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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 hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms
Minggang Wang, Longfeng Zhao, Ruijin Du, et al.
Applied Energy (2018) Vol. 220, pp. 480-495
Open Access | Times Cited: 133

Showing 1-25 of 133 citing articles:

Machine learning in energy economics and finance: A review
Hamed Ghoddusi, Germán G. Creamer, Nima Rafizadeh
Energy Economics (2019) Vol. 81, pp. 709-727
Closed Access | Times Cited: 350
Artificial Intelligence and emerging digital technologies in the energy sector
Wenjing Lyu, Jin Liu
Applied Energy (2021) Vol. 303, pp. 117615-117615
Closed Access | Times Cited: 209
Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization
Seçkin Karasu, Aytaç Altan
Energy (2021) Vol. 242, pp. 122964-122964
Closed Access | Times Cited: 182
Soft skills, hard skills: What matters most? Evidence from job postings
Wenjing Lyu, Liu Jin
Applied Energy (2021) Vol. 300, pp. 117307-117307
Closed Access | Times Cited: 119
Geopolitical risk trends and crude oil price predictability
Zhikai Zhang, Mengxi He, Yaojie Zhang, et al.
Energy (2022) Vol. 258, pp. 124824-124824
Closed Access | Times Cited: 98
Oil price forecasting: A hybrid GRU neural network based on decomposition–reconstruction methods
Shiqi Zhang, Jing Luo, Shuyuan Wang, et al.
Expert Systems with Applications (2023) Vol. 218, pp. 119617-119617
Closed Access | Times Cited: 93
Which Artificial Intelligence Algorithm Better Predicts the Chinese Stock Market?
Lin Chen, Zhilin Qiao, Minggang Wang, et al.
IEEE Access (2018) Vol. 6, pp. 48625-48633
Open Access | Times Cited: 110
A novel hybrid model for forecasting crude oil price based on time series decomposition
Hooman Abdollahi
Applied Energy (2020) Vol. 267, pp. 115035-115035
Closed Access | Times Cited: 102
Forecasting crude oil prices: A scaled PCA approach
Mengxi He, Yaojie Zhang, Danyan Wen, et al.
Energy Economics (2021) Vol. 97, pp. 105189-105189
Closed Access | Times Cited: 95
Monthly crude oil spot price forecasting using variational mode decomposition
Jinchao Li, Shaowen Zhu, Qianqian Wu
Energy Economics (2019) Vol. 83, pp. 240-253
Closed Access | Times Cited: 92
Short-term forecasting of natural gas prices by using a novel hybrid method based on a combination of the CEEMDAN-SE-and the PSO-ALS-optimized GRU network
Jun Wang, Junxing Cao, Shan Yuan, et al.
Energy (2021) Vol. 233, pp. 121082-121082
Closed Access | Times Cited: 91
Modes decomposition method in fusion with robust random vector functional link network for crude oil price forecasting
Ranjeeta Bisoi, P.K. Dash, Sthita Prajna Mishra
Applied Soft Computing (2019) Vol. 80, pp. 475-493
Closed Access | Times Cited: 90
A multi-factor integrated model for carbon price forecasting: Market interaction promoting carbon emission reduction
Lu‐Tao Zhao, Jing Miao, Shen Qu, et al.
The Science of The Total Environment (2021) Vol. 796, pp. 149110-149110
Closed Access | Times Cited: 89
Carbon price forecasting with complex network and extreme learning machine
Hua Xu, Minggang Wang, Shumin Jiang, et al.
Physica A Statistical Mechanics and its Applications (2019) Vol. 545, pp. 122830-122830
Closed Access | Times Cited: 87
The market-linkage of the volatility spillover between traditional energy price and carbon price on the realization of carbon value of emission reduction behavior
Qi Wu, Minggang Wang, Lixin Tian
Journal of Cleaner Production (2019) Vol. 245, pp. 118682-118682
Closed Access | Times Cited: 79
A novel multiscale forecasting model for crude oil price time series
Ranran Li, Yucai Hu, Jiani Heng, et al.
Technological Forecasting and Social Change (2021) Vol. 173, pp. 121181-121181
Closed Access | Times Cited: 56
Forecasting the crude oil prices with an EMD-ISBM-FNN model
Tianhui Fang, Chunling Zheng, Donghua Wang
Energy (2022) Vol. 263, pp. 125407-125407
Closed Access | Times Cited: 40
Crude oil price forecasting with machine learning and Google search data: An accuracy comparison of single-model versus multiple-model
Quande Qin, Zhaorong Huang, Zhihao Zhou, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 123, pp. 106266-106266
Closed Access | Times Cited: 26
A novel crude oil price forecasting model using decomposition and deep learning networks
Yao Dong, He Jiang, Yunting Guo, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 133, pp. 108111-108111
Closed Access | Times Cited: 14
Research on Crude Oil Futures Price Prediction Methods: A Perspective Based on Quantum Deep Learning
Dongsheng Zhai, Tianrui Zhang, Guoqiang Liang, et al.
Energy (2025), pp. 135080-135080
Closed Access | Times Cited: 1
Exploring energy flows embodied in China's economy from the regional and sectoral perspectives via combination of multi-regional input–output analysis and a complex network approach
Miaohan Tang, Jingke Hong, Guiwen Liu, et al.
Energy (2019) Vol. 170, pp. 1191-1201
Open Access | Times Cited: 68
Energy price prediction using data-driven models: A decade review
Hongfang Lü, Xin Ma, Minda Ma, et al.
Computer Science Review (2020) Vol. 39, pp. 100356-100356
Closed Access | Times Cited: 64
Achieving the Success of Sustainability Development Projects through Big Data Analytics and Artificial Intelligence Capability
Haili Zhang, Michael Song, Huanhuan He
Sustainability (2020) Vol. 12, Iss. 3, pp. 949-949
Open Access | Times Cited: 62
A multi-scale method for forecasting oil price with multi-factor search engine data
Ling Tang, Chengyuan Zhang, Ling Li, et al.
Applied Energy (2019) Vol. 257, pp. 114033-114033
Closed Access | Times Cited: 60
An effective rolling decomposition-ensemble model for gasoline consumption forecasting
Lean Yu, Yueming Ma, Mengyao Ma
Energy (2021) Vol. 222, pp. 119869-119869
Closed Access | Times Cited: 51
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