A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms

Citations

Cited by:

1. Haili Zhang & Michael Song & Huanhuan He, 2020. "Achieving the Success of Sustainability Development Projects through Big Data Analytics and Artificial Intelligence Capability," Sustainability, MDPI, vol. 12(3), pages 1-23, January.
2. Jha, Nimish & Kumar Tanneru, Hemanth & Palla, Sridhar & Hussain Mafat, Iradat, 2024. "Multivariate analysis and forecasting of the crude oil prices: Part I – Classical machine learning approaches," Energy, Elsevier, vol. 296(C).
3. Lyu, Wenjing & Liu, Jin, 2021. "Soft skills, hard skills: What matters most? Evidence from job postings," Applied Energy, Elsevier, vol. 300(C).
4. Faisal Khalil & Gordon Pipa, 2022. "Is Deep-Learning and Natural Language Processing Transcending the Financial Forecasting? Investigation Through Lens of News Analytic Process," Computational Economics, Springer;Society for Computational Economics, vol. 60(1), pages 147-171, June.
5. Hu, Yuntong & Xiao, Fuyuan, 2022. "A novel method for forecasting time series based on directed visibility graph and improved random walk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
6. Lean Yu & Yueming Ma, 2021. "A Data-Trait-Driven Rolling Decomposition-Ensemble Model for Gasoline Consumption Forecasting," Energies, MDPI, vol. 14(15), pages 1-26, July.
7. 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).
8. Jiang, Jianhua & Chen, Yujun & Meng, Xianqiu & Wang, Limin & Li, Keqin, 2019. "A novel density peaks clustering algorithm based on k nearest neighbors for improving assignment process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 702-713.
9. 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.
10. Wang, Fan & Tian, Lixin & Du, Ruijin & Dong, Gaogao, 2021. "Universal law in the crude oil market based on visibility graph algorithm and network structure," Resources Policy, Elsevier, vol. 70(C).
11. Wang, Ze & Gao, Xiangyun & Tang, Renwu & Liu, Xueyong & Sun, Qingru & Chen, Zhihua, 2019. "Identifying influential nodes based on fluctuation conduction network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 355-369.
12. Zhao, Haitao & Ezeh, Collins I. & Ren, Weijia & Li, Wentao & Pang, Cheng Heng & Zheng, Chenghang & Gao, Xiang & Wu, Tao, 2019. "Integration of machine learning approaches for accelerated discovery of transition-metal dichalcogenides as Hg0 sensing materials," Applied Energy, Elsevier, vol. 254(C).
13. Miguel A. Jaramillo-Morán & Agustín García-García, 2019. "Applying Artificial Neural Networks to Forecast European Union Allowance Prices: The Effect of Information from Pollutant-Related Sectors," Energies, MDPI, vol. 12(23), pages 1-18, November.
14. Tang, Miaohan & Hong, Jingke & Liu, Guiwen & Shen, Geoffrey Qiping, 2019. "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," Energy, Elsevier, vol. 170(C), pages 1191-1201.
15. Zhou, Yang & Xie, Chi & Wang, Gang-Jin & Zhu, You & Uddin, Gazi Salah, 2023. "Analysing and forecasting co-movement between innovative and traditional financial assets based on complex network and machine learning," Research in International Business and Finance, Elsevier, vol. 64(C).
16. Lyu, Wenjing & Liu, Jin, 2021. "Artificial Intelligence and emerging digital technologies in the energy sector," Applied Energy, Elsevier, vol. 303(C).
17. 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).
18. 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.
19. Hongwei Wang & Yuansheng Huang & Chong Gao & Yuqing Jiang, 2019. "Cost Forecasting Model of Transformer Substation Projects Based on Data Inconsistency Rate and Modified Deep Convolutional Neural Network," Energies, MDPI, vol. 12(16), pages 1-21, August.
20. He, Huizi & Sun, Mei & Li, Xiuming & Mensah, Isaac Adjei, 2022. "A novel crude oil price trend prediction method: Machine learning classification algorithm based on multi-modal data features," Energy, Elsevier, vol. 244(PA).
21. 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.
22. Guo, Jingjun & Zhao, Zhengling & Sun, Jingyun & Sun, Shaolong, 2022. "Multi-perspective crude oil price forecasting with a new decomposition-ensemble framework," Resources Policy, Elsevier, vol. 77(C).
23. E. B. Priyanka & S. Thangavel & K. Martin Sagayam & Ahmed A. Elngar, 2022. "Wireless network upgraded with artificial intelligence on the data aggregation towards the smart internet applications," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1254-1267, June.
