Abstract
Crude oil is the most important energy source in the world, and fluctuations in oil prices can significantly influence investors, companies, and governments. However, crude oil prices have numerous characteristics, including randomness, sudden structural changes, intrinsic nonlinearity, volatility, and chaotic nature. This makes the accurate forecasting of crude oil prices a difficult and challenging task. In this paper, a hybrid prediction model for crude oil futures prices is proposed, the accuracy and robustness of which are demonstrated via controlled experiments and sensitivity analysis. This study uses a new data denoising method for data processing to improve the accuracy and stability of the predictions of crude oil prices. Furthermore, the chaotic time-series prediction method, shallow neural networks, linear model prediction methods, and deep learning methods are adopted as submodels. The results of interval forecasts with narrow widths and high prediction accuracies are derived by introducing a confidence interval adjustment coefficient. The results of the simulation experiments indicate that the proposed hybrid prediction model exhibits higher accuracy and efficiency, as well as better robustness of the forecasting than the control models. In summary, the proposed forecasting framework can derive accurate point and interval forecasts and provide a valuable reference for the price forecasting of crude oil futures.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abdollahi, H., & Ebrahimi, S. B. (2020). A new hybrid model for forecasting Brent crude oil price. Energy, 200, 117520. https://doi.org/10.1016/j.energy.2020.117520
Abedin, M. Z., Chi, G., Uddin, M. M., Satu, M. S., Khan, M. I., & Hajek, P. (2021). Tax default prediction using feature transformation-based machine learning. IEEE Access, 9, 19864–19881. https://doi.org/10.1109/ACCESS.2020.3048018
Abedin, M. Z., Guotai, C., Moula, F., Azad, A. S. M. S., & Khan, M. S. U. (2019). Topological applications of multilayer perceptrons and support vector machines in financial decision support systems. International Journal of Finance and Economics, 24(1), 474–507. https://doi.org/10.1002/ijfe.1675
Abedin, M. Z., Moon, M. H., Hassan, M. K., & Hajek, P. (2021). Deep learning-based exchange rate prediction during the COVID-19 pandemic. Annals of Operations Research. https://doi.org/10.1007/s10479-021-04420-6
Agnolucci, P. (2009). Volatility in crude oil futures: A comparison of the predictive ability of GARCH and implied volatility models. Energy Economics, 31(2), 316–321. https://doi.org/10.1016/j.eneco.2008.11.001
bp China. (2021). BP World Energy Statistics Yearbook. https://www.mendeley.com/reference-management/web-importer/#id_3. Accessed 6 November 2021
Cerqueti, R., & Fanelli, V. (2021). Long memory and crude oil’s price predictability. Annals of Operations Research, 299(1–2), 895–906. https://doi.org/10.1007/s10479-019-03376-y
Chai, J., Xing, L. M., Zhou, X. Y., Zhang, Z. G., & Li, J. X. (2018). Forecasting the WTI crude oil price by a hybrid-refined method. Energy Economics, 71, 114–127. https://doi.org/10.1016/j.eneco.2018.02.004
Guo, X., Li, D., & Zhang, A. (2012). Improved support vector machine oil price forecast model based on genetic algorithm optimization parameters. AASRI Procedia, 1, 525–530. https://doi.org/10.1016/j.aasri.2012.06.082
Guotai, C., Abedin, M. Z., & Moula, F. E. (2017b). Chinese small business credit scoring: Application of multiple hybrids neural network. International Journal of Database Theory and Application, 10(2), 1–22. https://doi.org/10.14257/ijdta.2017.10.2.01
Guotai, C., Abedin, M. Z., & Moula, F. E. (2017a). Modeling credit approval data with neural networks: An experimental investigation and optimization*. Journal of Business Economics and Management, 18(2), 224–240. https://doi.org/10.3846/16111699.2017.1280844
He, A. W. W., Kwok, J. T. K., & Wan, A. T. K. (2010). An empirical model of daily highs and lows of West Texas Intermediate crude oil prices. Energy Economics, 32(6), 1499–1506. https://doi.org/10.1016/j.eneco.2010.07.012
He, Z., Xiao, L., & Wang, X. (2021). Minimization for ternary fixed polarity Reed-Muller expressions based on ternary quantum shuffled frog leaping algorithm. Applied Soft Computing, 110, 107647. https://doi.org/10.1016/j.asoc.2021.107647
Hu, W., Yang, Q., Chen, H. P., Yuan, Z., Li, C., Shao, S., & Zhang, J. (2021). New hybrid approach for short-term wind speed predictions based on preprocessing algorithm and optimization theory. Renewable Energy, 179, 2174–2186. https://doi.org/10.1016/j.renene.2021.08.044
