IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v263y2023ipas0360544222022897.html

Forecasting the crude oil prices with an EMD-ISBM-FNN model

Author

Listed:
  • Fang, Tianhui
  • Zheng, Chunling
  • Wang, Donghua

Abstract

In this paper, an improved slope-based method (ISBM) based on empirical mode decomposition (EMD) and feed-forward neural network (FNN) method, namely, the EMD-ISBM-FNN method is introduced to decompose and forecast the crude oil prices. Firstly, the ISBM-based EMD method is used to decompose the time series of Brent crude oil prices into several IMFs (intrinsic mode functions) and residuals rn(t). Then IMFs and residuals rn(t) are inputted into the FNN model as input layer neurons, which are trained and integrated by the FNN model to study the relationship between the output values of the FNN and actual values. In order to verify the forecasting results of the EMD-ISBM-FNN model, two research frameworks and three strategies are designed, and the EMD-FNN model and the FNN model as the benchmark models are constructed to compare their forecasting results. The research shows that the EMD-ISBM-FNN model proposed in this paper has the best forecasting effect under the three strategies, and the research framework of this paper is better than the previous scholars' research frameworks, too.

Suggested Citation

  • Fang, Tianhui & Zheng, Chunling & Wang, Donghua, 2023. "Forecasting the crude oil prices with an EMD-ISBM-FNN model," Energy, Elsevier, vol. 263(PA).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pa:s0360544222022897
    DOI: 10.1016/j.energy.2022.125407
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222022897
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.125407?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Yu, Lean & Zha, Rui & Stafylas, Dimitrios & He, Kaijian & Liu, Jia, 2020. "Dependences and volatility spillovers between the oil and stock markets: New evidence from the copula and VAR-BEKK-GARCH models," International Review of Financial Analysis, Elsevier, vol. 68(C).
    2. Coleman, Les, 2012. "Explaining crude oil prices using fundamental measures," Energy Policy, Elsevier, vol. 40(C), pages 318-324.
    3. Chinmoy, Lakshmi & Iniyan, S. & Goic, Ranko, 2019. "Modeling wind power investments, policies and social benefits for deregulated electricity market – A review," Applied Energy, Elsevier, vol. 242(C), pages 364-377.
    4. Li-Chen Chou & Wan-Hao Zhang, 2020. "The effect of democracy on energy efficiency in European countries," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 33(1), pages 3476-3491, January.
    5. Atukeren, Erdal & Çevik, Emrah İsmail & Korkmaz, Turhan, 2021. "Volatility spillovers between WTI and Brent spot crude oil prices: an analysis of granger causality in variance patterns over time," Research in International Business and Finance, Elsevier, vol. 56(C).
    6. 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.
    7. Hau, Liya & Zhu, Huiming & Huang, Rui & Ma, Xiang, 2020. "Heterogeneous dependence between crude oil price volatility and China’s agriculture commodity futures: Evidence from quantile-on-quantile regression," Energy, Elsevier, vol. 213(C).
    8. Coletti, Donald & Lalonde, René & Masson, Paul & Muir, Dirk & Snudden, Stephen, 2021. "Commodities and monetary policy: Implications for inflation and price level targeting," Journal of Policy Modeling, Elsevier, vol. 43(5), pages 982-999.
    9. Jammazi, Rania & Aloui, Chaker, 2012. "Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling," Energy Economics, Elsevier, vol. 34(3), pages 828-841.
    10. 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.
    11. Donghua Wang & Tianhui Fang, 2022. "Forecasting Crude Oil Prices with a WT-FNN Model," Energies, MDPI, vol. 15(6), pages 1-21, March.
