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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:

A novel method for online real-time forecasting of crude oil price
Yuan Zhao, Weiguo Zhang, Xue Gong, et al.
Applied Energy (2021) Vol. 303, pp. 117588-117588
Closed Access | Times Cited: 47

Showing 1-25 of 47 citing articles:

Grid search with a weighted error function: Hyper-parameter optimization for financial time series forecasting
Yuan Zhao, Weiguo Zhang, Xiufeng Liu
Applied Soft Computing (2024) Vol. 154, pp. 111362-111362
Open Access | Times Cited: 32
A dynamic clustering ensemble learning approach for crude oil price forecasting
Jiaxin Yuan, Jianping Li, Jun Hao
Engineering Applications of Artificial Intelligence (2023) Vol. 123, pp. 106408-106408
Closed Access | Times Cited: 23
A blending ensemble learning model for crude oil price forecasting
Mahmudul Hasan, Mohammad Zoynul Abedin, Petr Hájek, et al.
Annals of Operations Research (2024)
Open Access | Times Cited: 15
Carbon futures price forecasting based on feature selection
Yuan Zhao, Yaohui Huang, Zhijin Wang, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 135, pp. 108646-108646
Open Access | Times Cited: 9
Interval prediction approach to crude oil price based on three-way clustering and decomposition ensemble learning
Bingzhen Sun, Juncheng Bai, Xiaoli Chu, et al.
Applied Soft Computing (2022) Vol. 123, pp. 108933-108933
Closed Access | Times Cited: 32
Dynamic spatial–temporal graph convolutional recurrent networks for traffic flow forecasting
Z.H. Xia, Yong Zhang, Jielong Yang, et al.
Expert Systems with Applications (2023) Vol. 240, pp. 122381-122381
Closed Access | Times Cited: 19
Petroleum products consumption forecasting based on a new structural auto-adaptive intelligent grey prediction model
Flavian Emmanuel Sapnken, Jean Gaston Tamba
Expert Systems with Applications (2022) Vol. 203, pp. 117579-117579
Closed Access | Times Cited: 27
Uncertainty index and stock volatility prediction: evidence from international markets
Xue Gong, Weiguo Zhang, Weijun Xu, et al.
Financial Innovation (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 26
Predicting energy futures high-frequency volatility using technical indicators: The role of interaction
Xue Gong, Xin Ye, Weiguo Zhang, et al.
Energy Economics (2023) Vol. 119, pp. 106533-106533
Closed Access | Times Cited: 14
A new feature selection method based on importance measures for crude oil return forecasting
Yuan Zhao, Yaohui Huang, Zhijin Wang, et al.
Neurocomputing (2024) Vol. 581, pp. 127470-127470
Open Access | Times Cited: 5
Application of hybrid model based on CEEMDAN, SVD, PSO to wind energy prediction
Yagang Zhang, Yinchuan Chen
Environmental Science and Pollution Research (2021) Vol. 29, Iss. 15, pp. 22661-22674
Closed Access | Times Cited: 27
Hybrid forecasting system considering the influence of seasonal factors under energy sustainable development goals
Guomin Li, Zhiya Pan, Zihan Qi, et al.
Measurement (2023) Vol. 211, pp. 112607-112607
Closed Access | Times Cited: 11
Limited information limits accuracy: Whether ensemble empirical mode decomposition improves crude oil spot price prediction?
Kunliang Xu, Weiqing Wang
International Review of Financial Analysis (2023) Vol. 87, pp. 102625-102625
Closed Access | Times Cited: 11
A multisource data‐driven combined forecasting model based on internet search keyword screening method for interval soybean futures price
Rui Luo, Jinpei Liu, Piao Wang, et al.
Journal of Forecasting (2023) Vol. 43, Iss. 2, pp. 366-390
Open Access | Times Cited: 11
Energy infrastructure: Investment, sustainability and AI
Elena G. Popkova, Bruno S. Sergi
Resources Policy (2024) Vol. 91, pp. 104807-104807
Closed Access | Times Cited: 4
Climate risk and energy futures high frequency volatility prediction
Xue Gong, Ping Lai, Mengxi He, et al.
Energy (2024) Vol. 307, pp. 132466-132466
Closed Access | Times Cited: 4
A hybrid system with optimized decomposition on random deep learning model for crude oil futures forecasting
Jie Wang, Ying Zhang
Expert Systems with Applications (2025), pp. 126706-126706
Closed Access
Enhancing futures price forecasting with large-scale text feature extraction and compressed hybrid data-driven modeling
Lingyao Zhu, Huwei Liu, Junhui Zhao, et al.
Expert Systems with Applications (2025), pp. 127357-127357
Closed Access
Decomposition-based hybrid methods employing statistical, machine learning, and deep learning models for crude oil price forecasting
Sourav Kumar Purohit, Sibarama Panigrahi
Neural Computing and Applications (2025)
Closed Access
Point and interval prediction of crude oil futures prices based on chaos theory and multiobjective slime mold algorithm
Weixin Sun, Heli Chen, Feng Liu, et al.
Annals of Operations Research (2022)
Open Access | Times Cited: 18
Fortify the investment performance of crude oil market by integrating sentiment analysis and an interval-based trading strategy
Kun Yang, Zishu Cheng, Mingchen Li, et al.
Applied Energy (2023) Vol. 353, pp. 122102-122102
Closed Access | Times Cited: 10
Denoising or distortion: Does decomposition-reconstruction modeling paradigm provide a reliable prediction for crude oil price time series?
Kunliang Xu, Hongli Niu
Energy Economics (2023) Vol. 128, pp. 107129-107129
Closed Access | Times Cited: 9
Forecasting crude oil market returns: Enhanced moving average technical indicators
Danyan Wen, Li Liu, Yudong Wang, et al.
Resources Policy (2022) Vol. 76, pp. 102570-102570
Closed Access | Times Cited: 15
Evidence of the internationalization of China's crude oil futures: Asymmetric linkages to global financial risks
Jiaming Zhang, Songlin Guo, Bin Dou, et al.
Energy Economics (2023) Vol. 127, pp. 107083-107083
Closed Access | Times Cited: 9
An interval-valued carbon price forecasting method based on web search data and social media sentiment
Jinpei Liu, Xue Li, Piao Wang, et al.
Environmental Science and Pollution Research (2023) Vol. 30, Iss. 42, pp. 95840-95859
Closed Access | Times Cited: 7
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