{"id":"https://openalex.org/W3082103851","doi":"https://doi.org/10.1109/fuzz48607.2020.9177651","title":"Time Series Prediction Using Random Weights Fuzzy Neural Networks","display_name":"Time Series Prediction Using Random Weights Fuzzy Neural Networks","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3082103851","doi":"https://doi.org/10.1109/fuzz48607.2020.9177651","mag":"3082103851"},"language":"en","primary_location":{"id":"doi:10.1109/fuzz48607.2020.9177651","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz48607.2020.9177651","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 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/A5052397999","display_name":"Antonello Rosato","orcid":"https://orcid.org/0000-0002-4371-5925"},"institutions":[{"id":"https://openalex.org/I861853513","display_name":"Sapienza University of Rome","ror":"https://ror.org/02be6w209","country_code":"IT","type":"education","lineage":["https://openalex.org/I861853513"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Antonello Rosato","raw_affiliation_strings":["Department of Information Engineering, Electronics and Telecommunications (DIET), University of Rome \"La Sapienza\", Rome, Italy"],"affiliations":[{"raw_affiliation_string":"Department of Information Engineering, Electronics and Telecommunications (DIET), University of Rome \"La Sapienza\", Rome, Italy","institution_ids":["https://openalex.org/I861853513"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015796693","display_name":"Massimo Panella","orcid":"https://orcid.org/0000-0002-9876-1494"},"institutions":[{"id":"https://openalex.org/I861853513","display_name":"Sapienza University of Rome","ror":"https://ror.org/02be6w209","country_code":"IT","type":"education","lineage":["https://openalex.org/I861853513"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Massimo Panella","raw_affiliation_strings":["Department of Information Engineering, Electronics and Telecommunications (DIET), University of Rome \"La Sapienza\", Rome, Italy"],"affiliations":[{"raw_affiliation_string":"Department of Information Engineering, Electronics and Telecommunications (DIET), University of Rome \"La Sapienza\", Rome, Italy","institution_ids":["https://openalex.org/I861853513"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5052397999"],"corresponding_institution_ids":["https://openalex.org/I861853513"],"apc_list":null,"apc_paid":null,"fwci":0.2651,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.63120605,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":95},"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/T10320","display_name":"Neural Networks and Applications","score":0.9997000098228455,"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/T10320","display_name":"Neural Networks and Applications","score":0.9997000098228455,"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/T10820","display_name":"Fuzzy Logic and Control Systems","score":0.9991999864578247,"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":0.9937000274658203,"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/generalization","display_name":"Generalization","score":0.6690297722816467},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.667980432510376},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6666284203529358},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6315645575523376},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6264605522155762},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5728333592414856},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.546995997428894},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.5413693189620972},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4864923357963562},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4500208795070648},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2196144163608551}],"concepts":[{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6690297722816467},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.667980432510376},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6666284203529358},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6315645575523376},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6264605522155762},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5728333592414856},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.546995997428894},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.5413693189620972},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4864923357963562},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4500208795070648},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2196144163608551},{"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/fuzz48607.2020.9177651","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz48607.2020.9177651","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","raw_type":"proceedings-article"},{"id":"pmh:oai:iris.uniroma1.it:11573/1461035","is_oa":false,"landing_page_url":"http://hdl.handle.net/11573/1461035","pdf_url":null,"source":{"id":"https://openalex.org/S4377196107","display_name":"IRIS Research product catalog (Sapienza University of Rome)","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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1600426049","https://openalex.org/W1833345539","https://openalex.org/W1989665358","https://openalex.org/W1996640396","https://openalex.org/W2019207321","https://openalex.org/W2032270680","https://openalex.org/W2053794432","https://openalex.org/W2066366061","https://openalex.org/W2068844879","https://openalex.org/W2094070726","https://openalex.org/W2103414828","https://openalex.org/W2144141835","https://openalex.org/W2155910151","https://openalex.org/W2160270275","https://openalex.org/W2161940528","https://openalex.org/W2168530688","https://openalex.org/W2283215762","https://openalex.org/W2485122544","https://openalex.org/W2551138811","https://openalex.org/W2605962227","https://openalex.org/W2607226932","https://openalex.org/W2734883782","https://openalex.org/W2972734708","https://openalex.org/W4210509282"],"related_works":["https://openalex.org/W3162204513","https://openalex.org/W2371138613","https://openalex.org/W2048963458","https://openalex.org/W43109613","https://openalex.org/W2359952343","https://openalex.org/W2239445980","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W3146111732","https://openalex.org/W1990205660"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"introduce":[4],"Random":[5],"Weights":[6],"Fuzzy":[7],"Neural":[8],"Networks":[9],"as":[10],"a":[11,39],"suitable":[12],"tool":[13],"for":[14,65],"solving":[15],"prediction":[16,101],"problems.":[17],"The":[18,42],"generalization":[19],"capability":[20],"of":[21,81,88],"these":[22],"randomized":[23],"fuzzy":[24],"neural":[25],"networks":[26],"is":[27,44],"exploited":[28],"in":[29],"order":[30],"to":[31,51,62,77,99],"estimate":[32],"accurately":[33],"the":[34,52,58,69,78,86,89],"sample":[35],"be":[36,63],"predicted":[37],"from":[38],"multidimensional":[40],"input.":[41],"latter":[43],"obtained":[45],"by":[46,92],"applying":[47],"an":[48],"embedding":[49],"technique":[50],"time":[53,74],"series,":[54],"which":[55],"selects":[56],"only":[57],"meaningful":[59],"past":[60],"samples":[61],"used":[64],"prediction.":[66],"We":[67,84],"tested":[68],"proposed":[70,90],"approach":[71,91],"on":[72,104],"real-world":[73],"series":[75],"pertaining":[76],"application":[79],"context":[80],"power":[82],"delivery.":[83],"proved":[85],"efficacy":[87],"comparing":[93],"its":[94],"forecasting":[95],"accuracy":[96],"with":[97],"respect":[98],"other":[100],"systems":[102],"based":[103],"well-known":[105],"data-driven":[106],"regression":[107],"models.":[108]},"counts_by_year":[{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}