    12. Kang, Jia-Ning & Wei, Yi-Ming & Liu, Lan-cui & Yu, Bi-Ying & Liao, Hua, 2021. "A social learning approach to carbon capture and storage demonstration project management: An empirical analysis," Applied Energy, Elsevier, vol. 299(C).
    13. He, Mengxi & Zhang, Yaojie & Wen, Danyan & Wang, Yudong, 2021. "Forecasting crude oil prices: A scaled PCA approach," Energy Economics, Elsevier, vol. 97(C).
    14. 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.
    15. Yu, Lean & Liang, Shaodong & Chen, Rongda & Lai, Kin Keung, 2022. "Predicting monthly biofuel production using a hybrid ensemble forecasting methodology," International Journal of Forecasting, Elsevier, vol. 38(1), pages 3-20.
    16. Yu, Lean & Ma, Yueming & Ma, Mengyao, 2021. "An effective rolling decomposition-ensemble model for gasoline consumption forecasting," Energy, Elsevier, vol. 222(C).
    17. Wang, Qingfeng & Sun, Xu, 2017. "Crude oil price: Demand, supply, economic activity, economic policy uncertainty and wars – From the perspective of structural equation modelling (SEM)," Energy, Elsevier, vol. 133(C), pages 483-490.
    18. Yao, Can-Zhong & Kuang, Peng-Cheng, 2019. "A study of lead–lag structure between international crude oil price and several financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    19. 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.
    20. Murat, Atilim & Tokat, Ekin, 2009. "Forecasting oil price movements with crack spread futures," Energy Economics, Elsevier, vol. 31(1), pages 85-90, January.
    21. 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.
    22. repec:aen:journl:2009v30-02-a09 is not listed on IDEAS
    23. Mostafa, Mohamed M. & El-Masry, Ahmed A., 2016. "Oil price forecasting using gene expression programming and artificial neural networks," Economic Modelling, Elsevier, vol. 54(C), pages 40-53.
    24. An, Haizhong & Gao, Xiangyun & Fang, Wei & Huang, Xuan & Ding, Yinghui, 2014. "The role of fluctuating modes of autocorrelation in crude oil prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 382-390.
    25. Shen, Meng & Li, Xiang & Lu, Yujie & Cui, Qingbin & Wei, Yi-Ming, 2021. "Personality-based normative feedback intervention for energy conservation," Energy Economics, Elsevier, vol. 104(C).
    26. Kaufmann, Robert K., 2011. "The role of market fundamentals and speculation in recent price changes for crude oil," Energy Policy, Elsevier, vol. 39(1), pages 105-115, January.
    27. Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2008. "Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm," Energy Economics, Elsevier, vol. 30(5), pages 2623-2635, September.
    28. Hao, Xianfeng & Zhao, Yuyang & Wang, Yudong, 2020. "Forecasting the real prices of crude oil using robust regression models with regularization constraints," Energy Economics, Elsevier, vol. 86(C).
    29. Wang, Lu & Wu, Jiangbin & Cao, Yang & Hong, Yanran, 2022. "Forecasting renewable energy stock volatility using short and long-term Markov switching GARCH-MIDAS models: Either, neither or both?," Energy Economics, Elsevier, vol. 111(C).
    30. Abdollahi, Hooman & Ebrahimi, Seyed Babak, 2020. "A new hybrid model for forecasting Brent crude oil price," Energy, Elsevier, vol. 200(C).
    31. Lu, Quanying & Li, Yuze & Chai, Jian & Wang, Shouyang, 2020. "Crude oil price analysis and forecasting: A perspective of “new triangle”," Energy Economics, Elsevier, vol. 87(C).
    32. Wang, Yudong & Wu, Chongfeng, 2012. "Energy prices and exchange rates of the U.S. dollar: Further evidence from linear and nonlinear causality analysis," Economic Modelling, Elsevier, vol. 29(6), pages 2289-2297.
    33. Wang, Jie & Wang, Jun, 2016. "Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuations," Energy, Elsevier, vol. 102(C), pages 365-374.
    34. Lanza, Alessandro & Manera, Matteo & Giovannini, Massimo, 2005. "Modeling and forecasting cointegrated relationships among heavy oil and product prices," Energy Economics, Elsevier, vol. 27(6), pages 831-848, November.