24. Xu, Hua & Wang, Minggang & Jiang, Shumin & Yang, Weiguo, 2020. "Carbon price forecasting with complex network and extreme learning machine," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
25. 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.
26. Abdollahi, Hooman, 2020. "A novel hybrid model for forecasting crude oil price based on time series decomposition," Applied Energy, Elsevier, vol. 267(C).
27. An, Sufang & An, Feng & Gao, Xiangyun & Wang, Anjian, 2023. "Early warning of critical transitions in crude oil price," Energy, Elsevier, vol. 280(C).
28. Tang, Ling & Zhang, Chengyuan & Li, Ling & Wang, Shouyang, 2020. "A multi-scale method for forecasting oil price with multi-factor search engine data," Applied Energy, Elsevier, vol. 257(C).
29. Xie, Gang & Jiang, Fuxin & Zhang, Chengyuan, 2023. "A secondary decomposition-ensemble methodology for forecasting natural gas prices using multisource data," Resources Policy, Elsevier, vol. 85(PA).
30. Tiwari, Aviral Kumar & Sharma, Gagan Deep & Rao, Amar & Hossain, Mohammad Razib & Dev, Dhairya, 2024. "Unraveling the crystal ball: Machine learning models for crude oil and natural gas volatility forecasting," Energy Economics, Elsevier, vol. 134(C).
31. Xu, Yuhong & Zhao, Xinyao, 2024. "How does node centrality in a financial network affect asset price prediction?," The North American Journal of Economics and Finance, Elsevier, vol. 73(C).
32. An, Yimeng & Dang, Yaoguo & Wang, Junjie & Zhou, Huimin & Mai, Son T., 2024. "Mixed-frequency data Sampling Grey system Model: Forecasting annual CO2 emissions in China with quarterly and monthly economic-energy indicators," Applied Energy, Elsevier, vol. 370(C).
33. Tian, Zihao & Zhou, Jing & Tian, Lixin & Wang, David Z.W., 2024. "Dynamic spatio-temporal interactive clustering strategy for free-floating bike-sharing," Transportation Research Part B: Methodological, Elsevier, vol. 179(C).
34. Hosseini, Seyed Hossein & Shakouri G., Hamed & Kazemi, Aliyeh, 2021. "Oil price future regarding unconventional oil production and its near-term deployment: A system dynamics approach," Energy, Elsevier, vol. 222(C).
35. Li, Ranran & Hu, Yucai & Heng, Jiani & Chen, Xueli, 2021. "A novel multiscale forecasting model for crude oil price time series," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
36. Lu-Tao Zhao & Guan-Rong Zeng & Wen-Jing Wang & Zhi-Gang Zhang, 2019. "Forecasting Oil Price Using Web-based Sentiment Analysis," Energies, MDPI, vol. 12(22), pages 1-18, November.
37. Vera Ivanyuk, 2021. "Formulating the Concept of an Investment Strategy Adaptable to Changes in the Market Situation," Economies, MDPI, vol. 9(3), pages 1-19, June.
38. 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).
39. Na Fu & Liyan Geng & Junhai Ma & Xue Ding, 2023. "Price, Complexity, and Mathematical Model," Mathematics, MDPI, vol. 11(13), pages 1-30, June.
40. 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.
41. Fang, Tianhui & Zheng, Chunling & Wang, Donghua, 2023. "Forecasting the crude oil prices with an EMD-ISBM-FNN model," Energy, Elsevier, vol. 263(PA).
42. He, Huizi & Sun, Mei & Gao, Cuixia & Li, Xiuming, 2021. "Detecting lag linkage effect between economic policy uncertainty and crude oil price: A multi-scale perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
43. Wang, Xuerui & Li, Xiangyu & Li, Shaoting, 2022. "Point and interval forecasting system for crude oil price based on complete ensemble extreme-point symmetric mode decomposition with adaptive noise and intelligent optimization algorithm," Applied Energy, Elsevier, vol. 328(C).
44. He, Mengxi & Zhang, Yaojie & Wen, Danyan & Wang, Yudong, 2021. "Forecasting crude oil prices: A scaled PCA approach," Energy Economics, Elsevier, vol. 97(C).
45. Ding, Lili & Zhao, Zhongchao & Wang, Lei, 2022. "Probability density forecasts for natural gas demand in China: Do mixed-frequency dynamic factors matter?," Applied Energy, Elsevier, vol. 312(C).
46. Wang, Jun & Cao, Junxing & Yuan, Shan & Cheng, Ming, 2021. "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," Energy, Elsevier, vol. 233(C).
47. Wang, Minggang & Xu, Hua & Tian, Lixin & Eugene Stanley, H., 2018. "Degree distributions and motif profiles of limited penetrable horizontal visibility graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 620-634.
48. Zhang, X. & Chen, M.Y. & Wang, M.G. & Ge, Y.E. & Stanley, H.E., 2019. "A novel hybrid approach to Baltic Dry Index forecasting based on a combined dynamic fluctuation network and artificial intelligence method," Applied Mathematics and Computation, Elsevier, vol. 361(C), pages 499-516.