":0.9937999844551086,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9925000071525574,"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/hyperparameter","display_name":"Hyperparameter","score":0.7096999883651733},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.6624000072479248},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.6435999870300293},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.5583999752998352},{"id":"https://openalex.org/keywords/automation","display_name":"Automation","score":0.5317000150680542},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.503000020980835},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4336000084877014}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.7096999883651733},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.6624000072479248},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6510000228881836},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.6435999870300293},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5960999727249146},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5662000179290771},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.5583999752998352},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5432999730110168},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.5317000150680542},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.503000020980835},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4336000084877014},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.40549999475479126},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.35010001063346863},{"id":"https://openalex.org/C195975749","wikidata":"https://www.wikidata.org/wiki/Q1475705","display_name":"Fuzzy control system","level":3,"score":0.34060001373291016},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.3122999966144562},{"id":"https://openalex.org/C38180746","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate analysis","level":2,"score":0.3046000003814697},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.30320000648498535},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.2669999897480011}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fuzz62266.2025.11152170","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz62266.2025.11152170","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Fuzzy Systems (FUZZ)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2033879042","https://openalex.org/W2126105956","https://openalex.org/W2138605668","https://openalex.org/W2159761760","https://openalex.org/W2552480641","https://openalex.org/W2553757956","https://openalex.org/W2557046785","https://openalex.org/W2604847698","https://openalex.org/W2888165363","https://openalex.org/W2958089299","https://openalex.org/W2995739402","https://openalex.org/W3021613070","https://openalex.org/W3022643593","https://openalex.org/W3132782787","https://openalex.org/W3192456385","https://openalex.org/W4221148002","https://openalex.org/W4377115400","https://openalex.org/W4385075884","https://openalex.org/W4387569935","https://openalex.org/W4391688297","https://openalex.org/W4409568540"],"related_works":["https://openalex.org/W2406638334","https://openalex.org/W1991765889","https://openalex.org/W1990068454","https://openalex.org/W2472172556","https://openalex.org/W1919101720","https://openalex.org/W1570805059","https://openalex.org/W2119012848","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660"],"abstract_inverted_index":{"This":[0,81],"article":[1],"introduces":[2],"e-AutoMFIS2,":[3],"an":[4],"enhanced":[5],"version":[6,24],"of":[7],"the":[8,22,54,75],"e-AutoMFIS":[9],"model,":[10],"which":[11],"integrates":[12],"fuzzy":[13],"logic":[14],"and":[15,28,38,72,88,105,114,129],"ensemble":[16],"techniques":[17],"for":[18,58,77,122],"multivariate":[19,123],"forecasting.":[20],"While":[21],"initial":[23],"delivered":[25],"satisfactory":[26],"accuracy":[27,87],"interpretability,":[29],"it":[30,65],"faced":[31],"challenges":[32],"related":[33],"to":[34,102,132],"manual":[35,79],"parameter":[36,112],"tuning":[37],"random":[39],"data":[40],"subsampling,":[41],"affecting":[42],"its":[43,103],"efficiency.":[44],"To":[45],"overcome":[46],"these":[47],"limitations,":[48],"e-AutoMFIS2":[49,117],"automates":[50],"key":[51],"processes":[52],"using":[53],"NSGA-II":[55],"genetic":[56],"algorithm":[57],"hyperparameter":[59],"selection":[60,68,113],"through":[61,95],"multi-objective":[62],"optimization.":[63],"Additionally,":[64],"refines":[66],"subsample":[67],"by":[69],"leveraging":[70],"cross-correlation":[71],"autocorrelation,":[73],"eliminating":[74],"need":[76],"exhaustive":[78],"searches.":[80],"automation":[82],"enhances":[83],"efficiency":[84],"while":[85],"maintaining":[86],"interoperability.":[89],"The":[90,109],"model\u2019s":[91],"effectiveness":[92],"was":[93],"validated":[94],"case":[96],"studies,":[97],"demonstrating":[98],"superior":[99],"performance":[100],"compared":[101],"predecessor":[104],"other":[106],"forecasting":[107],"models.":[108],"improvements":[110],"in":[111],"subsampling":[115],"reinforce":[116],"as":[118],"a":[119,126],"robust":[120],"tool":[121],"forecasting,":[124],"offering":[125],"more":[127],"streamlined":[128],"effective":[130],"approach":[131],"handling":[133],"complex":[134],"datasets.":[135]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-10T00:00:00"}