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Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction

Citations

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Cited by:

  1. Mohammed-Khalil Ghali & Cecil Pang & Oscar Molina & Carlos Gershenson-Garcia & Daehan Won, 2025. "Forecasting Commodity Price Shocks Using Temporal and Semantic Fusion of Prices Signals and Agentic Generative AI Extracted Economic News," Papers 2508.06497, arXiv.org.
  2. Sun, Jingyun & Zhao, Panpan & Sun, Shaolong, 2022. "A new secondary decomposition-reconstruction-ensemble approach for crude oil price forecasting," Resources Policy, Elsevier, vol. 77(C).
  3. Arroyo Marioli,Francisco & Khadan,Jeetendra & Ohnsorge,Franziska Lieselotte & Yamazaki,Takefumi, 2023. "Forecasting Industrial Commodity Prices : Literature Review and a Model Suite," Policy Research Working Paper Series 10611, The World Bank.
  4. Chen, Haixin & Liu, Yancheng & Li, Xiangjie & Gu, Xiang & Fan, Kun, 2024. "Oil market regulatory: An ensembled model for prediction," Finance Research Letters, Elsevier, vol. 67(PA).
  5. Ding, Yishan, 2018. "A novel decompose-ensemble methodology with AIC-ANN approach for crude oil forecasting," Energy, Elsevier, vol. 154(C), pages 328-336.
  6. Chai, Jian & Lu, Quanying & Hu, Yi & Wang, Shouyang & Lai, Kin Keung & Liu, Hongtao, 2018. "Analysis and Bayes statistical probability inference of crude oil price change point," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 271-283.
  7. Zhao, Yang & Li, Jianping & Yu, Lean, 2017. "A deep learning ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 9-16.
  8. Liu, Jinpei & Zhao, Xiaoman & Luo, Rui & Tao, Zhifu, 2024. "A novel link prediction model for interval-valued crude oil prices based on complex network and multi-source information," Applied Energy, Elsevier, vol. 376(PB).
  9. Jianfang Cao & Hongyan Cui & Hao Shi & Lijuan Jiao, 2016. "Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-17, June.
  10. Chen, Sai & Song, Yan & Ding, Yueting & Zhang, Ming & Nie, Rui, 2021. "Using long short-term memory model to study risk assessment and prediction of China’s oil import from the perspective of resilience theory," Energy, Elsevier, vol. 215(PB).
  11. Wang, Xin & Sun, Mei, 2021. "A novel prediction model of multi-layer symbolic pattern network: Based on causation entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 575(C).
  12. Di Zhu & Yinghong Wang & Fenglin Zhang, 2022. "Energy Price Prediction Integrated with Singular Spectrum Analysis and Long Short-Term Memory Network against the Background of Carbon Neutrality," Energies, MDPI, vol. 15(21), pages 1-20, October.
  13. Zhang, Pinyi & Ci, Bicong, 2020. "Deep belief network for gold price forecasting," Resources Policy, Elsevier, vol. 69(C).
  14. Krzysztof Drachal & Michał Pawłowski, 2021. "A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities," Economies, MDPI, vol. 9(1), pages 1-22, January.
  15. Donghua Wang & Tianhui Fang, 2022. "Forecasting Crude Oil Prices with a WT-FNN Model," Energies, MDPI, vol. 15(6), pages 1-21, March.
  16. Su, Chi-Wei & Qin, Meng & Tao, Ran & Umar, Muhammad, 2020. "Financial implications of fourth industrial revolution: Can bitcoin improve prospects of energy investment?," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
  17. Liu, Zhenya & Teka, Hanen & You, Rongyu, 2023. "Conditional autoencoder pricing model for energy commodities," Resources Policy, Elsevier, vol. 86(PA).
  18. Wang, Lijun & An, Haizhong & Liu, Xiaojia & Huang, Xuan, 2016. "Selecting dynamic moving average trading rules in the crude oil futures market using a genetic approach," Applied Energy, Elsevier, vol. 162(C), pages 1608-1618.