    35. Quanying Lu & Yuying Sun & Yongmiao Hong & Shouyang Wang, 2022. "Forecasting interval-valued crude oil prices using asymmetric interval models," Quantitative Finance, Taylor & Francis Journals, vol. 22(11), pages 2047-2061, November.
    36. Lin, Qi, 2018. "Technical analysis and stock return predictability: An aligned approach," Journal of Financial Markets, Elsevier, vol. 38(C), pages 103-123.
    37. Kilian, Lutz & Lee, Thomas K., 2014. "Quantifying the speculative component in the real price of oil: The role of global oil inventories," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 71-87.
    38. Guillaume Chevillon, 2007. "Direct Multi‐Step Estimation And Forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 746-785, September.
    39. 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.
    40. Qiu-Su Wang & Chi-Wei Su & Yu-Fei Hua & Muhammad Umar, 2021. "Can fiscal decentralisation regulate the impact of industrial structure on energy efficiency?," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 34(1), pages 1727-1751, January.
    41. Zhang, Yaojie & Wei, Yu & Zhang, Yi & Jin, Daxiang, 2019. "Forecasting oil price volatility: Forecast combination versus shrinkage method," Energy Economics, Elsevier, vol. 80(C), pages 423-433.
    42. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li, Jinchao & Guo, Yuwei, 2025. "A hybrid model based on iTransformer for risk warning of crude oil price fluctuations," Energy, Elsevier, vol. 314(C).
    2. Liu, Jinpei & Qiu, Biyue & Du, Pengcheng & Zhao, Xiaoman & Zhu, Jiaming, 2025. "A novel probabilistic connectivity network link prediction model for natural gas price based on an improved K-shell algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 671(C).
    3. Dong, Xianzhou & Guo, Weiyong & Zhou, Cheng & Luo, Yongqiang & Tian, Zhiyong & Zhang, Limao & Wu, Xiaoying & Liu, Baobing, 2024. "Hybrid model for robust and accurate forecasting building electricity demand combining physical and data-driven methods," Energy, Elsevier, vol. 311(C).
    4. Parisa Foroutan & Salim Lahmiri, 2024. "Deep learning systems for forecasting the prices of crude oil and precious metals," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-40, December.
    5. Bai, Yun & Deng, Shuyun & Pu, Ziqiang & Li, Chuan, 2024. "Carbon price forecasting using leaky integrator echo state networks with the framework of decomposition-reconstruction-integration," Energy, Elsevier, vol. 305(C).
    6. Gong, Xue & Lai, Ping & He, Mengxi & Wen, Danyan, 2024. "Climate risk and energy futures high frequency volatility prediction," Energy, Elsevier, vol. 307(C).
    7. Soto Calvo, Manuel & Lee, Han Soo & Chisale, Sylvester William, 2024. "A novel method for long-term power demand prediction using enhanced data decomposition and neural network with integrated uncertainty analysis: A Cuba case study," Applied Energy, Elsevier, vol. 372(C).
    8. Ma, Yilin & Wang, Yudong & Wang, Weizhong & Zhang, Chong, 2023. "Portfolios with return and volatility prediction for the energy stock market," Energy, Elsevier, vol. 270(C).
    9. Lin, Yu & Dai, Dongsheng & Yu, Yuanyuan & Li, Zhaofeng & Huang, Wenhui & Zhao, Liangkai & Xing, Haiyang, 2025. "Forecasting natural gas prices using a novel hybrid model: Comparative study of different sliding windows," Energy, Elsevier, vol. 329(C).
    10. Hasnain Iftikhar & Aimel Zafar & Josue E. Turpo-Chaparro & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2023. "Forecasting Day-Ahead Brent Crude Oil Prices Using Hybrid Combinations of Time Series Models," Mathematics, MDPI, vol. 11(16), pages 1-19, August.
    11. Wen, Danyan & He, Mengxi & Wang, Yudong & Zhang, Yaojie, 2025. "Forecasting gasoline prices using oil prices: New evidence based on the rocket and feather hypothesis," Energy, Elsevier, vol. 335(C).
    12. 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).
