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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 compressed sensing based AI learning paradigm for crude oil price forecasting
Lean Yu, Yang Zhao, Ling Tang
Energy Economics (2014) Vol. 46, pp. 236-245
Closed Access | Times Cited: 121

Showing 1-25 of 121 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
A deep learning ensemble approach for crude oil price forecasting
Yang Zhao, Jianping Li, Lean Yu
Energy Economics (2017) Vol. 66, pp. 9-16
Closed Access | Times Cited: 340
Online big data-driven oil consumption forecasting with Google trends
Lean Yu, Yaqing Zhao, Ling Tang, et al.
International Journal of Forecasting (2018) Vol. 35, Iss. 1, pp. 213-223
Closed Access | Times Cited: 211
A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting
Lean Yu, Wei Dai, Ling Tang
Engineering Applications of Artificial Intelligence (2015) Vol. 47, pp. 110-121
Closed Access | Times Cited: 192
A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting
Lean Yu, Zishu Wang, Ling Tang
Applied Energy (2015) Vol. 156, pp. 251-267
Closed Access | Times Cited: 184
Price forecasting through neural networks for crude oil, heating oil, and natural gas
Bingzi Jin, Xiaojie Xu
Deleted Journal (2024) Vol. 1, pp. 100001-100001
Open Access | Times Cited: 135
Linear and nonlinear Granger causality investigation between carbon market and crude oil market: A multi-scale approach
Lean Yu, Jingjing Li, Ling Tang, et al.
Energy Economics (2015) Vol. 51, pp. 300-311
Closed Access | Times Cited: 158
A decomposition-clustering-ensemble learning approach for solar radiation forecasting
Shaolong Sun, Shouyang Wang, Guowei Zhang, et al.
Solar Energy (2018) Vol. 163, pp. 189-199
Closed Access | Times Cited: 145
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
A Novel CEEMD-Based EELM Ensemble Learning Paradigm for Crude Oil Price Forecasting
Ling Tang, Wei Dai, Lean Yu, et al.
International Journal of Information Technology & Decision Making (2014) Vol. 14, Iss. 01, pp. 141-169
Closed Access | Times Cited: 132
A CEEMDAN and XGBOOST‐Based Approach to Forecast Crude Oil Prices
Yingrui Zhou, Taiyong Li, Jiayi Shi, et al.
Complexity (2019) Vol. 2019, Iss. 1
Open Access | Times Cited: 119
Predicting corporate carbon footprints for climate finance risk analyses: A machine learning approach
Quyen Nguyen, Ivan Diaz‐Rainey, Duminda Kuruppuarachchi
Energy Economics (2021) Vol. 95, pp. 105129-105129
Closed Access | Times Cited: 95
Interval decomposition ensemble approach for crude oil price forecasting
Shaolong Sun, Yuying Sun, Shouyang Wang, et al.
Energy Economics (2018) Vol. 76, pp. 274-287
Closed Access | Times Cited: 93
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 novel decompose-ensemble methodology with AIC-ANN approach for crude oil forecasting
Yishan Ding
Energy (2018) Vol. 154, pp. 328-336
Closed Access | Times Cited: 87
A machine learning-based surrogate model to approximate optimal building retrofit solutions
Emmanouil Thrampoulidis, Georgios Mavromatidis, Aurélien Lucchi, et al.
Applied Energy (2020) Vol. 281, pp. 116024-116024
Open Access | Times Cited: 86
Deterministic and uncertainty crude oil price forecasting based on outlier detection and modified multi-objective optimization algorithm
Chunying Wu, Jianzhou Wang, Hao Yan
Resources Policy (2022) Vol. 77, pp. 102780-102780
Closed Access | Times Cited: 50
An empirical study on the response of the energy market to the shock from the artificial intelligence industry
Min Liu, Hongfei Liu, Chien‐Chiang Lee
Energy (2023) Vol. 288, pp. 129655-129655
Closed Access | Times Cited: 28
What can be learned from the historical trend of crude oil prices? An ensemble approach for crude oil price forecasting
Mingchen Li, Zishu Cheng, Wencan Lin, et al.
Energy Economics (2023) Vol. 123, pp. 106736-106736
Closed Access | Times Cited: 26
The role of green energy stock market in forecasting China's crude oil market: An application of IIS approach and sparse regression models
Faridoon Khan, Sara Muhammadullah, Arshian Sharif, et al.
Energy Economics (2023) Vol. 130, pp. 107269-107269
Closed Access | Times Cited: 24
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
An ICA-based support vector regression scheme for forecasting crude oil prices
Liwei Fan, Sijia Pan, Zimin Li, et al.
Technological Forecasting and Social Change (2016) Vol. 112, pp. 245-253
Closed Access | Times Cited: 75
LSSVR ensemble learning with uncertain parameters for crude oil price forecasting
Lean Yu, Huijuan Xu, Ling Tang
Applied Soft Computing (2016) Vol. 56, pp. 692-701
Closed Access | Times Cited: 75
The VEC-NAR model for short-term forecasting of oil prices
Fangzheng Cheng, Tian Li, Yi‐Ming Wei, et al.
Energy Economics (2018) Vol. 78, pp. 656-667
Closed Access | Times Cited: 68
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