American Journal of Business and Operations Research AJBOR 2692-2967 2770-0216 10.54216/AJBOR https://www.americaspg.com/journals/show/1289 2018 2018 Department of Entrepreneurship and Logistics, Plekhanov Russian University of Economics, Moscow 117997, Russia Irina V. Pustokhin Department of Logistics, State University of Management , Moscow 109542, Russia Denis A. Pustokhin 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. 2022 2022 19 31 10.54216/AJBOR.070202 https://www.americaspg.com/articleinfo/1/show/1289