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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

An adaptive forecasting approach for copper price volatility through hybrid and non-hybrid models
Diego Garcı́a, Werner Kristjanpoller
Applied Soft Computing (2018) Vol. 74, pp. 466-478
Closed Access | Times Cited: 67

Showing 1-25 of 67 citing articles:

Non-ferrous metals price forecasting based on variational mode decomposition and LSTM network
Yishun Liu, Chunhua Yang, Keke Huang, et al.
Knowledge-Based Systems (2019) Vol. 188, pp. 105006-105006
Closed Access | Times Cited: 219
Machine learning model for Bitcoin exchange rate prediction using economic and technology determinants
Wei Chen, Huilin Xu, Lifen Jia, et al.
International Journal of Forecasting (2020) Vol. 37, Iss. 1, pp. 28-43
Closed Access | Times Cited: 172
Forecasting Volatility of Australian Stock Market Applying WTC‐DCA‐Informer Framework
Hongjun Zeng, Ran Wu, Mohammad Zoynul Abedin, et al.
Journal of Forecasting (2025)
Closed Access | Times Cited: 2
A hybrid VMD–BiGRU model for rubber futures time series forecasting
Qing Zhu, Fan Zhang, Shan Liu, et al.
Applied Soft Computing (2019) Vol. 84, pp. 105739-105739
Closed Access | Times Cited: 113
A hybrid deep learning approach by integrating LSTM-ANN networks with GARCH model for copper price volatility prediction
Yan Hu, Jian Ni, Wen Liu
Physica A Statistical Mechanics and its Applications (2020) Vol. 557, pp. 124907-124907
Closed Access | Times Cited: 111
Forecasting Copper Prices Using Hybrid Adaptive Neuro-Fuzzy Inference System and Genetic Algorithms
Zakaria Alameer, Mohamed Abd Elaziz, Ahmed A. Ewees, et al.
Natural Resources Research (2019) Vol. 28, Iss. 4, pp. 1385-1401
Closed Access | Times Cited: 83
Improving multilayer perceptron neural network using chaotic grasshopper optimization algorithm to forecast iron ore price volatility
Ahmed A. Ewees, Mohamed Abd Elaziz, Zakaria Alameer, et al.
Resources Policy (2019) Vol. 65, pp. 101555-101555
Closed Access | Times Cited: 82
Developing a novel artificial intelligence model to estimate the capital cost of mining projects using deep neural network-based ant colony optimization algorithm
Hong Zhang, Hoang Nguyen, Xuan-Nam Bui, et al.
Resources Policy (2020) Vol. 66, pp. 101604-101604
Closed Access | Times Cited: 77
Hodrick–Prescott filter-based hybrid ARIMA–SLFNs model with residual decomposition scheme for carbon price forecasting
Quande Qin, Zhaorong Huang, Zhihao Zhou, et al.
Applied Soft Computing (2022) Vol. 119, pp. 108560-108560
Closed Access | Times Cited: 52
Deep Learning for Financial Markets: A Case-Based Analysis of BRICS Nations in the Era of Intelligent Forecasting
Shake Ibna Abir, Mohammad Hasan Sarwer, Mahmud Hasan, et al.
Journal of Economics Finance and Accounting Studies (2025) Vol. 7, Iss. 1, pp. 01-15
Open Access | Times Cited: 1
Predictive analytics of the copper spot price by utilizing complex network and artificial neural network techniques
Chao Wang, Xinyi Zhang, Minggang Wang, et al.
Resources Policy (2019) Vol. 63, pp. 101414-101414
Closed Access | Times Cited: 57
A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities
Krzysztof Drachal, Michal E. Pawlowski
Economies (2021) Vol. 9, Iss. 1, pp. 6-6
Open Access | Times Cited: 44
Forecasting Foreign Exchange Volatility Using Deep Learning Autoencoder‐LSTM Techniques
Gunho Jung, Sun‐Yong Choi
Complexity (2021) Vol. 2021, Iss. 1
Open Access | Times Cited: 43
Forecasting monthly copper price: A comparative study of various machine learning-based methods
Hong Zhang, Hoang Nguyen, Diep-Anh Vu, et al.
Resources Policy (2021) Vol. 73, pp. 102189-102189
Closed Access | Times Cited: 43
Fuzzy Rule-Based Prediction of Gold Prices using News Affect
Petr Hájek, Josef Novotný
Expert Systems with Applications (2022) Vol. 193, pp. 116487-116487
Open Access | Times Cited: 38
Enhancing multilayer perceptron neural network using archive-based harris hawks optimizer to predict gold prices
Iyad Abu Doush, Basem H.A. Ahmed, Mohammed A. Awadallah, et al.
Journal of King Saud University - Computer and Information Sciences (2023) Vol. 35, Iss. 5, pp. 101557-101557
Open Access | Times Cited: 19
Development of copper price from July 1959 and predicted development till the end of year 2022
Marek Vochоzka, Eva Kalinová, Peng Gao, et al.
Acta Montanistica Slovaca (2021), Iss. 26, pp. 262-280
Open Access | Times Cited: 37
Forecasting copper price by application of robust artificial intelligence techniques
Hasel Amini Khoshalan, Jamshid Shakeri, Iraj Najmoddini, et al.
Resources Policy (2021) Vol. 73, pp. 102239-102239
Closed Access | Times Cited: 36
Multi-step metal prices forecasting based on a data preprocessing method and an optimized extreme learning machine by marine predators algorithm
Pei Du, Ju’e Guo, Shaolong Sun, et al.
Resources Policy (2021) Vol. 74, pp. 102335-102335
Closed Access | Times Cited: 35
Copper and Aluminium as Economically Imperfect Substitutes, Production and Price Development
Vojtěch Bartoš, Marek Vochоzka, Veronika Šanderová
Acta Montanistica Slovaca (2022), Iss. 27, pp. 462-478
Open Access | Times Cited: 28
What do the AI methods tell us about predicting price volatility of key natural resources: Evidence from hyperparameter tuning
Mrinalini Srivastava, Amar Rao, Jaya Singh Parihar, et al.
Resources Policy (2022) Vol. 80, pp. 103249-103249
Closed Access | Times Cited: 27
Multi-Objective Ensemble Model for Short-Term Price Forecasting in Corn Price Time Series
Matheus Henrique Dal Molin Ribeiro, Víctor Henrique Alves Ribeiro, Gilberto Reynoso-Meza, et al.
2022 International Joint Conference on Neural Networks (IJCNN) (2019), pp. 1-8
Closed Access | Times Cited: 39
Scenario simulation of the EU carbon price and its enlightenment to China
Zhaopeng Li, Li Yang, Zhou Yinan, et al.
The Science of The Total Environment (2020) Vol. 723, pp. 137982-137982
Closed Access | Times Cited: 37
Multi-step-ahead copper price forecasting using a two-phase architecture based on an improved LSTM with novel input strategy and error correction
Hongyuan Luo, Deyun Wang, Jinhua Cheng, et al.
Resources Policy (2022) Vol. 79, pp. 102962-102962
Closed Access | Times Cited: 21
Copper Price Prediction Using Support Vector Regression Technique
Gabriel Astudillo, Raúl Carrasco, Christian Fernández‐Campusano, et al.
Applied Sciences (2020) Vol. 10, Iss. 19, pp. 6648-6648
Open Access | Times Cited: 31
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