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Crude oil price analysis and forecasting using wavelet decomposed ensemble model

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  1. Liu, Chang & Sun, Xiaolei & Wang, Jun & Li, Jianping & Chen, Jianming, 2021. "Multiscale information transmission between commodity markets: An EMD-Based transfer entropy network," Research in International Business and Finance, Elsevier, vol. 55(C).
  2. Zhang, Xiao-Han & Zhu, Qun-Xiong & He, Yan-Lin & Xu, Yuan, 2018. "A novel robust ensemble model integrated extreme learning machine with multi-activation functions for energy modeling and analysis: Application to petrochemical industry," Energy, Elsevier, vol. 162(C), pages 593-602.
  3. Chiroma, Haruna & Abdulkareem, Sameem & Herawan, Tutut, 2015. "Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction," Applied Energy, Elsevier, vol. 142(C), pages 266-273.
  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. Shah, Adil Ahmad & Dar, Arif Billah, 2021. "Exploring diversification opportunities across commodities and financial markets: Evidence from time-frequency based spillovers," Resources Policy, Elsevier, vol. 74(C).
  7. Xiong, Tao & Bao, Yukun & Hu, Zhongyi, 2013. "Beyond one-step-ahead forecasting: Evaluation of alternative multi-step-ahead forecasting models for crude oil prices," Energy Economics, Elsevier, vol. 40(C), pages 405-415.
  8. 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.
  9. Huang, Shupei & An, Haizhong & Wen, Shaobo & An, Feng, 2017. "Revisiting driving factors of oil price shocks across time scales," Energy, Elsevier, vol. 139(C), pages 617-629.
  10. Rui Luo & Jinpei Liu & Piao Wang & Zhifu Tao & Huayou Chen, 2024. "A multisource data‐driven combined forecasting model based on internet search keyword screening method for interval soybean futures price," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 366-390, March.
  11. Qi Zhang & Yi Hu & Jianbin Jiao & Shouyang Wang, 2024. "The impact of Russia–Ukraine war on crude oil prices: an EMC framework," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.
  12. Wen, Danyan & Liu, Li & Wang, Yudong & Zhang, Yaojie, 2022. "Forecasting crude oil market returns: Enhanced moving average technical indicators," Resources Policy, Elsevier, vol. 76(C).
  13. Fu, Guoyin, 2018. "Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system," Energy, Elsevier, vol. 148(C), pages 269-282.
  14. Shao, Zhen & Gao, Fei & Yang, Shan-Lin & Yu, Ben-gong, 2015. "A new semiparametric and EEMD based framework for mid-term electricity demand forecasting in China: Hidden characteristic extraction and probability density prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 876-889.
  15. 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.
  16. 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.
  17. Jyoti Choudhary & Haresh Kumar Sharma & Pradeep Malik & Saibal Majumder, 2025. "Price Forecasting of Crude Oil Using Hybrid Machine Learning Models," JRFM, MDPI, vol. 18(7), pages 1-25, June.
  18. Abdollahi, Hooman & Ebrahimi, Seyed Babak, 2020. "A new hybrid model for forecasting Brent crude oil price," Energy, Elsevier, vol. 200(C).
  19. 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.
  20. Bijay Neupane & Wei Lee Woon & Zeyar Aung, 2017. "Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting," Energies, MDPI, vol. 10(1), pages 1-27, January.
  21. 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.
  22. Jiaming Zhu & Peng Wu & Huayou Chen & Ligang Zhou & Zhifu Tao, 2018. "A Hybrid Forecasting Approach to Air Quality Time Series Based on Endpoint Condition and Combined Forecasting Model," IJERPH, MDPI, vol. 15(9), pages 1-19, September.
  23. Zhou, Jie & Sun, Mei & Han, Dun & Gao, Cuixia, 2021. "Analysis of oil price fluctuation under the influence of crude oil stocks and US dollar index — Based on time series network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
  24. Safari, Ali & Davallou, Maryam, 2018. "Oil price forecasting using a hybrid model," Energy, Elsevier, vol. 148(C), pages 49-58.
  25. Xiangcai Meng, 2018. "Does Agricultural Commodity Price Co-move with Oil Price in the Time-Frequency Space? Evidence from the Republic of Korea," International Journal of Energy Economics and Policy, Econjournals, vol. 8(4), pages 125-133.
  26. 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).
  27. Fan He & Xuansen He, 2019. "A Continuous Differentiable Wavelet Shrinkage Function for Economic Data Denoising," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 729-761, August.
  28. Godarzi, Ali Abbasi & Amiri, Rohollah Madadi & Talaei, Alireza & Jamasb, Tooraj, 2014. "Predicting oil price movements: A dynamic Artificial Neural Network approach," Energy Policy, Elsevier, vol. 68(C), pages 371-382.
  29. Polanco Martínez, Josué M. & Abadie, Luis M. & Fernández-Macho, J., 2018. "A multi-resolution and multivariate analysis of the dynamic relationships between crude oil and petroleum-product prices," Applied Energy, Elsevier, vol. 228(C), pages 1550-1560.