    13. Xiaotao Zhang & Zihui Xia & Feng He & Jing Hao, 2025. "Forecasting crude oil prices with alternative data and a deep learning approach," Annals of Operations Research, Springer, vol. 345(2), pages 1165-1191, February.
    14. Gong, Xue & Ji, Shidong & Zhang, Yaojie, 2025. "Attention to climate events and carbon price volatility," Finance Research Letters, Elsevier, vol. 79(C).
    15. Dehao Dai & Ding Ma & Dou Liu & Kerui Geng & Yiqing Wang, 2026. "Beyond Polarity: Multi-Dimensional LLM Sentiment Signals for WTI Crude Oil Futures Return Prediction," Papers 2603.11408, arXiv.org, revised Mar 2026.
    16. Wang, Jiqian & Chen, Chuang & Dai, Xingyu, 2025. "News topic attention and crude oil price predictability," International Review of Financial Analysis, Elsevier, vol. 108(PA).
    17. Yang, Jie & Feng, Yun & Yang, Hao, 2024. "The spillover and comovement of downside and upside tail risks among crude oil futures markets," International Review of Financial Analysis, Elsevier, vol. 96(PA).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Donghua Wang & Tianhui Fang, 2022. "Forecasting Crude Oil Prices with a WT-FNN Model," Energies, MDPI, vol. 15(6), pages 1-21, March.
    2. Liang, Qian & Lin, Qingyuan & Guo, Mengzhuo & Lu, Quanying & Zhang, Dayong, 2025. "Forecasting crude oil prices: A Gated Recurrent Unit-based nonlinear Granger Causality model," International Review of Financial Analysis, Elsevier, vol. 102(C).
    3. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    4. Abdollahi, Hooman, 2020. "A novel hybrid model for forecasting crude oil price based on time series decomposition," Applied Energy, Elsevier, vol. 267(C).
    5. 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.
    6. 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).
    7. 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.
    8. Tan, Jinghua & Li, Zhixi & Zhang, Chuanhui & Shi, Long & Jiang, Yuansheng, 2024. "A multiscale time-series decomposition learning for crude oil price forecasting," Energy Economics, Elsevier, vol. 136(C).
    9. He, Mengxi & Zhang, Yaojie & Wen, Danyan & Wang, Yudong, 2021. "Forecasting crude oil prices: A scaled PCA approach," Energy Economics, Elsevier, vol. 97(C).
    10. 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.
    11. Li, Mingchen & Cheng, Zishu & Lin, Wencan & Wei, Yunjie & Wang, Shouyang, 2023. "What can be learned from the historical trend of crude oil prices? An ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 123(C).
    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. 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.
    14. 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).
    15. 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.
    16. Khan, Faridoon & Muhammadullah, Sara & Sharif, Arshian & Lee, Chien-Chiang, 2024. "The role of green energy stock market in forecasting China's crude oil market: An application of IIS approach and sparse regression models," Energy Economics, Elsevier, vol. 130(C).
    17. Yan, Zichun & Tian, Fangzhu & Sun, Yuying & Wang, Shouyang, 2024. "A time-frequency-based interval decomposition ensemble method for forecasting gasoil prices under the trend of low-carbon development," Energy Economics, Elsevier, vol. 134(C).
    18. 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.
    19. Cheng, Zishu & Li, Mingchen & Sun, Yuying & Hong, Yongmiao & Wang, Shouyang, 2024. "Climate change and crude oil prices: An interval forecast model with interval-valued textual data," Energy Economics, Elsevier, vol. 134(C).
    20. Jiang, He & Hu, Weiqiang & Xiao, Ling & Dong, Yao, 2022. "A decomposition ensemble based deep learning approach for crude oil price forecasting," Resources Policy, Elsevier, vol. 78(C).

    More about this item

    Keywords

    ;
    ;
    ;

    JEL classification:

    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:263:y:2023:i:pa:s0360544222022897. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.