  19. Zhao, Lu-Tao & Wang, Yi & Guo, Shi-Qiu & Zeng, Guan-Rong, 2018. "A novel method based on numerical fitting for oil price trend forecasting," Applied Energy, Elsevier, vol. 220(C), pages 154-163.
  20. Zhu, Jiaming & Wu, Peng & Chen, Huayou & Liu, Jinpei & Zhou, Ligang, 2019. "Carbon price forecasting with variational mode decomposition and optimal combined model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 140-158.
  21. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
    • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
  22. Kertlly de Medeiros, Rennan & da Nóbrega Besarria, Cássio & Pitta de Jesus, Diego & Phillipe de Albuquerquemello, Vinicius, 2022. "Forecasting oil prices: New approaches," Energy, Elsevier, vol. 238(PC).
  23. Mustanen, Dmitri & Maaitah, Ahmad & Mishra, Tapas & Parhi, Mamata, 2022. "The power of investors’ optimism and pessimism in oil market forecasting," Energy Economics, Elsevier, vol. 114(C).
  24. Karasu, Seçkin & Altan, Aytaç, 2022. "Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization," Energy, Elsevier, vol. 242(C).
  25. Radosław Puka & Bartosz Łamasz & Marek Michalski, 2021. "Effectiveness of Artificial Neural Networks in Hedging against WTI Crude Oil Price Risk," Energies, MDPI, vol. 14(11), pages 1-26, June.
  26. Yu, Lean & Wang, Zishu & Tang, Ling, 2015. "A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting," Applied Energy, Elsevier, vol. 156(C), pages 251-267.
  27. Baruník, Jozef & Malinská, Barbora, 2016. "Forecasting the term structure of crude oil futures prices with neural networks," Applied Energy, Elsevier, vol. 164(C), pages 366-379.
  28. Radosław Puka & Bartosz Łamasz, 2020. "Using Artificial Neural Networks to Find Buy Signals for WTI Crude Oil Call Options," Energies, MDPI, vol. 13(17), pages 1-20, August.
  29. Homod, Raad Z. & Gaeid, Khalaf S. & Dawood, Suroor M. & Hatami, Alireza & Sahari, Khairul S., 2020. "Evaluation of energy-saving potential for optimal time response of HVAC control system in smart buildings," Applied Energy, Elsevier, vol. 271(C).
  30. Zhao, Geya & Xue, Minggao & Cheng, Li, 2023. "A new hybrid model for multi-step WTI futures price forecasting based on self-attention mechanism and spatial–temporal graph neural network," Resources Policy, Elsevier, vol. 85(PB).
  31. Xiaojun Chen & Yun Shi & Xiaozhou Wang, 2020. "Equilibrium Oil Market Share under the COVID-19 Pandemic," Papers 2007.15265, arXiv.org.
  32. Ge, Lei & Huang, Qiwei & Zhu, Fengshuang & Chen, Shun, 2025. "Advanced time series forecasting for commodities: Insights from the FEDformer model," Energy Economics, Elsevier, vol. 147(C).
  33. Bekiroglu, Korkut & Duru, Okan & Gulay, Emrah & Su, Rong & Lagoa, Constantino, 2018. "Predictive analytics of crude oil prices by utilizing the intelligent model search engine," Applied Energy, Elsevier, vol. 228(C), pages 2387-2397.
  34. Niu, Hongli & Xu, Kunliang & Liu, Cheng, 2021. "A decomposition-ensemble model with regrouping method and attention-based gated recurrent unit network for energy price prediction," Energy, Elsevier, vol. 231(C).
  35. Sina Aghaei, 2018. "A Data-Driven Approach for Modeling Stochasticity in Oil Market," Papers 1805.12110, arXiv.org.
  36. Wang, Minggang & Zhao, Longfeng & Du, Ruijin & Wang, Chao & Chen, Lin & Tian, Lixin & Eugene Stanley, H., 2018. "A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 220(C), pages 480-495.
