Forecasting of WTI crude oil using combined ANN-Whale optimization algorithm

Abstract

The current study predicts West Texas Intermediate (WTI) petroleum prices using an artificial neural network (ANN) with a whale optimization algorithm (WOA). In implementing the model, five parameters, including gold price, coal price, natural gas price, Dollar-Euro exchange rate, and Dollar-Yuan exchange rate, have been used as input to the combined model. The intelligent and basic ANN algorithm results compared to finding the ANN-WOA algorithm capacity in predicting the future price of WTI oil. ANN-WOA model improved the WTI price predicting accuracy up to 22% compared to the ANN. The ANN-WOA method with a value of R2 = 0.93 compared to the ANN method with a value of R2 = 0.75 was able to reduce the model error well. According to the significant impact that the input parameters of the combination model had on the WTI oil price prediction, therefore, in studies that predict price or other variables, highly correlated variables can significantly increase the accuracy of the forecast.