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Strategizing Crude Oil Market Dynamics: Using Deep Learning Predictive Models and the Influence of Parameter Optimization


Strategizing Crude Oil Market Dynamics: Using Deep Learning Predictive Models and the Influence of Parameter Optimization

Kachwaha, Shagun (2024) Strategizing Crude Oil Market Dynamics: Using Deep Learning Predictive Models and the Influence of Parameter Optimization. Masters thesis, Concordia University.

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Crude oil, originating from organic material deposited millions of years ago, serves as the raw material for products like gasoline, diesel, and jet fuel, highlighting its crucial role in modern industry and daily life. Predicting crude oil prices is vital for supply chain managers making operational decisions such as purchasing, production, and transportation. This research aims to predict oil prices to reduce operational costs, increase profit, and enhance competitive advantage. We employed deep learning models to capture the complex, nonlinear characteristics of crude oil price dynamics, utilizing a hyperparameter optimization framework with Bayesian optimization (Optuna) for improved convergence, reduced overfitting, and higher accuracy. In a world shaped by technological breakthroughs, geopolitical intricacies, and economic pressures, precise prediction of oil prices is challenging but essential. Advanced machine learning techniques like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) were utilized. This study used datasets including WTI and BRENT to explore neural network’s ability to understand complex market linkages. Three comparative metrics—RMSE, MAD, and MAPE—ensured result reliability and application across various domains. Forecasts were conducted across three time horizons: daily, weekly, and monthly, each crucial for different stakeholders such as day traders, logistical planners, and strategic decision-makers. Daily forecasts navigate immediate market volatility, weekly forecasts inform logistical and operational adjustments, and monthly forecasts align with long-term strategic planning.
Finally, we compared traditional statistical models with the best deep learning models for both Brent and WTI crude oil using RMSE, MAD, and MAPE to assess the robustness of the deep learning approaches.

Divisions:Concordia University > John Molson School of Business > Supply Chain and Business Technology Management
Item Type:Thesis (Masters)
Authors:Kachwaha, Shagun
Institution:Concordia University
Degree Name:M.S.C.M.
Program:Business Administration (Supply Chain and Business Technology Management specialization)
Date:23 April 2024
Thesis Supervisor(s):Lahmiri, Salim
Keywords:Keywords: Supply chain disruptions, Deep learning models, Forecasting, Bayesian Optimization, Oil Price Uncertainty
ID Code:993927
Deposited By: Shagun Kachwaha
Deposited On:06 Jun 2024 13:28
Last Modified:06 Jun 2024 13:28


