A novel grey wave forecasting method for predicting metal prices

Citations

Cited by:

1. Henriques, Irene & Sadorsky, Perry, 2023. "Forecasting rare earth stock prices with machine learning," Resources Policy, Elsevier, vol. 86(PA).
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   • {L}ukasz Bielak & Aleksandra Grzesiek & Joanna Janczura & Agnieszka Wy{l}oma'nska, 2021. "Market risk factors analysis for an international mining company. Multi-dimensional, heavy-tailed-based modelling," Papers 2107.07142, arXiv.org.
4. 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).
5. Zhou, Jianguo & Xu, Zhongtian, 2023. "A novel three-stage hybrid learning paradigm based on a multi-decomposition strategy, optimized relevance vector machine, and error correction for multi-step forecasting of precious metal prices," Resources Policy, Elsevier, vol. 80(C).
6. 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.
7. Kedong Yin & Danning Lu & Xuemei Li, 2017. "A Novel Grey Wave Method for Predicting Total Chinese Trade Volume," Sustainability, MDPI, vol. 9(12), pages 1-16, December.
8. 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.
9. Biswas, Pritam & Sinha, Rabindra Kumar & Sen, Phalguni, 2023. "A review of state-of-the-art techniques for the determination of the optimum cut-off grade of a metalliferous deposit with a bibliometric mapping in a surface mine planning context," Resources Policy, Elsevier, vol. 83(C).
10. 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).
11. 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).
12. Li, Ranran, 2023. "Forecasting energy spot prices: A multiscale clustering recognition approach," Resources Policy, Elsevier, vol. 81(C).
13. Chen, Yanhui & Zhang, Chuan & He, Kaijian & Zheng, Aibing, 2018. "Multi-step-ahead crude oil price forecasting using a hybrid grey wave model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 98-110.
14. 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).
15. Konstantinos Oikonomou & Dimitris Damigos, 2025. "Short term forecasting of base metals prices using a LightGBM and a LightGBM - ARIMA ensemble," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 38(1), pages 37-49, March.
16. Peng Jiang & Yi-Chung Hu & Wenbao Wang & Hang Jiang & Geng Wu, 2020. "Interval Grey Prediction Models with Forecast Combination for Energy Demand Forecasting," Mathematics, MDPI, vol. 8(6), pages 1-12, June.
17. 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).
18. 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).
19. 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).
20. Yu, Hui & Ding, Yinghui & Sun, Qingru & Gao, Xiangyun & Jia, Xiaoliang & Wang, Xinya & Guo, Sui, 2021. "Multi-scale comovement of the dynamic correlations between copper futures and spot prices," Resources Policy, Elsevier, vol. 70(C).
21. 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.
22. Dehghani, Hesam & Bogdanovic, Dejan, 2018. "Copper price estimation using bat algorithm," Resources Policy, Elsevier, vol. 55(C), pages 55-61.
23. 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).
24. Wang, Jianzhou & Niu, Xinsong & Zhang, Linyue & Lv, Mengzheng, 2021. "Point and interval prediction for non-ferrous metals based on a hybrid prediction framework," Resources Policy, Elsevier, vol. 73(C).
25. 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).
26. Yi-Chung Hu, 2017. "Nonadditive Grey Prediction Using Functional-Link Net for Energy Demand Forecasting," Sustainability, MDPI, vol. 9(7), pages 1-14, July.