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Forecasting copper prices by decision tree learning

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  1. Díaz-Borrego, Francisco J. & Escobar-Peréz, Bernabé & Miras-Rodríguez, María del Mar, 2021. "Estimating copper concentrates benchmark prices under dynamic market conditions," Resources Policy, Elsevier, vol. 70(C).
  2. Henriques, Irene & Sadorsky, Perry, 2023. "Forecasting rare earth stock prices with machine learning," Resources Policy, Elsevier, vol. 86(PA).
  3. Pin Li & Jinsuo Zhang, 2019. "Is China’s Energy Supply Sustainable? New Research Model Based on the Exponential Smoothing and GM(1,1) Methods," Energies, MDPI, vol. 12(2), pages 1-30, January.
  4. Ardian, Aldin & Kumral, Mustafa, 2020. "Incorporating stochastic correlations into mining project evaluation using the Jacobi process," Resources Policy, Elsevier, vol. 65(C).
  5. Li, Ning & Li, Jiaojiao & Wang, Qizhou & Yan, Dairong & Wang, Liguan & Jia, Mingtao, 2024. "A novel copper price forecasting ensemble method using adversarial interpretive structural model and sparrow search algorithm," Resources Policy, Elsevier, vol. 91(C).
  6. Zhang, Hong & Nguyen, Hoang & Bui, Xuan-Nam & Pradhan, Biswajeet & Mai, Ngoc-Luan & Vu, Diep-Anh, 2021. "Proposing two novel hybrid intelligence models for forecasting copper price based on extreme learning machine and meta-heuristic algorithms," Resources Policy, Elsevier, vol. 73(C).
  7. Kwas, Marek & Paccagnini, Alessia & Rubaszek, Michał, 2021. "Common factors and the dynamics of industrial metal prices. A forecasting perspective," Resources Policy, Elsevier, vol. 74(C).
  8. Desta Bati Nuno, 2022. "Analysis of the Causal Relationship between Precious Metal Prices and Inflation: Evidence from Ethiopia," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(1), pages 77-88.
  9. Zhang, Hong & Nguyen, Hoang & Vu, Diep-Anh & Bui, Xuan-Nam & Pradhan, Biswajeet, 2021. "Forecasting monthly copper price: A comparative study of various machine learning-based methods," Resources Policy, Elsevier, vol. 73(C).
  10. Bielak, Łukasz & Grzesiek, Aleksandra & Janczura, Joanna & Wyłomańska, Agnieszka, 2021. "Market risk factors analysis for an international mining company. Multi-dimensional, heavy-tailed-based modelling," Resources Policy, Elsevier, vol. 74(C).
  11. Drachal, Krzysztof, 2019. "Forecasting prices of selected metals with Bayesian data-rich models," Resources Policy, Elsevier, vol. 64(C).
  12. Du, Pei & Wang, Jianzhou & Yang, Wendong & Niu, Tong, 2020. "Point and interval forecasting for metal prices based on variational mode decomposition and an optimized outlier-robust extreme learning machine," Resources Policy, Elsevier, vol. 69(C).
  13. He, Zhichao & Huang, Jianhua, 2023. "A novel non-ferrous metal price hybrid forecasting model based on data preprocessing and error correction," Resources Policy, Elsevier, vol. 86(PB).
  14. Alameer, Zakaria & Elaziz, Mohamed Abd & Ewees, Ahmed A. & Ye, Haiwang & Jianhua, Zhang, 2019. "Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm," Resources Policy, Elsevier, vol. 61(C), pages 250-260.
  15. Liu, Kailei & Cheng, Jinhua & Yi, Jiahui, 2022. "Copper price forecasted by hybrid neural network with Bayesian Optimization and wavelet transform," Resources Policy, Elsevier, vol. 75(C).
  16. Su, Chi-Wei & Wang, Xiao-Qing & Zhu, Haotian & Tao, Ran & Moldovan, Nicoleta-Claudia & Lobonţ, Oana-Ramona, 2020. "Testing for multiple bubbles in the copper price: Periodically collapsing behavior," Resources Policy, Elsevier, vol. 65(C).
  17. Du, Pei & Guo, Ju’e & Sun, Shaolong & Wang, Shouyang & Wu, Jing, 2021. "Multi-step metal prices forecasting based on a data preprocessing method and an optimized extreme learning machine by marine predators algorithm," Resources Policy, Elsevier, vol. 74(C).
  18. Pincheira Brown, Pablo & Hardy, Nicolás, 2019. "Forecasting base metal prices with the Chilean exchange rate," Resources Policy, Elsevier, vol. 62(C), pages 256-281.
  19. Luo, Hongyuan & Wang, Deyun & Cheng, Jinhua & Wu, Qiaosheng, 2022. "Multi-step-ahead copper price forecasting using a two-phase architecture based on an improved LSTM with novel input strategy and error correction," Resources Policy, Elsevier, vol. 79(C).
  20. Liu, Weiping & Wang, Chengzhu & Li, Yonggang & Liu, Yishun & Huang, Keke, 2021. "Ensemble forecasting for product futures prices using variational mode decomposition and artificial neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
  21. Wang, Chao & Zhang, Xinyi & Wang, Minggang & Lim, Ming K. & Ghadimi, Pezhman, 2019. "Predictive analytics of the copper spot price by utilizing complex network and artificial neural network techniques," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
  22. Reza Derakhshani & Amin GhasemiNejad & Naeeme Amani Zarin & Mohammad Mahdi Amani Zarin & Mahdis sadat Jalaee, 2024. "Forecasting Copper Prices Using Deep Learning: Implications for Energy Sector Economies," Mathematics, MDPI, vol. 12(15), pages 1-10, July.
