Proposing two novel hybrid intelligence models for forecasting copper price based on extreme learning machine and meta-heuristic algorithms
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DOI: 10.1016/j.resourpol.2021.102195
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- Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2020. "A random walk through the trees: Forecasting copper prices using decision learning methods," Resources Policy, Elsevier, vol. 69(C).
- Zhao, Yang & Li, Jianping & Yu, Lean, 2017. "A deep learning ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 9-16.
- Dehghani, Hesam & Bogdanovic, Dejan, 2018. "Copper price estimation using bat algorithm," Resources Policy, Elsevier, vol. 55(C), pages 55-61.
- Rafal Weron & Florian Ziel, 2018.
"Electricity price forecasting,"
HSC Research Reports
HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Technology.
- Katarzyna Maciejowska & Rafal Weron, 2019. "Electricity price forecasting," HSC Research Reports HSC/19/01, Hugo Steinhaus Center, Wroclaw University of Technology.
- Liu, Chang & Hu, Zhenhua & Li, Yan & Liu, Shaojun, 2017. "Forecasting copper prices by decision tree learning," Resources Policy, Elsevier, vol. 52(C), pages 427-434.
- Buncic, Daniel & Moretto, Carlo, 2015.
"Forecasting copper prices with dynamic averaging and selection models,"
The North American Journal of Economics and Finance, Elsevier, vol. 33(C), pages 1-38.
- Buncic, Daniel & Moretto, Carlo, 2014. "Forecasting Copper Prices with Dynamic Averaging and Selection Models," Economics Working Paper Series 1430, University of St. Gallen, School of Economics and Political Science.
- Nowotarski, Jakub & Weron, Rafał, 2018.
"Recent advances in electricity price forecasting: A review of probabilistic forecasting,"
Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
- Jakub Nowotarski & Rafal Weron, 2016. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," HSC Research Reports HSC/16/07, Hugo Steinhaus Center, Wroclaw University of Technology.
- Weron, Rafał, 2014.
"Electricity price forecasting: A review of the state-of-the-art with a look into the future,"
International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
- Rafal Weron, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," HSC Research Reports HSC/14/07, Hugo Steinhaus Center, Wroclaw University of Technology.
- Tapia, Carlos & Coulton, Jeff & Saydam, Serkan, 2020. "Using entropy to assess dynamic behaviour of long-term copper price," Resources Policy, Elsevier, vol. 66(C).
- Luan, Jing & Yao, Zhong & Zhao, Futao & Song, Xin, 2019. "A novel method to solve supplier selection problem: Hybrid algorithm of genetic algorithm and ant colony optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 156(C), pages 294-309.
- Hu, Yan & Ni, Jian & Wen, Liu, 2020. "A hybrid deep learning approach by integrating LSTM-ANN networks with GARCH model for copper price volatility prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
- Wang, Deyun & Luo, Hongyuan & Grunder, Olivier & Lin, Yanbing & Guo, Haixiang, 2017. "Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm," Applied Energy, Elsevier, vol. 190(C), pages 390-407.
- Zhang, Hong & Nguyen, Hoang & Bui, Xuan-Nam & Nguyen-Thoi, Trung & Bui, Thu-Thuy & Nguyen, Nga & Vu, Diep-Anh & Mahesh, Vinyas & Moayedi, Hossein, 2020. "Developing a novel artificial intelligence model to estimate the capital cost of mining projects using deep neural network-based ant colony optimization algorithm," Resources Policy, Elsevier, vol. 66(C).
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- Zheng, Shuxian & Tan, Zhanglu & Xing, Wanli & Zhou, Xuanru & Zhao, Pei & Yin, Xiuqi & Hu, Han, 2022. "A comparative exploration of the chaotic characteristics of Chinese and international copper futures prices," Resources Policy, Elsevier, vol. 78(C).
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Keywords
Copper price; Forecasting price; Natural resources; Extreme learning machine; Optimization algorithms; Hybrid models;All these keywords.
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