Crude oil price prediction using CEEMDAN and LSTM-attention with news sentiment index
Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles, 76 (2021) 28
Publié en ligne : 2021-04-30
Article cité par
La fonctionnalité Article cité par… liste les citations d'un article. Ces citations proviennent de la base de données des articles de EDP Sciences, ainsi que des bases de données d'autres éditeurs participant au programme CrossRef Cited-by Linking Program. Vous pouvez définir une alerte courriel pour être prévenu de la parution d'un nouvel article citant " cet article (voir sur la page du résumé de l'article le menu à droite).
Article cité :
Citations de cet article :
Iron Ore Price Forecast based on a Multi-Echelon Tandem Learning Model
Weixu Pan, Shi Qiang Liu, Mustafa Kumral, Andrea D’Ariano, Mahmoud Masoud, Waqar Ahmed Khan and Adnan BakatherNatural Resources Research 33 (5) 1969 (2024)
https://doi.org/10.1007/s11053-024-10360-2
A novel approach to Predict WTI crude spot oil price: LSTM-based feature extraction with Xgboost Regressor
Ahmed Ihsan Simsek, Emre Bulut, Yunus Emre Gur and Esma Gültekin TarlaEnergy 309 133102 (2024)
https://doi.org/10.1016/j.energy.2024.133102
A novel crude oil price forecasting model using decomposition and deep learning networks
Yao Dong, He Jiang, Yunting Guo and Jianzhou WangEngineering Applications of Artificial Intelligence 133 108111 (2024)
https://doi.org/10.1016/j.engappai.2024.108111
LEST: Large language models and spatio-temporal data analysis for enhanced Sino-US exchange rate forecasting
Di Han, Wei Guo, Han Chen, Bocheng Wang and Zikun GuoInternational Review of Economics & Finance 96 103508 (2024)
https://doi.org/10.1016/j.iref.2024.103508
Enhancing Multi-step Brent Oil Price Forecasting with Ensemble Multi-scenario Bi-GRU Networks
Mohammed Alruqimi and Luca Di PersioInternational Journal of Computational Intelligence Systems 17 (1) (2024)
https://doi.org/10.1007/s44196-024-00640-3
A novel secondary decomposition method for forecasting crude oil price with twitter sentiment
Jieyi Li, Shuangyue Qian, Ling Li, Yuanxuan Guo, Jun Wu and Ling TangEnergy 290 129954 (2024)
https://doi.org/10.1016/j.energy.2023.129954
Fortify the investment performance of crude oil market by integrating sentiment analysis and an interval-based trading strategy
Kun Yang, Zishu Cheng, Mingchen Li, Shouyang Wang and Yunjie WeiApplied Energy 353 122102 (2024)
https://doi.org/10.1016/j.apenergy.2023.122102
Crude oil price forecasting using K-means clustering and LSTM model enhanced by dense-sparse-dense strategy
Alireza Jahandoost, Farhad Abedinzadeh Torghabeh, Seyyed Abed Hosseini and Mahboobeh HoushmandJournal of Big Data 11 (1) (2024)
https://doi.org/10.1186/s40537-024-00977-8
An Advanced Long Short-Term Memory (LSTM) Neural Network Method for Predicting Rate of Penetration (ROP)
Hui Ji, Yishan Lou, Shuting Cheng, Zelong Xie and Liang ZhuACS Omega 8 (1) 934 (2023)
https://doi.org/10.1021/acsomega.2c06308
A Hybrid Deep Learning Approach for Crude Oil Price Prediction
Hind Aldabagh, Xianrong Zheng and Ravi MukkamalaJournal of Risk and Financial Management 16 (12) 503 (2023)
https://doi.org/10.3390/jrfm16120503
Crude oil futures price prediction by composite machine learning model
Hao Dong, Yingrong Zheng and Na LiAnnals of Operations Research (2023)
https://doi.org/10.1007/s10479-023-05434-y
Learning-Based Stock Trending Prediction by Incorporating Technical Indicators and Social Media Sentiment
Zhaoxia Wang, Zhenda Hu, Fang Li, Seng-Beng Ho and Erik CambriaCognitive Computation 15 (3) 1092 (2023)
https://doi.org/10.1007/s12559-023-10125-8
The role of online news sentiment in carbon price prediction of China’s carbon markets
Muyan Liu and Qianwei YingEnvironmental Science and Pollution Research 30 (14) 41379 (2023)
https://doi.org/10.1007/s11356-023-25197-0
An Integrated Time Series Prediction Model Based on Empirical Mode Decomposition and Two Attention Mechanisms
Xianchang Wang, Siyu Dong and Rui ZhangInformation 14 (11) 610 (2023)
https://doi.org/10.3390/info14110610
240 (2023)
https://doi.org/10.1109/ICCKE60553.2023.10326291
Green Bond Index Prediction Based on CEEMDAN-LSTM
Jiaqi Wang, Jiulin Tang and Kun GuoFrontiers in Energy Research 9 (2022)
https://doi.org/10.3389/fenrg.2021.793413
A novel crude oil price trend prediction method: Machine learning classification algorithm based on multi-modal data features
Huizi He, Mei Sun, Xiuming Li and Isaac Adjei MensahEnergy 244 122706 (2022)
https://doi.org/10.1016/j.energy.2021.122706
Forecasting Crude Oil Prices with a WT-FNN Model
Donghua Wang and Tianhui FangEnergies 15 (6) 1955 (2022)
https://doi.org/10.3390/en15061955
International Natural Gas Price Trends Prediction with Historical Prices and Related News
Renchu Guan, Aoqing Wang, Yanchun Liang, Jiasheng Fu and Xiaosong HanEnergies 15 (10) 3573 (2022)
https://doi.org/10.3390/en15103573
A Hybrid Framework Using PCA, EMD and LSTM Methods for Stock Market Price Prediction with Sentiment Analysis
Krittakom Srijiranon, Yoskorn Lertratanakham and Tanatorn TanantongApplied Sciences 12 (21) 10823 (2022)
https://doi.org/10.3390/app122110823
Applying attention-based BiLSTM and technical indicators in the design and performance analysis of stock trading strategies
Ming-Che Lee, Jia-Wei Chang, Sheng-Cheng Yeh, et al.Neural Computing and Applications 34 (16) 13267 (2022)
https://doi.org/10.1007/s00521-021-06828-4
A Novel Sensor System for In Vivo Perception Reconstruction Based on Long Short-Term Memory Networks
Ding Han, Guozheng Yan, Lichao Wang, Fangfang Hua and Lin YanSensors 22 (19) 7407 (2022)
https://doi.org/10.3390/s22197407
(2022)
https://doi.org/10.21203/rs.3.rs-2179239/v1