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Forecasting energy time-series data using a fuzzy ARTMAP neural network

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Show simple item record de Assis Pedrobon Ferreira, Willian Grout, Ian da Silva, Alexandre César Rodrigues 2021-08-06T10:46:22Z 2021-08-06T10:46:22Z 2020
dc.description peer-reviewed en_US
dc.description.abstract Time-series forecasting is an important field of machine learning and is fundamental in analyzing trends based on historical data from various sources. In this paper, a fuzzy ARTMAP neural network for time series forecasting is presented. To validate the proposed system, two energy-related datasets from Great Britain were selected. With a promising processing time and accuracy as good as a traditional machine learning algorithm, the fuzzy ARTMAP neural network has shown that can be a good option to perform forecasting considering different time-based data issues. en_US
dc.language.iso eng en_US
dc.publisher IEEE Computer Society en_US
dc.relation.ispartofseries 2020 International Conference on Power, Energy and Innovations (ICPEI);pp.1-4
dc.rights © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. en_US
dc.subject time-series forecasting en_US
dc.subject fuzzy ARTMAP neural network en_US
dc.subject energy data en_US
dc.title Forecasting energy time-series data using a fuzzy ARTMAP neural network en_US
dc.type info:eu-repo/semantics/conferenceObject en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.identifier.doi 10.1109/ICPEI49860.2020.9431435
dc.contributor.sponsor Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil (CAPES) en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US

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