  37. Gun Il Kim & Beakcheol Jang, 2023. "Petroleum Price Prediction with CNN-LSTM and CNN-GRU Using Skip-Connection," Mathematics, MDPI, vol. 11(3), pages 1-16, January.
  38. Yu, Hongchu & Fang, Zhixiang & Lu, Feng & Murray, Alan T. & Zhang, Hengcai & Peng, Peng & Mei, Qiang & Chen, Jinhai, 2019. "Impact of oil price fluctuations on tanker maritime network structure and traffic flow changes," Applied Energy, Elsevier, vol. 237(C), pages 390-403.
  39. Abdollahi, Hooman, 2020. "A novel hybrid model for forecasting crude oil price based on time series decomposition," Applied Energy, Elsevier, vol. 267(C).
  40. Wang, Minggang & Tian, Lixin & Zhou, Peng, 2018. "A novel approach for oil price forecasting based on data fluctuation network," Energy Economics, Elsevier, vol. 71(C), pages 201-212.
  41. Zheng, Li & Sun, Yuying & Wang, Shouyang, 2024. "A novel interval-based hybrid framework for crude oil price forecasting and trading," Energy Economics, Elsevier, vol. 130(C).
  42. Zuzanna Karolak, 2021. "Energy prices forecasting using nonlinear univariate models," Bank i Kredyt, Narodowy Bank Polski, vol. 52(6), pages 577-598.
  43. Hossein Hassani & Mohammad Reza Yeganegi & Xu Huang, 2021. "Fusing Nature with Computational Science for Optimal Signal Extraction," Stats, MDPI, vol. 4(1), pages 1-15, January.
  44. Xu, Kunliang & Wang, Weiqing, 2023. "Limited information limits accuracy: Whether ensemble empirical mode decomposition improves crude oil spot price prediction?," International Review of Financial Analysis, Elsevier, vol. 87(C).
  45. Qin, Meng & Su, Chi-Wei & Hao, Lin-Na & Tao, Ran, 2020. "The stability of U.S. economic policy: Does it really matter for oil price?," Energy, Elsevier, vol. 198(C).
  46. Ouyang, Zisheng & Lu, Min & Ouyang, Zhongzhe & Zhou, Xuewei & Wang, Ren, 2024. "A novel integrated method for improving the forecasting accuracy of crude oil: ESMD-CFastICA-BiLSTM-Attention," Energy Economics, Elsevier, vol. 138(C).
  47. Ghaemi Asl, Mahdi & Adekoya, Oluwasegun Babatunde & Rashidi, Muhammad Mahdi & Ghasemi Doudkanlou, Mohammad & Dolatabadi, Ali, 2022. "Forecast of Bayesian-based dynamic connectedness between oil market and Islamic stock indices of Islamic oil-exporting countries: Application of the cascade-forward backpropagation network," Resources Policy, Elsevier, vol. 77(C).
  48. Tian, Xuecheng & Wang, Shuaian & Liu, Yan & Yang, Ying, 2025. "Data-driven optimization for container ship bunkering management under fuel price uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 198(C).
  49. Zhao, Yuan & Zhang, Weiguo & Gong, Xue & Wang, Chao, 2021. "A novel method for online real-time forecasting of crude oil price," Applied Energy, Elsevier, vol. 303(C).
  50. Su, Chi-Wei & Wang, Dan & Mirza, Nawazish & Zhong, Yifan & Umar, Muhammad, 2023. "The impact of consumer confidence on oil prices," Energy Economics, Elsevier, vol. 124(C).
  51. Na Fu & Liyan Geng & Junhai Ma & Xue Ding, 2023. "Price, Complexity, and Mathematical Model," Mathematics, MDPI, vol. 11(13), pages 1-30, June.
  52. Lu-Tao Zhao & Shun-Gang Wang & Zhi-Gang Zhang, 2020. "Oil Price Forecasting Using a Time-Varying Approach," Energies, MDPI, vol. 13(6), pages 1-16, March.
