Navigating market sentiments: a novel approach to iron ore price forecasting with weighted fuzzy time series
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Universidade Federal de Minas Gerais
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Artigo de periódico
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Membros da banca
Resumo
The 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.
Abstract
Assunto
Série temporal, Minério de ferro, Inteligência artificial, Previsão de preços- minério
Palavras-chave
Machine learning, Time series, Natural language processing, Iron ore
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https://www.mdpi.com/2078-2489/15/5/251