Jiang, M., Jia, L., Chen, Z., & Chen, W. (2020). The two-stage machine learning ensemble models for stock price prediction by combining mode decomposition, extreme learning machine and improved harmony search algorithm. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03690-w
Jiang, P., Liu, Z., Wang, J., & Zhang, L. (2021). Decomposition-selection-ensemble forecasting system for energy futures price forecasting based on multi-objective version of chaos game optimization algorithm. Resources Policy, 73(July), 102234. https://doi.org/10.1016/j.resourpol.2021.102234
Khalilpourazari, S., & Hashemi Doulabi, H. (2021). Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03871-7
Li, J., Chu, B., Chai, N., Wu, B., Shi, B., & Ou, F. (2021). Work resumption rate and migrant workers’ income during the COVID-19 pandemic. Frontiers in Public Health, 9(May), 1–13. https://doi.org/10.3389/fpubh.2021.678934
Li, J., Zhu, S., & Wu, Q. (2019). Monthly crude oil spot price forecasting using variational mode decomposition. Energy Economics, 83, 240–253. https://doi.org/10.1016/j.eneco.2019.07.009
Li, L. L., Wen, S. Y., Tseng, M. L., & Chiu, A. S. F. (2020). Photovoltaic array prediction on short-term output power method in centralized power generation system. Annals of Operations Research, 290(1–2), 243–263. https://doi.org/10.1007/s10479-018-2879-y
Li, S., Chen, H., Wang, M., Heidari, A. A., & Mirjalili, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems, 111, 300–323. https://doi.org/10.1016/j.future.2020.03.055
Lin, B., & Zhang, C. (2021). A novel hybrid machine learning model for short-term wind speed prediction in inner Mongolia, China. Renewable Energy, 179, 1565–1577. https://doi.org/10.1016/j.renene.2021.07.126
Lv, M., Wang, J., Niu, X., & Lu, H. (2022). A newly combination model based on data denoising strategy and advanced optimization algorithm for short-term wind speed prediction. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-021-03595-x
Medina-Olivares, V., Calabrese, R., Dong, Y., & Shi, B. (2021). Spatial dependence in microfinance credit default. International Journal of Forecasting, (xxxx). https://doi.org/10.1016/j.ijforecast.2021.05.009
Mingming, T., & Jinliang, Z. (2012). A multiple adaptive wavelet recurrent neural network model to analyze crude oil prices. Journal of Economics and Business, 64(4), 275–286. https://doi.org/10.1016/j.jeconbus.2012.03.002
Mirmirani, S., & Cheng Li, H. (2004). A comparison of var and neural networks with genetic algorithm in forecasting price of oil. Advances in Econometrics, 19, 203–223. https://doi.org/10.1016/S0731-9053(04)19008-7
P., D. P. R., V. C., V. R., & T., G. M. (2018). Ant Lion optimization algorithm for optimal sizing of renewable energy resources for loss reduction in distribution systems. Journal of Electrical Systems and Information Technology, 5(3), 663–680. https://doi.org/10.1016/j.jesit.2017.06.001
Premkumar, M., Jangir, P., Sowmya, R., Alhelou, H. H., Heidari, A. A., & Chen, H. (2021). MOSMA: Multi-objective slime mould algorithm based on Elitist non-dominated sorting. IEEE Access, 9(July), 3229–3248. https://doi.org/10.1109/ACCESS.2020.3047936
Qiao, W., Yang, Z., Kang, Z., & Pan, Z. (2020). Short-term natural gas consumption prediction based on Volterra adaptive filter and improved whale optimization algorithm. Engineering Applications of Artificial Intelligence, 87(136), 103323. https://doi.org/10.1016/j.engappai.2019.103323
Qin, X. (2020). Oil shocks and financial systemic stress: International evidence. Energy Economics, 92, 104945. https://doi.org/10.1016/J.ENECO.2020.104945
Sun, X., Hao, J., & Li, J. (2020). Multi ‑ objective optimization of crude oil—supply portfolio based on interval prediction data. Annals of Operations Research, (0123456789). https://doi.org/10.1007/s10479-020-03701-w
Sun, S., Sun, Y., Wang, S., & Wei, Y. (2018). Interval decomposition ensemble approach for crude oil price forecasting. Energy Economics, 76, 274–287. https://doi.org/10.1016/j.eneco.2018.10.015
Trierweiler Ribeiro, G., Alves Portela Santos, A., Cocco Mariani, V., & dos Santos Coelho, L. (2021). Novel hybrid model based on echo state neural network applied to the prediction of stock price return volatility. Expert Systems with Applications, 184(March). https://doi.org/10.1016/j.eswa.2021.115490
Wang, B., & Wang, J. (2020). Energy futures and spots prices forecasting by hybrid SW-GRU with EMD and error evaluation. Energy Economics, 90, 104827. https://doi.org/10.1016/j.eneco.2020.104827
Wang, J., Athanasopoulos, G., Hyndman, R. J., & Wang, S. (2018). Crude oil price forecasting based on internet concern using an extreme learning machine. International Journal of Forecasting, 34(4), 665–677. https://doi.org/10.1016/j.ijforecast.2018.03.009
