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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 new secondary decomposition-reconstruction-ensemble approach for crude oil price forecasting
Jingyun Sun, Zhao Panpan, Shaolong Sun
Resources Policy (2022) Vol. 77, pp. 102762-102762
Closed Access | Times Cited: 42

Showing 1-25 of 42 citing articles:

Decomposition integration and error correction method for photovoltaic power forecasting
Guohui Li, Xuan Wei, Hong Yang
Measurement (2023) Vol. 208, pp. 112462-112462
Closed Access | Times Cited: 53
Research on the potential for China to achieve carbon neutrality: A hybrid prediction model integrated with elman neural network and sparrow search algorithm
Meng Yang, Yi‐Sheng Liu
Journal of Environmental Management (2022) Vol. 329, pp. 117081-117081
Open Access | Times Cited: 43
An ensemble dynamic self-learning model for multiscale carbon price forecasting
Wen Zhang, Zhibin Wu, Xiao‐Jun Zeng, et al.
Energy (2022) Vol. 263, pp. 125820-125820
Open Access | Times Cited: 40
A novel hybrid model based on deep learning and error correction for crude oil futures prices forecast
Tunhua Wu, Jinghan Dong, Zhaocai Wang, et al.
Resources Policy (2023) Vol. 83, pp. 103602-103602
Closed Access | Times Cited: 36
Carbon price forecasting based on secondary decomposition and feature screening
Jingmiao Li, Dehong Liu
Energy (2023) Vol. 278, pp. 127783-127783
Closed Access | Times Cited: 34
TLIA: Time-series forecasting model using long short-term memory integrated with artificial neural networks for volatile energy markets
Dalal AL-Alimi, Ayman Mutahar AlRassas, Mohammed A. A. Al‐qaness, et al.
Applied Energy (2023) Vol. 343, pp. 121230-121230
Closed Access | Times Cited: 26
A novel hybrid model for crude oil price forecasting based on MEEMD and Mix-KELM
Jingjing Li, Zhanjiang Hong, Chengyuan Zhang, et al.
Expert Systems with Applications (2024) Vol. 246, pp. 123104-123104
Closed Access | Times Cited: 11
A decomposition ensemble based deep learning approach for crude oil price forecasting
He Jiang, Weiqiang Hu, Ling Xiao, et al.
Resources Policy (2022) Vol. 78, pp. 102855-102855
Closed Access | Times Cited: 30
Agricultural commodity futures prices prediction based on a new hybrid forecasting model combining quadratic decomposition technology and LSTM model
Tingting Zhang, Zhenpeng Tang
Frontiers in Sustainable Food Systems (2024) Vol. 8
Open Access | Times Cited: 6
An ensemble self-learning framework combined with dynamic model selection and divide-conquer strategies for carbon emissions trading price forecasting
Rui Yang, Hui Liu, Yanfei Li
Chaos Solitons & Fractals (2023) Vol. 173, pp. 113692-113692
Closed Access | Times Cited: 14
Deep learning systems for forecasting the prices of crude oil and precious metals
Parisa Foroutan, Salim Lahmiri
Financial Innovation (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 5
Crude Oil Price Time Series Forecasting: A Novel Approach Based on Variational Mode Decomposition, Time-Series Imaging, and Deep Learning
Zi-Jian Peng, Chuan Zhang, Yu‐Xin Tian
IEEE Access (2023) Vol. 11, pp. 82216-82231
Open Access | Times Cited: 12
A multiple feature fusion-based intelligent optimization ensemble model for carbon price forecasting
Jujie Wang, Jian Guo Dong, Xin Zhang, et al.
Process Safety and Environmental Protection (2024) Vol. 187, pp. 1558-1575
Closed Access | Times Cited: 4
Do EEMD based decomposition-ensemble models indeed improve prediction for crude oil futures prices?
Kunliang Xu, Hongli Niu
Technological Forecasting and Social Change (2022) Vol. 184, pp. 121967-121967
Closed Access | Times Cited: 16
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
A novel link prediction model for interval-valued crude oil prices based on complex network and multi-source information
Jinpei Liu, Xiaoman Zhao, Rui Luo, et al.
Applied Energy (2024) Vol. 376, pp. 124261-124261
Closed Access | Times Cited: 3
Oil and gold price prediction using optimized fuzzy inference system based extreme learning machine
Sudeepa Das, Tirath Prasad Sahu, Rekh Ram Janghel
Resources Policy (2022) Vol. 79, pp. 103109-103109
Closed Access | Times Cited: 14
Short-term traffic flow prediction based on secondary hybrid decomposition and deep echo state networks
Guojing Hu, Robert W. Whalin, Tor A. Kwembe, et al.
Physica A Statistical Mechanics and its Applications (2023) Vol. 632, pp. 129313-129313
Closed Access | Times Cited: 8
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: 8
Forecasting China carbon price using an error-corrected secondary decomposition hybrid model integrated fuzzy dispersion entropy and deep learning paradigm
Po Yun, Yingtong Zhou, Chenghui Liu, et al.
Environmental Science and Pollution Research (2024) Vol. 31, Iss. 11, pp. 16530-16553
Open Access | Times Cited: 2
Crude Oil Prices Forecast Based on Mixed-Frequency Deep Learning Approach and Intelligent Optimization Algorithm
Wanbo Lu, Zhaojie Huang
Entropy (2024) Vol. 26, Iss. 5, pp. 358-358
Open Access | Times Cited: 2
Multi-step ozone concentration prediction model based on improved secondary decomposition and adaptive kernel density estimation
Jianguo Zhou, Luming Zhou, Chenhao Cai, et al.
Process Safety and Environmental Protection (2024) Vol. 190, pp. 386-404
Closed Access | Times Cited: 2
Research on runoff interval prediction method based on deep learning ensemble modeling with hydrological factors
Jinghan Huang, Zhaocai Wang, Jinghan Dong, et al.
Stochastic Environmental Research and Risk Assessment (2024)
Closed Access | Times Cited: 2
DMEformer: A newly designed dynamic model ensemble transformer for crude oil futures prediction
Chao Liu, Kaiyi Ruan, Xinmeng Ma
Heliyon (2023) Vol. 9, Iss. 6, pp. e16715-e16715
Open Access | Times Cited: 6
Handwritten Pattern Recognition Using Birds-Flocking Inspired Data Augmentation Technique
Yihan Xu, Aamir Wali
IEEE Access (2023) Vol. 11, pp. 71426-71434
Open Access | Times Cited: 6
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