  53. Li, Jinchao & Zhu, Shaowen & Wu, Qianqian, 2019. "Monthly crude oil spot price forecasting using variational mode decomposition," Energy Economics, Elsevier, vol. 83(C), pages 240-253.
  54. Karasu, Seçkin & Altan, Aytaç & Bekiros, Stelios & Ahmad, Wasim, 2020. "A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series," Energy, Elsevier, vol. 212(C).
  55. Cen, Zhongpei & Wang, Jun, 2019. "Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer," Energy, Elsevier, vol. 169(C), pages 160-171.
  56. Wei Liu & Yoshihisa Suzuki & Shuyi Du, 2024. "Forecasting the Stock Price of Listed Innovative SMEs Using Machine Learning Methods Based on Bayesian optimization: Evidence from China," Computational Economics, Springer;Society for Computational Economics, vol. 63(5), pages 2035-2068, May.
  57. repec:zib:zbjtin:v:3:y:2023:i:1:p:06-12 is not listed on IDEAS
  58. Cheng, Fangzheng & Li, Tian & Wei, Yi-ming & Fan, Tijun, 2019. "The VEC-NAR model for short-term forecasting of oil prices," Energy Economics, Elsevier, vol. 78(C), pages 656-667.
  59. Sarat Chandra Nayak & Satchidananda Dehuri & Sung-Bae Cho, 2024. "Elitist-opposition-based artificial electric field algorithm for higher-order neural network optimization and financial time series forecasting," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-43, December.
  60. Qin, Quande & Xie, Kangqiang & He, Huangda & Li, Li & Chu, Xianghua & Wei, Yi-Ming & Wu, Teresa, 2019. "An effective and robust decomposition-ensemble energy price forecasting paradigm with local linear prediction," Energy Economics, Elsevier, vol. 83(C), pages 402-414.
  61. Zhen-Yao Chen & R. J. Kuo, 2019. "Combining SOM and evolutionary computation algorithms for RBF neural network training," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1137-1154, March.
  62. Jesús Molina‐Muñoz & Andrés Mora‐Valencia & Javier Perote, 2024. "Predicting carbon and oil price returns using hybrid models based on machine and deep learning," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(2), June.
  63. A. Usha Ruby & J. George Chellin Chandran & B. N. Chaithanya & T. J. Swasthika Jain & Renuka Patil, 2024. "Effective Crude Oil Prediction Using CHS-EMD Decomposition and PS-RNN Model," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 1295-1314, August.
  64. Pablo Cansado-Bravo & Carlos Rodríguez-Monroy, 2018. "Persistence of Oil Prices in Gas Import Prices and the Resilience of the Oil-Indexation Mechanism. The Case of Spanish Gas Import Prices," Energies, MDPI, vol. 11(12), pages 1-17, December.
  65. Wang, Minggang & Chen, Ying & Tian, Lixin & Jiang, Shumin & Tian, Zihao & Du, Ruijin, 2016. "Fluctuation behavior analysis of international crude oil and gasoline price based on complex network perspective," Applied Energy, Elsevier, vol. 175(C), pages 109-127.
  66. Gao, Xiangyun & Fang, Wei & An, Feng & Wang, Yue, 2017. "Detecting method for crude oil price fluctuation mechanism under different periodic time series," Applied Energy, Elsevier, vol. 192(C), pages 201-212.
  67. Kou, Mingting & Zhang, Menglin & Yang, Yuanqi & Shao, Hanqing, 2024. "Energy finance research: What happens beneath the literature?," International Review of Financial Analysis, Elsevier, vol. 95(PB).
  68. Wang, Jue & Athanasopoulos, George & Hyndman, Rob J. & Wang, Shouyang, 2018. "Crude oil price forecasting based on internet concern using an extreme learning machine," International Journal of Forecasting, Elsevier, vol. 34(4), pages 665-677.
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