ABSTRACT
The variations in the price of crude oil are very erratic, nonlinear, and dynamic with a high degree of uncertainty making it much more difficult to predict accurately. As a result, the opacity and intricacy in determining the crude oil price have been a significant topic of interest for researchers. This paper develops an efficient Genetic Algorithm(GA) based fine-tuned Support Vector Regression(SVR) model for predicting crude oil prices. The strategy utilizes key economic factors that ascertain the price per barrel, which serves as the input. The NASDAQ dataset used in this work encompasses ten years of daily data. The GA technique fine-tunes the parameters of the SVR model to boost the model's ability to foresee crude oil price fluctuations. The proposed model's performance is evaluated by employing various major criteria that compare our model to its counterparts, such as SVR and Long Short-Term Memory (LSTM) approaches. In light of these criteria, the findings of root mean square error (RMSE) and mean absolute percentage error (MAPE) indicate that this model surpasses others in predicting crude oil prices more accurately. Finally, this study also analyzes the impact of persistent uncertainness concerning the COVID-19 outbreak on crude oil price trends. © 2022 IEEE.