Navigating market sentiments: a novel approach to iron ore price forecasting with weighted fuzzy time series

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dc.creatorFlavio Mauricio da Cunha Souza
dc.creatorGeraldo Pereira Rocha Filho
dc.creatorFrederico Gadelha Guimarães
dc.creatorRodolfo I. Meneguette
dc.creatorGustavo Pessin
dc.date.accessioned2026-01-20T20:39:53Z
dc.date.issued2024-04-29
dc.identifier.doihttps://doi.org/10.3390/info15050251
dc.identifier.issn2078-2489
dc.identifier.urihttps://hdl.handle.net/1843/1448
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofInformation
dc.rightsAcesso aberto
dc.subjectSérie temporal
dc.subjectMinério de ferro
dc.subjectInteligência artificial
dc.subjectPrevisão de preços- minério
dc.subject.otherMachine learning
dc.subject.otherTime series
dc.subject.otherNatural language processing
dc.subject.otherIron ore
dc.titleNavigating market sentiments: a novel approach to iron ore price forecasting with weighted fuzzy time series
dc.typeArtigo de periódico
local.citation.epage18
local.citation.spage1
local.citation.volume15
local.description.resumoThe global iron ore price is influenced by numerous factors, thus showcasing a complex interplay among them. The collective expectations of market participants over time shape the variations and trends within the iron ore price time series. Consequently, devising a robust forecasting model for the volatility of iron ore prices, as well as for other assets connected to this commodity, is critical for guiding future investments and decision-making processes in mining companies. Within this framework, the integration of artificial intelligence techniques, encompassing both technical and fundamental analyses, is aimed at developing a comprehensive, autonomous hybrid system for decision support, which is specialized in iron ore asset management. This approach not only enhances the accuracy of predictions but also supports strategic planning in the mining sector.
local.identifier.orcidhttps://orcid.org/0009-0001-2711-0512
local.identifier.orcidhttps://orcid.org/0000-0001-6795-2768
local.identifier.orcidhttps://orcid.org/0000-0001-9238-8839
local.identifier.orcidhttps://orcid.org/0000-0003-2982-4006
local.identifier.orcidhttps://orcid.org/0000-0002-7411-9229
local.publisher.countryBrasil
local.publisher.departmentICX - DEPARTAMENTO DE CIÊNCIA DA COMPUTAÇÃO
local.publisher.initialsUFMG
local.subject.cnpqCIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
local.url.externahttps://www.mdpi.com/2078-2489/15/5/251

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