presenting a model for predicting global crude oil prices based on artificial intelligence algorithm

Predicting the price trend of crude oil and its fluctuations has always been one of the challenges facing traders in oil markets. crude oil is the primary source of energy supply worldwide, serving as a fundamental pillar of the global economy. furthermore, it plays a crucial role in financial markets and the development of the global economy. as a result, this study aims to review and evaluate algorithms, especially machine learning algorithms that are comparable based on real data (such as bayesian networks, artificial neural networks, support vector machines, k-nearest neighbors), for crude oil price forecasting. . for this research, variables such as eu oil prices and us oil prices from 1990 to 2020 have been considered. due to important events such as the outbreak of covid-19, data from 1999 to 2020 have been selected. eu economic growth variables, us economic growth, gold price, eu death rate, us death rate, us oil reserves, us oil production, eu oil production, corona disease, to predict the price of natural gas with west oil price texas global were selected. mae, mse, rmse, r2, rmsle, mape, tt (sec) indicators have been used to check the accuracy and compare each algorithm. python software was used for analysis.the results of this study have shown that, based on the machine learning algorithms used in the first model (brent crude oil price), algorithms like linear regression, least angle regression, orthogonal matching pursuit, and bayesian ridge are the best algorithms for predicting the brent crude oil price. additionally, the results of machine learning algorithms in the second model (global brent oil price and west texas oil price) indicate that linear regression, least angle regression, orthogonal matching pursuit, and bayesian ridge are the best algorithms for predicting west texas oil prices. due to the complexity of the financial and economic markets, no model can predict with absolute accuracy, but using the available information and data, the proposed model can come very close to predicting the price of natural gas.

presenting a model for predicting global crude oil prices based on artificial intelligence algorithm

predicting the price trend of crude oil and its fluctuations has always been one of the challenges facing traders in oil markets. in this regard, in order to make rational predictions about the future, efforts have been made to establish and expand quantitative and qualitative forecasting methods. therefore, forecasting methods have always been important tools for future researchers. consequently, this study aims to investigate and evaluate algorithms, especially machine learning algorithms (such as bayesian networks, artificial neural networks, support vector machines, k-nearest neighbors), for predicting crude oil prices. for this current research, variables such as european union oil prices and u.s. oil prices from 1990 to 2020 are considered. python software was used for analysis.the results of this study have shown that, based on the machine learning algorithms used in the first model (brent crude oil price), algorithms like linear regression, least angle regression, orthogonal matching pursuit, and bayesian ridge are the best algorithms for predicting the brent crude oil price. additionally, the results of machine learning algorithms in the second model (global brent oil price and west texas oil price) indicate that linear regression, least angle regression, orthogonal matching pursuit, and bayesian ridge are the best algorithms for predicting west texas oil prices.