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

Irina V.; Denis A.

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

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Volume 7 • Issue 2 • PP: 19-31 • 2022

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

Irina V. Pustokhin ^1*

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,

Denis A. Pustokhin ²

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

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

* Corresponding Author.

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

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

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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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Pustokhin, Irina V., Pustokhin, Denis A.. "Forecasting crude oil prices based on machine learning statistics methods and random sparse Bayesian learning." American Journal of Business and Operations Research, vol. Volume 7, no. Issue 2, 2022, pp. 19-31. DOI: https://doi.org/10.54216/AJBOR.070202

Pustokhin, I., Pustokhin, D. (2022). Forecasting crude oil prices based on machine learning statistics methods and random sparse Bayesian learning. American Journal of Business and Operations Research, Volume 7(Issue 2), 19-31. DOI: https://doi.org/10.54216/AJBOR.070202

Pustokhin, Irina V., Pustokhin, Denis A.. "Forecasting crude oil prices based on machine learning statistics methods and random sparse Bayesian learning." American Journal of Business and Operations Research Volume 7, no. Issue 2 (2022): 19-31. DOI: https://doi.org/10.54216/AJBOR.070202

Pustokhin, I., Pustokhin, D. (2022) 'Forecasting crude oil prices based on machine learning statistics methods and random sparse Bayesian learning', American Journal of Business and Operations Research, Volume 7(Issue 2), pp. 19-31. DOI: https://doi.org/10.54216/AJBOR.070202

Pustokhin I, Pustokhin D. Forecasting crude oil prices based on machine learning statistics methods and random sparse Bayesian learning. American Journal of Business and Operations Research. 2022;Volume 7(Issue 2):19-31. DOI: https://doi.org/10.54216/AJBOR.070202

I. Pustokhin, D. Pustokhin, "Forecasting crude oil prices based on machine learning statistics methods and random sparse Bayesian learning," American Journal of Business and Operations Research, vol. Volume 7, no. Issue 2, pp. 19-31, 2022. DOI: https://doi.org/10.54216/AJBOR.070202

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