Fossil energy market price prediction by using machine learning with optimal hyper-parameters: A comparative study

Abstract

Fossil energy markets are important commodities, and their price fluctuations impact worldwide economy and financial markets. Hence, it is essential to forecast the prices of fossil energy commodities. In this study, various machine learning systems are optimized by using Bayesian optimization method and implemented to forecast prices in 12 different fossil energy markets including crude oil, natural gas, propane, kerosene, gasoline, heating oil, and coal based on price information from all fossil energy markets. The optimized machine learning systems considered in modeling and simulations are Gaussian regression process, support vector regression, regression trees, k-nearest neighbor algorithm, and deep feedforward neural networks. The simulation results show that Gaussian regression process is the best system to forecast fossil energy markets. In addition, the deep learning feedforward neural networks system yields to stable forecasts. Furthermore, the prediction of the prices in natural gas, coal, and propane markets is a difficult task compared to the prediction of the prices in crude oil markets. The understanding of the effectiveness of machine learning systems in pricing different fossil energy markets can help international economic agents establish appropriate investment strategies.