Background and Theoretical Dilemma: The United States of America (USA) is the world’s largest consumer of crude oil in the world. Ensuring the sustainability of the role of crude oil in the USA makes the need for effective crude oil supply chain logistics to be important. Therefore, this study tested the predictive ability of two machine learning models such as random forest and support vector machine (SVM) in relation to a classical statistical method such as ARIMA (Autoregressive Integrated Moving Average) for predicting the volume of crude oil import into the USA from 2024 to 2034. Method: Crude oil import data used for the prediction were sourced from U.S. Energy Information Administration. The data contained importation data from 1973 to 2023. The performance of the predictive models was tested with mean absolute error (MAE) and Root Mean Square Error (RMSE). Key Findings and Conclusion: Among the three predictive approaches used, SVM had the least MAE (265.65) and RMSE (362.91). This was followed by random forest (MAE =479.37; RMSE = 620.75) while ARIMA had the poorest performance (MAE =1670.10; RMSE = 2195.91). This implies that SVM outperformed the other predictive model for determining the import of crude oil from 2023 to 2034. In addition, among the sources from which crude oil is being imported to USA, Iraq, Canada and Russia have the highest feature importance for the random forest model. This implies that machine learning approach not only help predicts the future supply need for crude oil, but also areas where logistic management should be targeted to.
References
[1]
Bagchi, B., & Paul, B. (2023). Effects of Crude oil Price Shocks on Stock Markets and Currency Exchange Rates in the Context of the Russia-Ukraine conflict: Evidence from G7 Countries. Journal of Risk and Financial Management, 16, Article 64. https://doi.org/10.3390/jrfm16020064
[2]
Bird, R. C. (2018). VUCA and the Legal Environment of Business. University of Connecticut.
[3]
Chai, T., & Draxler, R. R. (2014). Root Mean Square Error (RMSE) or Mean Absolute Error (MAE)—Arguments against Avoiding RMSE in the Literature. Geoscientific Model Development, 7, 1247-1250. https://doi.org/10.5194/gmd-7-1247-2014
[4]
Dai, L., Xu, L., Lim, M. K., Tseng, M. L., & Tan, K. H. (2022). Enhancing Resilience and Agility of Oil Supply Chain in the Context of COVID-19: A Multi-Methodological Study. International Journal of Production Economics, 247, Article ID: 108495. https://doi.org/10.1016/j.ijpe.2022.108495
[5]
Foroutan, P., & Lahmiri, S. (2024). Deep Learning Systems for Forecasting the Prices of Crude Oil and Precious Metals. Financial Innovation, 10, 1-40. https://doi.org/10.1186/s40854-024-00637-z
[6]
Gajewski, P., Čule, B., & Rankovic, N. (2023). Unveiling the Power of ARIMA, Support Vector and Random Forest Regressors for the Future of the Dutch Employment Market. Journal of Theoretical and Applied Electronic Commerce Research, 18, 1365-1403. https://doi.org/10.3390/jtaer18030069
[7]
GeeksforGeeks (2025). Feature Importance with Random Forests. https://www.geeksforgeeks.org/feature-importance-with-random-forests/
[8]
Golgeci, I., Yildiz, H. E., & Andersson, U. (2020). The Rising Tensions between Efficiency and Resilience in Global Value Chains in the Post-Covid-19 World. Transnational Corporations, 27, 127-141. https://doi.org/10.18356/99b1410f-en
[9]
Guido, R., Ferrisi, S., Lofaro, D., & Conforti, D. (2024). An Overview on the Advancements of Support Vector Machine Models in Healthcare Applications: A Review. Information, 15, Article 235. https://doi.org/10.3390/info15040235
[10]
Gunasekaran, A., Lai, K., & Edwincheng, T. (2008). Responsive Supply Chain: A Competitive Strategy in a Networked Economy. Omega, 36, 549-564. https://doi.org/10.1016/j.omega.2006.12.002
[11]
He, H., Sun, M., Li, X., & Mensah, I. A. (2022). A Novel Crude Oil Price Trend Prediction Method: Machine Learning Classification Algorithm Based on Multi-Modal Data Features. Energy, 244, Article 122706. https://doi.org/10.1016/j.energy.2021.122706
[12]
Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts. https://otexts.com/fpp3/
[13]
Jaiswal, J. K., & Samikannu, R. (2017). Application of Random Forest Algorithm on Feature Subset Selection and Classification and Regression. In 2017 World Congress on Computing and Communication Technologies (WCCCT) (pp. 65-68). IEEE. https://doi.org/10.1109/wccct.2016.25
[14]
Jierula, A., Wang, S., Oh, T., & Wang, P. (2021). Study on Accuracy Metrics for Evaluating the Predictions of Damage Locations in Deep Piles Using Artificial Neural Networks with Acoustic Emission Data. Applied Sciences, 11, Article 2314. https://doi.org/10.3390/app11052314
[15]
Jo, J., Kim, U., Lee, E., Lee, J., & Kim, S. (2023). A Supply Chain-Oriented Model to Predict Crude Oil Import Prices in South Korea Based on the Hybrid Approach. Sustainability, 15, Article 16725. https://doi.org/10.3390/su152416725
[16]
Kontopoulou, V. I., Panagopoulos, A. D., Kakkos, I., & Matsopoulos, G. K. (2023). A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks. Future Internet, 15, Article 255. https://doi.org/10.3390/fi15080255
[17]
Kuhn, M., & Johnson, K. (2019). Feature Engineering and Selection: A Practical Approach for Predictive Models. CRC Press.