Wang, J., Du, P., Lu, H., Yang, W., & Niu, T. (2018). An improved grey model optimized by multi-objective ant lion optimization algorithm for annual electricity consumption forecasting. Applied Soft Computing Journal, 72, 321–337. https://doi.org/10.1016/j.asoc.2018.07.022
Wang, J., Du, P., Niu, T., & Yang, W. (2017). A novel hybrid system based on a new proposed algorithm—Multi-Objective Whale Optimization Algorithm for wind speed forecasting. Applied Energy, 208(July), 344–360. https://doi.org/10.1016/j.apenergy.2017.10.031
Wang, J., Niu, T., Du, P., & Yang, W. (2020). Ensemble probabilistic prediction approach for modeling uncertainty in crude oil price. Applied Soft Computing Journal, 95, 106509. https://doi.org/10.1016/j.asoc.2020.106509
Wang, J., Niu, X., Zhang, L., & Lv, M. (2021). Point and interval prediction for non-ferrous metals based on a hybrid prediction framework. Resources Policy, 73(July), 102222. https://doi.org/10.1016/j.resourpol.2021.102222
Wang, J., Wang, S., & Li, Z. (2021b). Wind speed deterministic forecasting and probabilistic interval forecasting approach based on deep learning, modified tunicate swarm algorithm, and quantile regression. Renewable Energy, 179, 1246–1261. https://doi.org/10.1016/j.renene.2021.07.113
Wang, J., Wang, S., Zeng, B., & Lu, H. (2022). A novel ensemble probabilistic forecasting system for uncertainty in wind speed. Applied Energy, 313(January), 118796. https://doi.org/10.1016/j.apenergy.2022.118796
Wang, S., Wang, J., Lu, H., & Zhao, W. (2021c). A novel combined model for wind speed prediction—combination of linear model, shallow neural networks, and deep learning approaches. Energy, 234, 121275. https://doi.org/10.1016/j.energy.2021.121275
Wang, Y., Wang, J., Li, Z., Yang, H., & Li, H. (2021d). Design of a combined system based on two-stage data preprocessing and multi-objective optimization for wind speed prediction. Energy, 231, 121125. https://doi.org/10.1016/j.energy.2021.121125
Wu, C., Wang, J., & Hao, Y. (2022). Deterministic and uncertainty crude oil price forecasting based on outlier detection and modified multi-objective optimization algorithm. Resources Policy, 77(March), 102780. https://doi.org/10.1016/j.resourpol.2022.102780
Yang, L., Chen, G., Rytter, N. G. M., Zhao, J., & Yang, D. (2019). A genetic algorithm-based grey-box model for ship fuel consumption prediction towards sustainable shipping. Annals of Operations Research. https://doi.org/10.1007/s10479-019-03183-5
You, S., Liu, T., Zhang, M., Zhao, X., Dong, Y., Wu, B., et al. (2021). African swine fever outbreaks in China led to gross domestic product and economic losses. Nature Food, 2(10), 802–808. https://doi.org/10.1038/s43016-021-00362-1
Zhang, Q., Di, P., & Farnoosh, A. (2021). Study on the impacts of Shanghai crude oil futures on global oil market and oil industry based on VECM and DAG models. Energy, 223, 120050. https://doi.org/10.1016/j.energy.2021.120050
Zhang, L., Wang, J., & Wang, B. (2020). Energy market prediction with novel long short-term memory network: case study of energy futures index volatility. Energy, 211, 118634. https://doi.org/10.1016/j.energy.2020.118634
Zhang, P., & Ci, B. (2020). Deep belief network for gold price forecasting. Resources Policy, 69(August), 101806. https://doi.org/10.1016/j.resourpol.2020.101806
Zhao, Y., Zhang, W., Gong, X., & Wang, C. (2021). A novel method for online real-time forecasting of crude oil price. Applied Energy, 303(May), 117588. https://doi.org/10.1016/j.apenergy.2021.117588
Acknowledgements
This paper is supported by the National Natural Science Foundation of China (72104046, 71872033), LiaoNing Revitalization Talents Program (XLYC1907012), Liaoning Provincial Cultural Masters and “Four Batches” Talent Training Project (LNSGYP20071), National Statistical Science Research Project (2021LZ24), Research Project of Liaoning Education Department (LN2019Q48), “Double First Class” Scientific Research Key Project in Gansu Province (GSSYLXM-06) and Liaoning BaiQianWan Talents Program (113).
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Weixin Sun, Heli Chen and Yong Wang. The first draft of the manuscript was written by Weixin Sun and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors have no relevant financial or non-financial interests to disclose.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix 1
Rights and permissions
About this article
Cite this article
Sun, W., Chen, H., Liu, F. et al. Point and interval prediction of crude oil futures prices based on chaos theory and multiobjective slime mold algorithm. Ann Oper Res 345, 1003–1033 (2025). https://doi.org/10.1007/s10479-022-04781-6
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10479-022-04781-6