Alam, M. S., Murshed, M., Manigandan, P., Pachiyappan, D., & Abduvaxitovna, S. Z. (2023). Forecasting oil, coal, and natural gas prices in the pre-and post-COVID scenarios: Contextual evidence from India using time series forecasting tools. Resources Policy, 81, 103342. https://doi.org/10.1016/j.resourpol.2023.103342
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1), 53. https://doi.org/10.1186/s40537-021-00444-8
Bauquis, P. R. (2001). A Reappraisal of Energy Supply and Demand in 2050. Oil & Gas Science and Technology - Revue d’IFP Energies Nouvelles, 56(4), 389–402. https://doi.org/10.2516/ogst:2001034
Bouteska, A., Hajek, P., Fisher, B., & Abedin, M. Z. (2023). Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network. Research in International Business and Finance, 64, 101863. https://doi.org/10.1016/j.ribaf.2022.101863
Busari, G. A., & Lim, D. H. (2021). Crude oil price prediction: A comparison between AdaBoost-LSTM and AdaBoost-GRU for improving forecasting performance. Computers & Chemical Engineering, 155, 107513. https://doi.org/10.1016/j.compchemeng.2021.107513
Cen, Z., & Wang, J. (2019). Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer. Energy, 169, 160–171. https://doi.org/10.1016/j.energy.2018.12.016
Chen, Y., He, K., & Tso, G. K. F. (2017). Forecasting Crude Oil Prices: A Deep Learning based Model. Procedia Computer Science, 122, 300–307. https://doi.org/10.1016/j.procs.2017.11.373
Cho, K., van Merriënboer, B., Bahdanau, D., & Bengio, Y. (2014). On the Properties of Neural Machine Translation: Encoder–Decoder Approaches. In D. Wu, M. Carpuat, X. Carreras, & E. M. Vecchi (Eds.), Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation (pp. 103–111). Association for Computational Linguistics. https://doi.org/10.3115/v1/W14-4012
de Amorim, L. B. V., Cavalcanti, G. D. C., & Cruz, R. M. O. (2023). The choice of scaling technique matters for classification performance. Applied Soft Computing, 133, 109924. https://doi.org/10.1016/j.asoc.2022.109924
Fang, Y., Wang, W., Wu, P., & Zhao, Y. (2023). A sentiment-enhanced hybrid model for crude oil price forecasting. Expert Systems with Applications, 215, 119329. https://doi.org/10.1016/j.eswa.2022.119329
Gao, C., Yan, J., Zhou, S., Varshney, P. K., & Liu, H. (2019). Long short-term memory-based deep recurrent neural networks for target tracking. Information Sciences, 502, 279–296. https://doi.org/10.1016/j.ins.2019.06.039
Gao, W., Aamir, M., Shabri, A. B., Dewan, R., & Aslam, A. (2019). Forecasting Crude Oil Price Using Kalman Filter Based on the Reconstruction of Modes of Decomposition Ensemble Model. IEEE Access, 7, 149908–149925. https://doi.org/10.1109/ACCESS.2019.2946992
Gharib, C., Mefteh-Wali, S., Serret, V., & Ben Jabeur, S. (2021). Impact of COVID-19 pandemic on crude oil prices: Evidence from Econophysics approach. Resources Policy, 74, 102392. https://doi.org/10.1016/j.resourpol.2021.102392
Gholamy, A., Kreinovich, V., & Kosheleva, O. (2018). Why 70/30 or 80/20 Relation Between Training and Testing Sets: A Pedagogical Explanation. Departmental Technical Reports (CS). https://scholarworks.utep.edu/cs_techrep/1209
Guo, J., Zhao, Z., Sun, J., & Sun, S. (2022). Multi-perspective crude oil price forecasting with a new decomposition-ensemble framework. Resources Policy, 77, 102737. https://doi.org/10.1016/j.resourpol.2022.102737
Guo, L., Huang, X., Li, Y., & Li, H. (2023). Forecasting crude oil futures price using machine learning methods: Evidence from China. Energy Economics, 127, 107089. https://doi.org/10.1016/j.eneco.2023.107089
Gupta, E. (2008). Oil vulnerability index of oil-importing countries. Energy Policy, 36(3), 1195–1211. https://doi.org/10.1016/j.enpol.2007.11.011
He, K., Zha, R., Wu, J., & Lai, K. K. (2016). Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price. Sustainability, 8(4), Article 4. https://doi.org/10.3390/su8040387
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-term Memory. Neural Computation, 9, 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Iqbal, W., Fatima, A., Yumei, H., Abbas, Q., & Iram, R. (2020). Oil supply risk and affecting parameters associated with oil supplementation and disruption. Journal of Cleaner Production, 255, 120187. https://doi.org/10.1016/j.jclepro.2020.120187
Jiang, Y., Kim, H., Asnani, H., Kannan, S., Oh, S., & Viswanath, P. (2020). LEARN Codes: Inventing Low-Latency Codes via Recurrent Neural Networks. IEEE Journal on Selected Areas in Information Theory, 1(1), 207–216. https://doi.org/10.1109/JSAIT.2020.2988577
Karasu, S., & Altan, A. (2022). Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization. Energy, 242, 122964. https://doi.org/10.1016/j.energy.2021.122964
Kurt, B., Gürlek, B., Keskin, S., Özdemir, S., Karadeniz, Ö., Kırkbir, İ. B., Kurt, T., Ünsal, S., Kart, C., Baki, N., & Turhan, K. (2023). Prediction of gestational diabetes using deep learning and Bayesian optimization and traditional machine learning techniques. Medical & Biological Engineering & Computing, 61(7), 1649–1660. https://doi.org/10.1007/s11517-023-02800-7
Li, R., Hu, Y., Heng, J., & Chen, X. (2021). A novel multiscale forecasting model for crude oil price time series. Technological Forecasting and Social Change, 173(C). https://ideas.repec.org//a/eee/tefoso/v173y2021ics0040162521006144.html