  30. Ju, Keyi & Su, Bin & Zhou, Dequn & Wu, Junmin & Liu, Lifan, 2016. "Macroeconomic performance of oil price shocks: Outlier evidence from nineteen major oil-related countries/regions," Energy Economics, Elsevier, vol. 60(C), pages 325-332.
  31. Kang, Sang Hoon & Tiwari, Aviral Kumar & Albulescu, Claudiu Tiberiu & Yoon, Seong-Min, 2019. "Exploring the time-frequency connectedness and network among crude oil and agriculture commodities V1," Energy Economics, Elsevier, vol. 84(C).
  32. Zhang, Tingting & Tang, Zhenpeng & Wu, Junchuan & Du, Xiaoxu & Chen, Kaijie, 2021. "Multi-step-ahead crude oil price forecasting based on two-layer decomposition technique and extreme learning machine optimized by the particle swarm optimization algorithm," Energy, Elsevier, vol. 229(C).
  33. Ju, Keyi & Su, Bin & Zhou, Dequn & Zhang, Yuqiang, 2016. "An incentive-oriented early warning system for predicting the co-movements between oil price shocks and macroeconomy," Applied Energy, Elsevier, vol. 163(C), pages 452-463.
  34. Sina Aghaei, 2018. "A Data-Driven Approach for Modeling Stochasticity in Oil Market," Papers 1805.12110, arXiv.org.
  35. 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.
  36. 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.
  37. Abdollahi, Hooman, 2020. "A novel hybrid model for forecasting crude oil price based on time series decomposition," Applied Energy, Elsevier, vol. 267(C).
  38. Abdelmounaim Hadjira & Hicham Salhi & Lyes Choubar, 2025. "OPEC Basket Monthly Crude Oil Price Forecasting: Comparative Study Between Prophet Facebook, NNAR, FTS Models," Computational Economics, Springer;Society for Computational Economics, vol. 66(2), pages 1647-1669, August.
  39. Jiang, Meihui & An, Haizhong & Jia, Xiaoliang & Sun, Xiaoqi, 2017. "The influence of global benchmark oil prices on the regional oil spot market in multi-period evolution," Energy, Elsevier, vol. 118(C), pages 742-752.
  40. 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).
  41. Lin, Ling & Jiang, Yong & Xiao, Helu & Zhou, Zhongbao, 2020. "Crude oil price forecasting based on a novel hybrid long memory GARCH-M and wavelet analysis model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 543(C).
  42. Bai, Xiwen, 2021. "Tanker freight rates and economic policy uncertainty: A wavelet-based copula approach," Energy, Elsevier, vol. 235(C).
  43. Zhang, Jin-Liang & Zhang, Yue-Jun & Zhang, Lu, 2015. "A novel hybrid method for crude oil price forecasting," Energy Economics, Elsevier, vol. 49(C), pages 649-659.
  44. Gao, Xiangyun & An, Haizhong & Fang, Wei & Li, Huajiao & Sun, Xiaoqi, 2014. "The transmission of fluctuant patterns of the forex burden based on international crude oil prices," Energy, Elsevier, vol. 73(C), pages 380-386.
  45. 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.
  46. Hooftman, Danny A.P. & Bullock, James M. & Jones, Laurence & Eigenbrod, Felix & Barredo, José I. & Forrest, Matthew & Kindermann, Georg & Thomas, Amy & Willcock, Simon, 2022. "Reducing uncertainty in ecosystem service modelling through weighted ensembles," Ecosystem Services, Elsevier, vol. 53(C).
  47. Taiyong Li & Zhenda Hu & Yanchi Jia & Jiang Wu & Yingrui Zhou, 2018. "Forecasting Crude Oil Prices Using Ensemble Empirical Mode Decomposition and Sparse Bayesian Learning," Energies, MDPI, vol. 11(7), pages 1-23, July.
  48. Yu, Lean & Li, Jingjing & Tang, Ling & Wang, Shuai, 2015. "Linear and nonlinear Granger causality investigation between carbon market and crude oil market: A multi-scale approach," Energy Economics, Elsevier, vol. 51(C), pages 300-311.
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  50. Xiaojie Xu, 2018. "Causal structure among US corn futures and regional cash prices in the time and frequency domain," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(13), pages 2455-2480, October.
  51. Zhang, Chuanguo & Qu, Xuqin, 2015. "The effect of global oil price shocks on China's agricultural commodities," Energy Economics, Elsevier, vol. 51(C), pages 354-364.
  52. Emmanuel Senyo Fianu, 2022. "Analyzing and Forecasting Multi-Commodity Prices Using Variants of Mode Decomposition-Based Extreme Learning Machine Hybridization Approach," Forecasting, MDPI, vol. 4(2), pages 1-27, June.
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  54. Jiang Wu & Yu Chen & Tengfei Zhou & Taiyong Li, 2019. "An Adaptive Hybrid Learning Paradigm Integrating CEEMD, ARIMA and SBL for Crude Oil Price Forecasting," Energies, MDPI, vol. 12(7), pages 1-23, April.
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