Navigating Market Sentiments: A Novel Approach to Iron Ore Price Forecasting with Weighted Fuzzy Time Series

被引：3

作者：

Souza, Flavio Mauricio da Cunha ^{[1
]}

Rocha Filho, Geraldo Pereira ^{[2
]}

Guimaraes, Frederico Gadelha ^{[3
]}

Meneguette, Rodolfo I. ^{[4
]}

Pessin, Gustavo ^{[5
]}

机构：

[1] Univ Fed Ouro Preto, Postgrad Program Instrumentat Control & Automat Mi, BR-35400000 Ouro Preto, Brazil

[2] State Univ Southwest Bahia, Dept Exact & Technol Sci, BR-45083900 Vitoria Da Conquista, Brazil

[3] Univ Fed Minas Gerais, Comp Sci Dept, BR-31270901 Belo Horizonte, Brazil

[4] Univ Sao Paulo, Inst Math & Comp Sci, BR-13566590 Sao Carlos, Brazil

[5] Vale Technol Inst, BR-35400000 Ouro Preto, Brazil

来源：

INFORMATION | 2024年 / 15卷 / 05期

关键词：

machine learning; time series; natural language processing; iron ore;

D O I：

10.3390/info15050251

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页数：18

共 24 条

[1]

Alves D.S., 2015, Ph.D. Thesis

[2] Forecasting with Twitter Data [J].

Arias, Marta ;

Arratia, Argimiro ;

Xuriguera, Ramon .

ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2013, 5 (01)

[3] Fuzzy time series model based on weighted association rule for financial market forecasting [J].

Cheng, Ching-Hsue ;

Chen, Chung-Hsi .

EXPERT SYSTEMS, 2018, 35 (04)

[4]

de Lima P. C., 2019, Scalable models for probabilistic forecasting with fuzzy time series

[5] BERT for Stock Market Sentiment Analysis [J].

de Sousa, Matheus Gomes ;

Sakiyama, Kenzo ;

Rodrigues, Lucas de Souza ;

de Moraes, Pedro Henrique ;

Fernandes, Eraldo Rezende ;

Matsubara, Edson Takashi .

2019 IEEE 31ST INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2019), 2019, :1597-1601

[6]

Devlin J, 2019, Arxiv, DOI [arXiv:1810.04805, DOI 10.48550/ARXIV.1810.04805]

[7]

Dias BCD, 2021, 2021 IEEE WORLD AI IOT CONGRESS (AIIOT), P433, DOI [10.1109/AIIoT52608.2021.9454179, 10.1109/AIIOT52608.2021.9454179]

[8] Improving multilayer perceptron neural network using chaotic grasshopper optimization algorithm to forecast iron ore price volatility [J].

Ewees, Ahmed A. ;

Abd Elaziz, Mohamed ;

Alameer, Zakaria ;

Ye, Haiwang ;

Zhang Jianhua .

RESOURCES POLICY, 2020, 65

[9]

Igarashi W., 2020, Braz. J. Bus, V3, P470, DOI [10.34140/bjbv3n1-029, DOI 10.34140/BJBV3N1-029]

[10]

Jowitt S.M., 2020, SEG Discovery, V122, P33, DOI [10.5382/SEGnews.2020-122.fea-02, DOI 10.5382/SEGNEWS.2020-122.FEA-02]

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