Marchese, M., Kyriakou, I., Tamvakis, M., & Di Iorio, F. (2020). Forecasting crude oil and refined products volatilities and correlations: New evidence from fractionally integrated multivariate GARCH models. Energy Economics, 88, 104757. https://doi.org/10.1016/j.eneco.2020.104757
Mohsin, M., & Jamaani, F. (2023). A novel deep-learning technique for forecasting oil price volatility using historical prices of five precious metals in context of green financing – A comparison of deep learning, machine learning, and statistical models. Resources Policy, 86, 104216. https://doi.org/10.1016/j.resourpol.2023.104216
Moshiri, S., & Foroutan, F. (2006). Forecasting Nonlinear Crude Oil Futures Prices. The Energy Journal, 27(4), 81–96. https://doi.org/10.5547/ISSN0195-6574-EJ-Vol27-No4-4
Nagendra Kumar, Y. J., Preetham, P., Kiran Varma, P., Rohith, P., & Dilip Kumar, P. (2020). Crude Oil Price Prediction Using Deep Learning. 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), 118–123. https://doi.org/10.1109/ICIRCA48905.2020.9183258
Niu, T., Wang, J., Lu, H., Yang, W., & Du, P. (2021). A Learning System Integrating Temporal Convolution and Deep Learning for Predictive Modeling of Crude Oil Price. IEEE
Transactions on Industrial Informatics, 17(7), 4602–4612. https://doi.org/10.1109/TII.2020.3016594
Oil vulnerability index of oil-importing countries—ScienceDirect. (n.d.). Retrieved March 23, 2024, from https://www-sciencedirect-com.lib-ezproxy.concordia.ca/science/article/pii/S0301421507005022?via%3Dihub
Ranjit, M. P., Ganapathy, G., Sridhar, K., & Arumugham, V. (2019). Efficient Deep Learning Hyperparameter Tuning Using Cloud Infrastructure: Intelligent Distributed Hyperparameter Tuning with Bayesian Optimization in the Cloud. 2019 IEEE 12th International Conference on Cloud Computing (CLOUD), 520–522. https://doi.org/10.1109/CLOUD.2019.00097
Saxena, S. (2021, March 16). What is LSTM? Introduction to Long Short-Term Memory. Analytics Vidhya. https://www.analyticsvidhya.com/blog/2021/03/introduction-to-long-short-term-memory-lstm/
Sen, A., & Dutta Choudhury, K. (2024). Forecasting the Crude Oil prices for last four decades using deep learning approach. Resources Policy, 88, 104438. https://doi.org/10.1016/j.resourpol.2023.104438
Shenfield, A., & Howarth, M. (2020). A Novel Deep Learning Model for the Detection and Identification of Rolling Element-Bearing Faults. Sensors (Basel, Switzerland), 20. https://doi.org/10.3390/s20185112
Su, M., Liu, H., Yu, C., & Duan, Z. (2022). A New crude oil futures forecasting method based on fusing quadratic forecasting with residual forecasting. Digital Signal Processing, 130, 103691. https://doi.org/10.1016/j.dsp.2022.103691
Wang, J., Zhou, H., Hong, T., Li, X., & Wang, S. (2020). A multi-granularity heterogeneous combination approach to crude oil price forecasting. Energy Economics, 91, 104790. https://doi.org/10.1016/j.eneco.2020.104790
Wu, J., Chen, Y., Zhou, T., & Li, T. (2019). An Adaptive Hybrid Learning Paradigm Integrating CEEMD, ARIMA and SBL for Crude Oil Price Forecasting. Energies, 12(7), Article 7. https://doi.org/10.3390/en12071239
Xu, Z., Mohsin, M., Ullah, K., & Ma, X. (2023). Using econometric and machine learning models to forecast crude oil prices: Insights from economic history. Resources Policy, 83(C). https://ideas.repec.org//a/eee/jrpoli/v83y2023ics0301420723003252.html
Yu, L., Xu, H., & Tang, L. (2017). LSSVR ensemble learning with uncertain parameters for crude oil price forecasting. Applied Soft Computing, 56, 692–701. https://doi.org/10.1016/j.asoc.2016.09.023
Zhang, T., Tang, Z., Wu, J., Du, X., & Chen, K. (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, 229, 120797. https://doi.org/10.1016/j.energy.2021.120797
Zhou, D.-X. (2020). Theory of deep convolutional neural networks: Downsampling. Neural Networks, 124, 319–327. https://doi.org/10.1016/j.neunet.2020.01.018
Aghaabbasi, M., Ali, M., Jasinski, M., Leonowicz, Z., & Novak, T. (2023). On Hyperparameter Optimization of Machine Learning Methods Using a Bayesian Optimization Algorithm to Predict Work Travel Mode Choice. IEEE ACCESS, 11, 19762–19774. https://doi.org/10.1109/ACCESS.2023.3247448
Alali, Y., Harrou, F., & Sun, Y. (2022). A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models. SCIENTIFIC REPORTS, 12(1), 2467. https://doi.org/10.1038/s41598-022-06218-3
Mohammed Abdelkader, E., Zayed, T., Elshaboury, N., & Taiwo, R. (2024). A hybrid Bayesian optimization-based deep learning model for modeling the condition of saltwater pipes in Hong Kong. International Journal of Construction Management, 0(0), 1–17. https://doi.org/10.1080/15623599.2024.2304392
Sani, S., Xia, H., Milisavljevic-Syed, J., & Salonitis, K. (2023). Supply Chain 4.0: A Machine Learning-Based Bayesian-Optimized LightGBM Model for Predicting Supply Chain Risk. Machines, 11(9), Article 9. https://doi.org/10.3390/machines11090888
Snoek, J., Larochelle, H., & Adams, R. P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms (arXiv:1206.2944). arXiv. https://doi.org/10.48550/arXiv.1206.2944
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