Machine Intelligence Research Labs (MIR Labs) Scientific Network for Innovation and Research Excellence, Washington 98071, USA

Abstract

Crude oil price prediction is a challenging task due to its complex nonlinear and chaotic behavior. There is a great need for oil price volatility measuring and modeling of oil price chaotic behavior. During the last couple of decades, both academicians and practitioners have devoted proactive knowledge to address this issue. Combined predictors are one of the most promising forms in Machine learning (ML). It can be found in different styles in the literature such as Meta learning, Ensemble based prediction, Hybrid methods and more. The aim of this paper to conduct comprehensive comparisons among the combined prediction model in order to improve the performance.