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Forecasting the COMEX copper spot price by means of neural networks and ARIMA models

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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. Erdinc Akyildirim & Oguzhan Cepni & Shaen Corbet & Gazi Salah Uddin, 2023. "Forecasting mid-price movement of Bitcoin futures using machine learning," Annals of Operations Research, Springer, vol. 330(1), pages 553-584, November.
  3. Henriques, Irene & Sadorsky, Perry, 2023. "Forecasting rare earth stock prices with machine learning," Resources Policy, Elsevier, vol. 86(PA).
  4. GRITLI, Mohamed Ilyes, 2018. "Quel avenir du dinar tunisien face à l'euro ? Prévision avec le modèle ARIMA [What future of the Tunisian dinar against the euro? Prediction with the ARIMA model]," MPRA Paper 83937, University Library of Munich, Germany.
  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. Omer Berat Sezer & Mehmet Ugur Gudelek & Ahmet Murat Ozbayoglu, 2019. "Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019," Papers 1911.13288, arXiv.org.
  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. 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).
  9. Drachal, Krzysztof, 2019. "Forecasting prices of selected metals with Bayesian data-rich models," Resources Policy, Elsevier, vol. 64(C).
  10. 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).
  11. 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).
  12. 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.
  13. Marta Matyjaszek & Gregorio Fidalgo Valverde & Alicja Krzemień & Krzysztof Wodarski & Pedro Riesgo Fernández, 2020. "Optimizing Predictor Variables in Artificial Neural Networks When Forecasting Raw Material Prices for Energy Production," Energies, MDPI, vol. 13(8), pages 1-15, April.
  14. 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).
  15. 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).
  16. 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).
  17. 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.
  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. Krzemień, Alicja & Riesgo Fernández, Pedro & Suárez Sánchez, Ana & Diego Álvarez, Isidro, 2016. "Beyond the pan-european standard for reporting of exploration results, mineral resources and reserves," Resources Policy, Elsevier, vol. 49(C), pages 81-91.
  21. 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).
  22. 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.
  23. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2021. "Economic drivers of commodity volatility: The case of copper," Resources Policy, Elsevier, vol. 73(C).
  24. 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).
  25. 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).
  26. Shangkun Deng & Yingke Zhu & Xiaoru Huang & Shuangyang Duan & Zhe Fu, 2022. "High-Frequency Direction Forecasting of the Futures Market Using a Machine-Learning-Based Method," Future Internet, MDPI, vol. 14(6), pages 1-21, June.
  27. Yasir Alsaedi & Gurudeo Anand Tularam & Victor Wong, 2019. "Application of ARIMA Modelling for the Forecasting of Solar, Wind, Spot and Options Electricity Prices: The Australian National Electricity Market," International Journal of Energy Economics and Policy, Econjournals, vol. 9(4), pages 263-272.
  28. 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).
  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. Reus, Lorenzo & Pagnoncelli, Bernardo & Armstrong, Margaret, 2019. "Better management of production incidents in mining using multistage stochastic optimization," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
  33. Tapia, Carlos & Coulton, Jeff & Saydam, Serkan, 2020. "Using entropy to assess dynamic behaviour of long-term copper price," Resources Policy, Elsevier, vol. 66(C).
  34. 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).
  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. Rettinger, Moritz & Mandl, Christian & Minner, Stefan, 2024. "A data-driven approach for optimal operational and financial commodity hedging," European Journal of Operational Research, Elsevier, vol. 316(1), pages 341-360.
  38. Pincheira, Pablo & Hardy, Nicolas, 2018. "Forecasting Base Metal Prices with Commodity Currencies," MPRA Paper 83564, University Library of Munich, Germany.
  39. 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).
  40. Riesgo García, María Victoria & Krzemień, Alicja & Manzanedo del Campo, Miguel Ángel & Escanciano García-Miranda, Carmen & Sánchez Lasheras, Fernando, 2018. "Rare earth elements price forecasting by means of transgenic time series developed with ARIMA models," Resources Policy, Elsevier, vol. 59(C), pages 95-102.
  41. Chou, Jui-Sheng & Ngo, Ngoc-Tri, 2016. "Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns," Applied Energy, Elsevier, vol. 177(C), pages 751-770.
  42. 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).
  43. Herrera, Gabriel Paes & Constantino, Michel & Tabak, Benjamin Miranda & Pistori, Hemerson & Su, Jen-Je & Naranpanawa, Athula, 2019. "Long-term forecast of energy commodities price using machine learning," Energy, Elsevier, vol. 179(C), pages 214-221.
  44. Matyjaszek, Marta & Riesgo Fernández, Pedro & Krzemień, Alicja & Wodarski, Krzysztof & Fidalgo Valverde, Gregorio, 2019. "Forecasting coking coal prices by means of ARIMA models and neural networks, considering the transgenic time series theory," Resources Policy, Elsevier, vol. 61(C), pages 283-292.
  45. Huijian Dong & Xiaomin Guo & Han Reichgelt & Ruizhi Hu, 2020. "Predictive power of ARIMA models in forecasting equity returns: a sliding window method," Journal of Asset Management, Palgrave Macmillan, vol. 21(6), pages 549-566, October.
  46. Buelga Díaz, Arturo & Diego Álvarez, Isidro & Castañón Fernández, César & Krzemień, Alicja & Iglesias Rodríguez, Francisco Javier, 2021. "Calculating ultimate pit limits and determining pushbacks in open-pit mining projects," Resources Policy, Elsevier, vol. 72(C).
  47. Chen, Yanhui & He, Kaijian & Zhang, Chuan, 2016. "A novel grey wave forecasting method for predicting metal prices," Resources Policy, Elsevier, vol. 49(C), pages 323-331.
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