Deep Learning Networks for Simultaneous Multiple Crude Oil Price Predictions

Samson Isaac, Barna Thomas Lass, Tinuke Monsurat Isiaka, Umar Miqdad Bello and Magdaline Peter

Keywords: Crude Oil, Particle Swarm Optimization, Gravitational Search Algorithm, Bidirectional Long Short[1]Term Memory


The conventional linear econometric and statistical models are not effective for forecasting the nonlinear and complex nature of crude oil prices. Computational intelligence techniques and hybrid modelling principles have been proposed to address this issue. Multiple forecasts can be combined using linear or nonlinear methods to create an aggregate forecast. Currently, there is no study on the optimization of deep learning with a hybrid of gravitational search algorithm (GSA) and particle swarm optimization (PSO) for forecasting crude oil price benchmarks, including West Texas Intermediate (WTI), Brent, and Dubai. Additionally, most studies have focused only on using WTI crude oil spot prices as their benchmark. A Bidirectional Long Short-Term Memory (BiLSTM) with hybrid Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) to forecast Crude Oil prices is proposed. The proposed model outperformed FNNPSOGSA, FNNGA, LSTM and BiLSTM models with root mean square errors (RMSE) of 0.0029, 0.0011, and 0.0029, respectively. The proposed model is suited for Crude Oil forecasting.