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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:

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

Showing 21 citing articles:

Enhancing state of charge and state of energy estimation in Lithium-ion batteries based on a TimesNet model with Gaussian data augmentation and error correction
Chu Zhang, Yue Zhang, Zhengbo Li, et al.
Applied Energy (2024) Vol. 359, pp. 122669-122669
Closed Access | Times Cited: 16
Multistep short-term wind power forecasting model based on secondary decomposition, the kernel principal component analysis, an enhanced arithmetic optimization algorithm, and error correction
Guolian Hou, Junjie Wang, Yuzhen Fan
Energy (2023) Vol. 286, pp. 129640-129640
Closed Access | Times Cited: 37
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
The Application of a BiGRU Model with Transformer-Based Error Correction in Deformation Prediction for Bridge SHM
Xu Wang, Xie Guilin, Youjia Zhang, et al.
Buildings (2025) Vol. 15, Iss. 4, pp. 542-542
Open Access
How good are different machine and deep learning models in forecasting the future price of metals? Full sample versus sub-sample
A.S. Amirtha Varshini, Parthajit Kayal, Moinak Maiti
Resources Policy (2024) Vol. 92, pp. 105040-105040
Open Access | Times Cited: 3
MFTM-Informer: A multi-step prediction model based on multivariate fuzzy trend matching and Informer
Lu‐Tao Zhao, Yue Li, Xue-Hui Chen, et al.
Information Sciences (2024) Vol. 681, pp. 121268-121268
Closed Access | Times Cited: 3
A novel non-ferrous metal price hybrid forecasting model based on data preprocessing and error correction
Zhichao He, J. S. Huang
Resources Policy (2023) Vol. 86, pp. 104189-104189
Closed Access | Times Cited: 9
Reliable novel hybrid extreme gradient boosting for forecasting copper prices using meta-heuristic algorithms: A thirty-year analysis
Zohre Nabavi, Mohammad Mirzehi, Hesam Dehghani
Resources Policy (2024) Vol. 90, pp. 104784-104784
Closed Access | Times Cited: 3
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
Jianguo Zhou, Zhongtian Xu
Resources Policy (2022) Vol. 80, pp. 103148-103148
Closed Access | Times Cited: 12
Forecasting energy spot prices: A multiscale clustering recognition approach
Ranran Li
Resources Policy (2023) Vol. 81, pp. 103320-103320
Closed Access | Times Cited: 7
Bionic-inspired oil price prediction: Auditory multi-feature collaboration network
Han Wu, Yan Liang, Xiao‐Zhi Gao, et al.
Expert Systems with Applications (2023) Vol. 244, pp. 122971-122971
Closed Access | Times Cited: 5
Evaluating the Effectiveness of Modern Forecasting Models in Predicting Commodity Futures Prices in Volatile Economic Times
László Vancsura, Tibor Tatay, Tibor Bareith
Risks (2023) Vol. 11, Iss. 2, pp. 27-27
Open Access | Times Cited: 5
Volatility index prediction based on a hybrid deep learning system with multi-objective optimization and mode decomposition
Chaonan Tian, Tong Niu, Wei Wei
Expert Systems with Applications (2022) Vol. 213, pp. 119184-119184
Closed Access | Times Cited: 8
Forecasting Copper Prices Using Deep Learning: Implications for Energy Sector Economies
Reza Derakhshani, Amin GhasemiNejad, Naeeme Amani Zarin, et al.
Mathematics (2024) Vol. 12, Iss. 15, pp. 2316-2316
Open Access | Times Cited: 1
EV load forecasting using a refined CNN-LSTM-AM
Juan Ran, Yunbo Gong, Yu Hu, et al.
Electric Power Systems Research (2024) Vol. 238, pp. 111091-111091
Closed Access | Times Cited: 1
A Novel System Based on Selection Strategy and Ensemble Mode for Non-Ferrous Metal Futures Market Management
Sibo Yang, Wendong Yang, Kai Zhang, et al.
Systems (2023) Vol. 11, Iss. 2, pp. 55-55
Open Access | Times Cited: 2
Crude oil price forecasting: An ensemble‐driven long short‐term memory model based on CEEMDAN decomposition and ALS‐PSO optimization
Xiaoyi Wang, Pei Yang, Xiaofeng Zhou, et al.
Energy Science & Engineering (2023) Vol. 11, Iss. 11, pp. 4054-4076
Open Access | Times Cited: 1
Reliable Novel Hybrid Extreme Gradient Boosting for Forecasting Copper Prices Using Meta-Heuristic Algorithms: A Thirty-Year Analysis
Zohre Nabavi, Mohammad Mirzehi, Hesam Dehghani
(2023)
Closed Access
Comparación de algoritmos de Deep Learning para pronósticos en los precios de criptomonedas
Luis Miguel Jiménez Gómez, Erick Lambis-Alandete, Juan D. Velásquez-Henao
Ingeniería y Competitividad (2023) Vol. 25, Iss. 3
Open Access
Copper price prediction using LSTM recurrent neural network integrated simulated annealing algorithm
Jiahao Chen, Jiahui Yi, Kailei Liu, et al.
PLoS ONE (2023) Vol. 18, Iss. 10, pp. e0285631-e0285631
Open Access
A mesterséges intelligencia szerepe a fenntartható gazdasági döntésekben

Soproni Egyetem Kiadó eBooks (2023)
Open Access
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