  23. Zhao, Jue & Hosseini, Shahab & Chen, Qinyang & Jahed Armaghani, Danial, 2023. "Super learner ensemble model: A novel approach for predicting monthly copper price in future," Resources Policy, Elsevier, vol. 85(PB).
  24. Yifei Zhao & Jianhong Chen & Hideki Shimada & Takashi Sasaoka, 2023. "Non-Ferrous Metal Price Point and Interval Prediction Based on Variational Mode Decomposition and Optimized LSTM Network," Mathematics, MDPI, vol. 11(12), pages 1-16, June.
  25. 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).
  26. Shen, Junjie & Huang, Shupei, 2022. "Copper cross-market volatility transition based on a coupled hidden Markov model and the complex network method," Resources Policy, Elsevier, vol. 75(C).
  27. Ewees, Ahmed A. & Elaziz, Mohamed Abd & Alameer, Zakaria & Ye, Haiwang & Jianhua, Zhang, 2020. "Improving multilayer perceptron neural network using chaotic grasshopper optimization algorithm to forecast iron ore price volatility," Resources Policy, Elsevier, vol. 65(C).
  28. Esangbedo, Moses Olabhele & Taiwo, Blessing Olamide & Abbas, Hawraa H. & Hosseini, Shahab & Sazid, Mohammed & Fissha, Yewuhalashet, 2024. "Enhancing the exploitation of natural resources for green energy: An application of LSTM-based meta-model for aluminum prices forecasting," Resources Policy, Elsevier, vol. 92(C).
  29. Liu, Qing & Liu, Min & Zhou, Hanlu & Yan, Feng, 2022. "A multi-model fusion based non-ferrous metal price forecasting," Resources Policy, Elsevier, vol. 77(C).
  30. Cifuentes, Sebastián & Cortazar, Gonzalo & Ortega, Hector & Schwartz, Eduardo S., 2020. "Expected prices, futures prices and time-varying risk premiums: The case of copper," Resources Policy, Elsevier, vol. 69(C).
  31. Becerra, Miguel & Jerez, Alejandro & Garcés, Hugo O. & Demarco, Rodrigo, 2022. "Copper price: A brief analysis of China’s impact over its short-term forecasting," Resources Policy, Elsevier, vol. 75(C).
  32. Parviz Sohrabi & Hesam Dehghani & Behshad Jodeiri Shokri, 2022. "Determination of optimal production rate under price uncertainty—Sari Gunay gold mine, Iran," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 35(2), pages 187-201, June.
  33. Fernandez, Viviana & Pastén-Henríquez, Boris & Tapia-Griñen, Pablo & Wagner, Rodrigo, 2023. "Commodity prices under the threat of operational disruptions: Labor strikes at copper mines," Journal of Commodity Markets, Elsevier, vol. 32(C).
  34. Zhu, Yongguang & Xu, Deyi & Cheng, Jinhua & Ali, Saleem Hassan, 2018. "Estimating the impact of China's export policy on tin prices: a mode decomposition counterfactual analysis method," Resources Policy, Elsevier, vol. 59(C), pages 250-264.
  35. Dehghani, Hesam & Bogdanovic, Dejan, 2018. "Copper price estimation using bat algorithm," Resources Policy, Elsevier, vol. 55(C), pages 55-61.
  36. Rubaszek, Michał & Karolak, Zuzanna & Kwas, Marek, 2020. "Mean-reversion, non-linearities and the dynamics of industrial metal prices. A forecasting perspective," Resources Policy, Elsevier, vol. 65(C).
  37. Yang, Mo & Wang, Ruotong & Zeng, Zixun & Li, Peizhi, 2024. "Improved prediction of global gold prices: An innovative Hurst-reconfiguration-based machine learning approach," Resources Policy, Elsevier, vol. 88(C).
  38. Pincheira, Pablo & Hardy, Nicolas, 2018. "Forecasting Base Metal Prices with Commodity Currencies," MPRA Paper 83564, University Library of Munich, Germany.
  39. Khoshalan, Hasel Amini & Shakeri, Jamshid & Najmoddini, Iraj & Asadizadeh, Mostafa, 2021. "Forecasting copper price by application of robust artificial intelligence techniques," Resources Policy, Elsevier, vol. 73(C).
  40. Nabavi, Zohre & Mirzehi, Mohammad & Dehghani, Hesam, 2024. "Reliable novel hybrid extreme gradient boosting for forecasting copper prices using meta-heuristic algorithms: A thirty-year analysis," Resources Policy, Elsevier, vol. 90(C).
  41. Krzysztof Drachal, 2018. "Some Novel Bayesian Model Combination Schemes: An Application to Commodities Prices," Sustainability, MDPI, vol. 10(8), pages 1-27, August.
  42. Huang, Yu-ting & Bai, Yu-long & Yu, Qing-he & Ding, Lin & Ma, Yong-jie, 2022. "Application of a hybrid model based on the Prophet model, ICEEMDAN and multi-model optimization error correction in metal price prediction," Resources Policy, Elsevier, vol. 79(C).
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