[18]
Mohapatra, N., Shreya, K., & Chinmay, A. (2020). Optimization of the Random Forest Algorithm. In Advances in Data Science and Management (pp. 201-208). Springer. https://doi.org/10.1007/978-981-15-0077-0_21
[19]
Ning, Y., Kazemi, H., & Tahmasebi, P. (2022). A Comparative Machine Learning Study for Time Series Oil Production Forecasting: ARIMA, LSTM, and Prophet. Computers & Geosciences, 164, Article 105126. https://doi.org/10.1016/j.cageo.2022.105126
[20]
Oladosu, G., Leiby, P., Uria-Martinez, R., & Bowman, D. (2022). Sensitivity of the U.S. Economy to Oil Prices Controlling for Domestic Production and Imports. Energy Economics, 115, Article 106355. https://doi.org/10.1016/j.eneco.2022.106355
[21]
Pandian, S. (2024). K-Fold Cross Validation Technique and its Essentials. Analytics Vidhya. https://www.analyticsvidhya.com/blog/2022/02/k-fold-cross-validation-technique-and-its-essentials/
[22]
Safari, A., & Davallou, M. (2018). Oil Price Forecasting Using a Hybrid Model. Energy, 148, 49-58. https://doi.org/10.1016/j.energy.2018.01.007
[23]
Sayeed, M. A., Rahman, A., Rahman, A., & Rois, R. (2024). On the Interpretability of the SVM Model for Predicting Infant Mortality in Bangladesh. Journal of Health, Population and Nutrition, 43, Article No. 170. https://doi.org/10.1186/s41043-024-00646-9
[24]
Schölkopf, B., & Smola, A. J. (2018). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and beyond. MIT Press.
[25]
Sonkavde, G., Dharrao, D. S., Bongale, A. M., Deokate, S. T., Doreswamy, D., & Bhat, S. K. (2023). Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications. International Journal of Financial Studies, 11, Article 94. https://doi.org/10.3390/ijfs11030094
[26]
Tissaoui, L., El Mhamedi, A., & Benabdelhafid, A. (2022). A Prediction-Based Supply Chain Recovery Strategy under Disruption Risks. International Journal of Production Research, 61, 7670-7684. https://doi.org/10.1080/00207543.2022.2161022
[27]
U.S. Energy Information Administration (EIA) (2023). Oil and Petroleum Products Explained. https://www.eia.gov/energyexplained/oil-and-petroleum-products/imports-and-exports.php
[28]
U.S. Energy Information Administration (EIA) (2024). Short-Term Energy Outlook. https://www.eia.gov/outlooks/steo/
[29]
Wang, P., Zhang, H., Qin, Z., & Zhang, G. (2017). A Novel Hybrid-Garch Model Based on ARIMA and SVM for PM 2.5 Concentrations Forecasting. Atmospheric Pollution Research, 8, 850-860. https://doi.org/10.1016/j.apr.2017.01.003
[30]
Yates, L. A., Aandahl, Z., Richards, S. A., & Brook, B. W. (2023). Cross Validation for Model Selection: A Review with Examples from Ecology. Ecological Monographs, 93, e1557. https://doi.org/10.1002/ecm.1557
[31]
Zhu, H. (2023). Oil Demand Forecasting in Importing and Exporting Countries: AI-Based Analysis of Endogenous and Exogenous Factors. Sustainability, 15, Article 13592. https://doi.org/10.3390/su151813592
