2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 7 , Issue 2 , PP: 19-31, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
Irina V. Pustokhin 1 * , Denis A. Pustokhin 2
Doi: https://doi.org/10.54216/AJBOR.070202
Oil price forecasting has received a great deal of interest from both professionals and scholars because of the unique characteristics of the oil price and its enormous impact on a wide range of economic sectors. In response to this problem, the authors set out to develop a strong model for accurately predicting the Brent crude oil price. We employed the Linear Regression and Random Forest models to examine the market interrelationships present in the oil price time series. Next, the models are given weights such that the experimental time series can be accurately predicted. These errors are quantified in terms of root mean squared errors (RMSE), average errors (MAE), and average percentage errors (MAPE). Results and forecast accuracy of the model as compared to the other model. To maximize their output and order levels and reduce the negative impact of potential shocks, countries that produce and import crude oil benefit greatly from accurate crude oil price forecasts.
, Linear Regression , Random Forest , Machine learning , Brent crude Oil , Forecasting.
[1] M. Yang et al., ―Meta-analysis of acupuncture for relieving non-organic dyspeptic symptoms suggestive
of diabetic gastroparesis,‖ BMC complementary and alternative medicine, vol. 13, no. 1, pp. 1–12, 2013.
[2] D. Sun, H. Wen, D. Wang, and J. Xu, ―A random forest model of landslide susceptibility mapping based
on hyperparameter optimization using Bayes algorithm,‖ Geomorphology, vol. 362, p. 107201, 2020.
[3] F. Picciolo, A. Papandreou, K. Hubacek, and F. Ruzzenenti, ―How crude oil prices shape the global
division of labor,‖ Applied Energy, vol. 189, pp. 753–761, 2017.
[4] J.-Y. Wan and C.-W. Kao, ―Interactions between oil and financial markets—Do conditions of financial
stress matter?,‖ Energy Economics, vol. 52, pp. 160–175, 2015.
[5] L.-T. Zhao, Y. Wang, S.-Q. Guo, and G.-R. Zeng, ―A novel method based on numerical fitting for oil
price trend forecasting,‖ Applied Energy, vol. 220, pp. 154–163, 2018.
[6] R. B. Barsky and L. Kilian, ―Oil and the macroeconomy since the 1970s,‖ Journal of Economic
Perspectives, vol. 18, no. 4, pp. 115–134, 2004.
[7] A. Safari and M. Davallou, ―Oil price forecasting using a hybrid model,‖ Energy, vol. 148, pp. 49–58,
2018.
[8] G. Wu and Y.-J. Zhang, ―Does China factor matter? An econometric analysis of international crude oil
prices,‖ Energy Policy, vol. 72, pp. 78–86, 2014.
[9] C. Morana, ―The oil price-macroeconomy relationship since the mid-1980s: A global perspective,‖ The
Energy Journal , vol. 34, no. 3, 2013.
[10] A. Azadeh, M. Moghaddam, M. Khakzad, and V. Ebrahimipour, ―A flexible neural network-fuzzy
mathematical programming algorithm for improvement of oil price estimation and forecasting,‖
Computers & Industrial Engineering, vol. 62, no. 2, pp. 421–430, 2012.
[11] M. Hamdi and C. Aloui, ―Forecasting crude oil price using artificial neural networks: a literature
survey,‖ Econ Bull, vol. 3, no. 2, pp. 1339–1359, 2015.
[12] S. Moshiri and F. Foroutan, ―Forecasting nonlinear crude oil futures prices,‖ The energy journal , vol. 27,
no. 4, 2006.
[13] L. Yu, W. Dai, and L. Tang, ―A novel decomposition ensemble model with extended extreme learning
machine for crude oil price forecasting,‖ Engineering Applications of Artificial Intelligence, vol. 47, pp.
110–121, 2016.
[14] M. Khashei and M. Bijari, ―A new hybrid methodology for nonlinear time series forecasting,‖ Modelling
and Simulation in Engineering, vol. 2011, 2011.
[15] A. Timmermann, ―Forecast combinations,‖ Handbook of economic forecasting, vol. 1, pp. 135–196,
2006.
[16] T. K. Saha, S. Pal, and R. Sarkar, ―Prediction of wetland area and depth using linear regression model
and artificial neural network based cellular automata,‖ Ecological Informatics, vol. 62, p. 101272, 2021.
[17] A. Sultan, W. Sałabun, S. Faizi, and M. Ismail, ―Hesitant Fuzzy linear regression model for decision
making,‖ Symmetry, vol. 13, no. 10, p. 1846, 2021.
[18] F. M. Ottaviani and A. De Marco, ―Multiple Linear Regression Model for Improved Project Cost
Forecasting,‖ Procedia Computer Science, vol. 196, pp. 808–815, 2022.
[19] L. Breiman, ―Bagging predictors,‖ Machine learning, vol. 24, no. 2, pp. 123–140, 1996.
[20] L. Breiman, ―Random forests,‖ Machine learning, vol. 45, no. 1, pp. 5–32, 2001.
