Simultaneous projection of world multiple crude oil price benchmarks via hybride of particle swarm optimization-gravitational search algorithm redesigned neural networks

Tóm tắt

     Conventional methods such as econometrical and statistical models are no longer feasible to handle the nonlinear, non-stationary, chaotic, volatile, and complex nature of crude oil prices due to their linearity nature; computational intelligence techniques were proposed to address these issues. More so, single modelling principle and traditional methods have not been effective enough in forecasting of crude oil prices, hybrid modelling principles have been employed to predict crude oil prices by researchers with better improvements. A group researchers opined that the use of hybrid models can also reduce the risk of choosing an inappropriate model because the use of a single model cannot always accurately forecast the extremely complex crude oil price time-series. A combination method can be applied to multiple forecasts and perform linear or nonlinear combinations of the forecasts, leading to an aggregate forecast. However, no research is found to have used the hybrid of gravitational search algorithm (GSA) and particle swarm optimization (PSO) to train artificial neural network (ANN) for simultaneous prediction of crude oil price benchmarks of WTI, Brent and Dubai. More so, most researchers have dwelled on only West Texas Intermediate (WIT) crude oil spot prices as their benchmark.