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American Journal of Business and Operations Research
Volume 7 , Issue 2, PP: 19-31 , 2022 | Cite this article as | XML | Html |PDF


Forecasting crude oil prices based on machine learning statistics methods and random sparse Bayesian learning

Authors Names :   Irina V. Pustokhin   1 *     Denis A. Pustokhin   2  

1  Affiliation :  Department of Entrepreneurship and Logistics, Plekhanov Russian University of Economics, Moscow 117997, Russia

    Email :  Pustohina.IV@rea.ru

2  Affiliation :  Department of Logistics, State University of Management , Moscow 109542, Russia

    Email :  da_pustohin@guu.ru

Doi   :   https://doi.org/10.54216/AJBOR.070202

Received: March 06, 2022 Accepted: August 20, 2022

Abstract :

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.

Keywords :

 Linear Regression; Random Forest; Machine learning; Brent crude Oil; Forecasting.

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Cite this Article as :
Irina V. Pustokhin , Denis A. Pustokhin, Forecasting crude oil prices based on machine learning statistics methods and random sparse Bayesian learning, American Journal of Business and Operations Research, Vol. 7 , No. 2 , (2022) : 19-31 (Doi   :  https://doi.org/10.54216/AJBOR.070202)