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An ICA-based support vector regression scheme for forecasting crude oil prices

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  1. Erdinc Akyildirim & Oguzhan Cepni & Shaen Corbet & Gazi Salah Uddin, 2023. "Forecasting mid-price movement of Bitcoin futures using machine learning," Annals of Operations Research, Springer, vol. 330(1), pages 553-584, November.
  2. Quande Qin & Huangda He & Li Li & Ling-Yun He, 2020. "A Novel Decomposition-Ensemble Based Carbon Price Forecasting Model Integrated with Local Polynomial Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1249-1273, April.
  3. 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.
  4. Li, Xuemei & Liu, Xiaoxing, 2023. "Functional classification and dynamic prediction of cumulative intraday returns in crude oil futures," Energy, Elsevier, vol. 284(C).
  5. Xiao, Yu-jie & Wang, Xiao-kang & Wang, Jian-qiang & Zhang, Hong-yu, 2021. "An adaptive decomposition and ensemble model for short-term air pollutant concentration forecast using ICEEMDAN-ICA," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
  6. Theodore Syriopoulos & Michael Tsatsaronis & Ioannis Karamanos, 2021. "Support Vector Machine Algorithms: An Application to Ship Price Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 55-87, January.
  7. Hualing Lin & Qiubi Sun, 2020. "Crude Oil Prices Forecasting: An Approach of Using CEEMDAN-Based Multi-Layer Gated Recurrent Unit Networks," Energies, MDPI, vol. 13(7), pages 1-21, March.
  8. Asit Kumar Das & Debahuti Mishra & Kaberi Das & Pradeep Kumar Mallick & Sachin Kumar & Mikhail Zymbler & Hesham El-Sayed, 2022. "Prophesying the Short-Term Dynamics of the Crude Oil Future Price by Adopting the Survival of the Fittest Principle of Improved Grey Optimization and Extreme Learning Machine," Mathematics, MDPI, vol. 10(7), pages 1-33, March.
  9. Shu, Lei & Lu, Feiyang & Chen, Yu, 2023. "Robust forecasting with scaled independent component analysis," Finance Research Letters, Elsevier, vol. 51(C).
  10. Witold Orzeszko, 2021. "Nonlinear Causality between Crude Oil Prices and Exchange Rates: Evidence and Forecasting," Energies, MDPI, vol. 14(19), pages 1-16, September.
  11. Linlin Zhao & Jasper Mbachu & Zhansheng Liu, 2019. "Exploring the Trend of New Zealand Housing Prices to Support Sustainable Development," Sustainability, MDPI, vol. 11(9), pages 1-18, April.
  12. 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.
  13. Dimitrios Vortelinos & Angeliki Menegaki & Ioannis Passas & Alexandros Garefalakis & Georgios Viskadouros, 2024. "Heterogeneous Responses of Energy and Non-Energy Assets to Crises in Commodity Markets," Energies, MDPI, vol. 17(21), pages 1-25, October.
  14. Zhao, Zhengling & Sun, Shaolong & Sun, Jingyun & Wang, Shouyang, 2024. "A novel hybrid model with two-layer multivariate decomposition for crude oil price forecasting," Energy, Elsevier, vol. 288(C).
  15. Taiyong Li & Min Zhou & Chaoqi Guo & Min Luo & Jiang Wu & Fan Pan & Quanyi Tao & Ting He, 2016. "Forecasting Crude Oil Price Using EEMD and RVM with Adaptive PSO-Based Kernels," Energies, MDPI, vol. 9(12), pages 1-21, December.
  16. 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.
  17. Lu-Tao Zhao & Guan-Rong Zeng & Ling-Yun He & Ya Meng, 2020. "Forecasting Short-Term Oil Price with a Generalised Pattern Matching Model Based on Empirical Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1151-1169, April.
  18. 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.
  19. 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).
  20. Manickavasagam, Jeevananthan & Visalakshmi, S. & Apergis, Nicholas, 2020. "A novel hybrid approach to forecast crude oil futures using intraday data," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
  21. 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).
  22. 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.
  23. Yingrui Zhou & Taiyong Li & Jiayi Shi & Zijie Qian, 2019. "A CEEMDAN and XGBOOST-Based Approach to Forecast Crude Oil Prices," Complexity, Hindawi, vol. 2019, pages 1-15, February.
  24. Xu, Kunliang & Niu, Hongli, 2023. "Denoising or distortion: Does decomposition-reconstruction modeling paradigm provide a reliable prediction for crude oil price time series?," Energy Economics, Elsevier, vol. 128(C).
  25. Wu, Chunying & Wang, Jianzhou & Hao, Yan, 2022. "Deterministic and uncertainty crude oil price forecasting based on outlier detection and modified multi-objective optimization algorithm," Resources Policy, Elsevier, vol. 77(C).
  26. Marcin Fałdziński & Piotr Fiszeder & Witold Orzeszko, 2020. "Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression," Energies, MDPI, vol. 14(1), pages 1-18, December.
  27. Yu-Sheng Kao & Kazumitsu Nawata & Chi-Yo Huang, 2020. "Predicting Primary Energy Consumption Using Hybrid ARIMA and GA-SVR Based on EEMD Decomposition," Mathematics, MDPI, vol. 8(10), pages 1-19, October.
  28. Junwei Chen, 2023. "Analysis of Bitcoin Price Prediction Using Machine Learning," JRFM, MDPI, vol. 16(1), pages 1-25, January.
  29. Ana Lazcano & Miguel A. Jaramillo-Morán & Julio E. Sandubete, 2024. "Back to Basics: The Power of the Multilayer Perceptron in Financial Time Series Forecasting," Mathematics, MDPI, vol. 12(12), pages 1-18, June.
  30. Chai, Jian & Xing, Li-Min & Zhou, Xiao-Yang & Zhang, Zhe George & Li, Jie-Xun, 2018. "Forecasting the WTI crude oil price by a hybrid-refined method," Energy Economics, Elsevier, vol. 71(C), pages 114-127.
  31. E, Jianwei & Bao, Yanling & Ye, Jimin, 2017. "Crude oil price analysis and forecasting based on variational mode decomposition and independent component analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 412-427.
  32. Taiyong Li & Yingrui Zhou & Xinsheng Li & Jiang Wu & Ting He, 2019. "Forecasting Daily Crude Oil Prices Using Improved CEEMDAN and Ridge Regression-Based Predictors," Energies, MDPI, vol. 12(19), pages 1-25, September.
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