Open Access

Hybrid Neural Network Architecture for Accurate Forecasting of Crude Oil Prices in Volatile Energy Markets

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Dr. Jack Thompson ¹ , Dr. Mia Johnson ¹ ,

⁴ School of Economics University of Melbourne, Melbourne, Australia

⁴ Department of Data Science University of Sydney, Sydney, Australia

Abstract

Crude oil price forecasting is a complex nonlinear time-series problem influenced by macroeconomic dynamics, geopolitical instability, and speculative market behavior. Traditional statistical models often fail to capture the high volatility and non-stationarity of crude oil price movements. This study proposes a hybrid neural network architecture that integrates deep learning mechanisms with ensemble and feature transformation techniques to improve predictive accuracy in volatile energy markets. The proposed framework leverages Long Short-Term Memory (LSTM) networks, hybridized with gradient-based optimization and ensemble learning strategies, to model long-range dependencies in oil price sequences. Prior studies highlight the limitations of gradient descent-based recurrent architectures in learning long-term dependencies effectively (Bengio & P. F., 1994), motivating the adoption of improved deep learning structures.

The model is conceptually grounded in deep learning frameworks such as TensorFlow (Abadi et al., 2016) and integrates insights from support vector machines, XGBoost, and wavelet-based decomposition approaches to enhance feature representation. Comparative literature indicates that hybrid and ensemble methods outperform standalone models in financial forecasting tasks (Jammazi, 2012; Zhao et al., 2017). The proposed architecture further incorporates risk-sensitive learning considerations inspired by financial volatility studies (Wang et al., 2020).

The study demonstrates that combining sequential learning with ensemble optimization significantly improves forecasting stability under high volatility conditions. The findings contribute to the advancement of intelligent financial forecasting systems and provide a scalable framework for energy market prediction under uncertainty.

Keywords

Crude Oil Forecasting, Hybrid Neural Network, LSTM,, Deep Learning

References

A. a. N. I. N. Khashman, "Intelligent prediction of crude oil price using Support Vector Machines. Applied Machine Intelligence and Informatics (SAMI), 2011 IEEE 9th International Symposium on. IEEE, 2011."

D. T. V. R. K. Y. Jeevan Nagendra Kumar, “GIS-MAP Based Spatial Analysis of Rainfall Data of Andhra Pradesh and Telangana States Using R”, in International Journal of Electrical and Computer Engineering (IJECE), Vol 7, No 1, February 2017.

A. C. Jammazi R, "Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling. Energy Economics, 2012 May."

M. B. P. C. J. C. Z. D. A. D. J. .. &. K. M. Abadi, "Tensorflow: A system for large-scale machine learning. In 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16), pp. 265-283., 2016."

S. e. a. Wang, "Handling financial risks in crude oil imports: Taking into account crude oil prices as well as country and transportation risks. Transportation Research Part E: Logistics and Transportation Review 133, 2020."

A. S. G. C. B. Y. J. N. K. G. K. B Sankara Babu, "Medical Disease Prediction using Grey Wolf optimization and Auto Encoder based Recurrent Neural Network”, Periodicals of Engineering and Natural Sciences, June 2018."

Y. S. P. &. F. P. Bengio, "Learning long-term dependencies with gradient descent is difficult, IEEE Transactions on Neural Networks, pp. 157-166., 1994."

L. A. A. A. A. Gabralla, "Computational modeling of crude oil price forecasting: A review of two decades of research. International Journal of Computer Information Systems and Industrial Management Applications, pp. 729-740, 2013."

T. O. Hodson, "Root-mean-square error (RMSE) or mean absolute error (MAE), https://gmd.copernicus.org/articles/15/5481/2022/."

L. D. Busari GA, "Crude oil price prediction: A comparison between AdaBoost-LSTM and AdaBoost-GRU for improving forecasting performance. Computers & Chemical Engineering. 155:107513, 2021."

G. S. K. P. P. C. L. G. S. G. V. R. Dr. Y. Jeevan Nagendra Kumar, "A Data Science View on Effects of Agriculture & Industry Sector on the GDP of India, International Journal of Recent Technology and Engineering, Vol-8, No.1, May 2019."

Z. S. Ghaffari A, "A novel algorithm for prediction of crude oil price variation based on soft computing. Energy Economics, 2009."

K. M. Gumus M, "Crude oil price forecasting using XGBoost, Computer science and engineering (UBMK)."

S. &. S. J. Hochreiter, "Long short-term memory. Neural, 1997."

L. D. X. S. A. A. G. Gabralla, "Computational modeling of crude oil price forecasting: A review of two decades of research. International Journal of Computer Information Systems and Industrial Management Applications, 2013."

T. A. J. a. R. K. M. R. KRULICKÝ, "Navigating the Dynamics of Brent Crude Oil Prices: Factors, Trends, and Insights. Acta Montanistica Slovaca 29.1, 2024."

S. Y. W. S. W. Y. Sun S, "Interval decomposition ensemble approach for crude oil price forecasting. Energy Economics 76:274, 2018."

Keras Documentation, "https://keras.io./"

S. W. Li, "Text-based crude oil price forecasting: A deep learning approach. International Journal of Forecasting, 2019."

L. X. Z. a. S. W. Yu, "Assessing potentiality of support vector machine method in crude oil price forecasting. EURASIA Journal of Mathematics, Science and Technology Education, 2017."

A. e. a. Mo, "Developing window behavior models for residential buildings using XGBoost algorithm. Energy and Buildings 205, 2019."

Y. J. L. a. L. Y. Zhao, "A deep learning ensemble approach for crude oil price forecasting. Energy Economics, 2017."

H. e. a. Mo, "Developing window behavior models for residential buildings using XGBoost algorithm. Energy and Buildings 205, 2019."

D. e. a. Wen, "Extreme risk spillovers between crude oil prices and the US exchange rate. Energy 212, 2020."

S. e. a. Wang, "Crude oil price forecasting using XGBoost. Computer science and engineering (UBMK)."

Y.-X. Q.-B. W. a. J.-Q. Z. Wu, "Improved EEMD-based crude oil price forecasting using LSTM networks. Physica A: Statistical Mechanics and its Applications 516, pp. 114-124, 2019."

W. e. a. Xie, "A new method for crude oil price forecasting based on support vector machines, Computational Science–ICCS 2006."

T. Vijayakumar, "COMPARATIVE STUDY OF CAPSULE NEURAL NETWORK IN VARIOUS APPLICATIONS. Journal of Artificial Intelligence, Vol 1(01), pp. 19-27, 2019."

https://fred.stlouisfed.org/series/DCOILBRENTEU

A. Bilal et al., "BC-QNet: A quantum-infused ELM model for breast cancer diagnosis. Computers in Biology and Medicine, 2024."

M. Shafiq et al., "Thermal modeling and Machine learning for optimizing heat transfer in smart city infrastructure balancing energy efficiency and Climate Impact. Thermal Science and Engineering Progress, 2024."

M. Shafiq et al., "Efficient nanoscale characterization of wafer surfaces using intelligent sampling and Bayesian optimization. Thermal Science and Engineering Progress, 2024."

M. Shafiq et al., "Multiplexing techniques for future fiber optic communications with spatial multiplexing. Opt Quant Electron, 2024."

M. Shafiq et al., "A novel Skin lesion prediction and classification technique: ViT‐GradCAM. Skin Research and Technology, 2024."

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Hybrid Neural Network Architecture for Accurate Forecasting of Crude Oil Prices in Volatile Energy Markets

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