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OpenAlex Citation Counts

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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 improved wavelet–ARIMA approach for forecasting metal prices
Thomas Kriechbaumer, Andrew Angus, David Parsons, et al.
Resources Policy (2013) Vol. 39, pp. 32-41
Open Access | Times Cited: 161

Showing 26-50 of 161 citing articles:

Price forecasting in the precious metal market: A multivariate EMD denoising approach
Kaijian He, Yanhui Chen, Kwok Fai Tso
Resources Policy (2017) Vol. 54, pp. 9-24
Closed Access | Times Cited: 58
A novel grey wave forecasting method for predicting metal prices
Yanhui Chen, Kaijian He, Chuan Zhang
Resources Policy (2016) Vol. 49, pp. 323-331
Closed Access | Times Cited: 55
Improving Adaptive Neuro-Fuzzy Inference System Based on a Modified Salp Swarm Algorithm Using Genetic Algorithm to Forecast Crude Oil Price
Mohamed Abd Elaziz, Ahmed A. Ewees, Zakaria Alameer
Natural Resources Research (2019) Vol. 29, Iss. 4, pp. 2671-2686
Closed Access | Times Cited: 54
A novel method based on numerical fitting for oil price trend forecasting
Lu‐Tao Zhao, Yi Wang, Shi-Qiu Guo, et al.
Applied Energy (2018) Vol. 220, pp. 154-163
Closed Access | Times Cited: 52
Nickel Price Forecast Based on the LSTM Neural Network Optimized by the Improved PSO Algorithm
Bilin Shao, Maolin Li, Yu Zhao, et al.
Mathematical Problems in Engineering (2019) Vol. 2019, Iss. 1
Open Access | Times Cited: 49
Point and interval prediction for non-ferrous metals based on a hybrid prediction framework
Jianzhou Wang, Xinsong Niu, Linyue Zhang, et al.
Resources Policy (2021) Vol. 73, pp. 102222-102222
Closed Access | Times Cited: 37
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
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: 27
A novel hybrid model integrating modified ensemble empirical mode decomposition and LSTM neural network for multi-step precious metal prices prediction
Yu Lin, Qidong Liao, Zixiao Lin, et al.
Resources Policy (2022) Vol. 78, pp. 102884-102884
Closed Access | Times Cited: 25
Forecasting rare earth stock prices with machine learning
Irene Henriques, Perry Sadorsky
Resources Policy (2023) Vol. 86, pp. 104248-104248
Closed Access | Times Cited: 13
A novel copper price forecasting ensemble method using adversarial interpretive structural model and sparrow search algorithm
LI Nin, Jiaojiao Li, Qizhou Wang, et al.
Resources Policy (2024) Vol. 91, pp. 104892-104892
Closed Access | Times Cited: 5
Spillovers, integration and causality in LME non-ferrous metal markets
Cetin Ciner, Brian M. Lucey, Larisa Yarovaya
Journal of commodity markets (2018) Vol. 17, pp. 100079-100079
Open Access | Times Cited: 44
A weighted EMD-based prediction model based on TOPSIS and feed forward neural network for noised time series
Wang Jun, Tang Lingyu, Yuyan Luo, et al.
Knowledge-Based Systems (2017) Vol. 132, pp. 167-178
Closed Access | Times Cited: 43
Testing for multiple bubbles in the copper price: Periodically collapsing behavior
Chi‐Wei Su, Xiaoqing Wang, Haotian Zhu, et al.
Resources Policy (2020) Vol. 65, pp. 101587-101587
Open Access | Times Cited: 39
A multi-model fusion based non-ferrous metal price forecasting
Qing Liu, Min Liu, Hanlu Zhou, et al.
Resources Policy (2022) Vol. 77, pp. 102714-102714
Closed Access | Times Cited: 19
Prediction of COVID-19 Data Using Improved ARIMA-LSTM Hybrid Forecast Models
Yongchao Jin, Qian Cao, Kenan Wang, et al.
IEEE Access (2023) Vol. 11, pp. 67956-67967
Open Access | Times Cited: 11
Response of groundwater levels to ENSO under the influence of mining
Xiaoping Zhou, Shan He, Honghui Sang, et al.
Frontiers in Earth Science (2025) Vol. 13
Open Access
Globality in the metal markets: Leveraging cross-learning to forecast aluminum and copper prices
Konstantinos Oikonomou, D. Damigos, Dimitrios Dimitriou
Resources Policy (2025) Vol. 103, pp. 105558-105558
Closed Access
A metal price forecasting framework optimized with TLBO metaheuristic-algorithm and selective for highest returns
Shih‐Hsien Tseng, Manh-Hung Nguyen
Expert Systems with Applications (2025), pp. 127937-127937
Closed Access
Forecasting Based on Decomposed Financial Return Series: A Wavelet Analysis
Theo Berger
Journal of Forecasting (2015) Vol. 35, Iss. 5, pp. 419-433
Closed Access | Times Cited: 37
Investment in new tungsten mining projects
Ana Suárez Sánchez, Alicja Krzemień, Pedro Riesgo Fernández, et al.
Resources Policy (2015) Vol. 46, pp. 177-190
Open Access | Times Cited: 35
Comparison of Forecasting India’s Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model
Feng Jiang, Xue Yang, Shuyu Li
Sustainability (2018) Vol. 10, Iss. 7, pp. 2225-2225
Open Access | Times Cited: 35
How does the anthropogenic activity affect the spring discharge?
Yonghong Hao, Juan Zhang, Jiaojiao Wang, et al.
Journal of Hydrology (2016) Vol. 540, pp. 1053-1065
Closed Access | Times Cited: 34
Economic and Technological Analysis of Commercial LNG Production in the EU
Vladimír Hönig, Petr Procházka, Michal Obergruber, et al.
Energies (2019) Vol. 12, Iss. 8, pp. 1565-1565
Open Access | Times Cited: 34
Point and interval forecasting for metal prices based on variational mode decomposition and an optimized outlier-robust extreme learning machine
Pei Du, Jianzhou Wang, Wendong Yang, et al.
Resources Policy (2020) Vol. 69, pp. 101881-101881
Closed Access | Times Cited: 31

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