Coke price prediction approach based on dense GRU and opposition-based learning salp swarm algorithm

Coke price prediction is critical for smart coking plants to make sensible production plan. The prediction of coke price fluctuations is a time-series problem, and gated recurrent unit (GRU) performs well on dealing with it. Meanwhile, densely connected GRU can improve the information flow of time-series data, but its key parameters are sensitive. Therefore, a novel coke price prediction method, named DGOLSCPP, is proposed using dense GRU (DGRU) and opposition-based learning salp swarm algorithm (OLSSA). Firstly, a model with two layers stacked DGRU is constructed for capturing deeper features. Secondly, OLSSA is proposed by introducing opposition-based learning, following and stochastic walk operation for enhancing searching ability. Finally, OLSSA is employed to adjust the key parameters of DGRU for winning the accurate predictive results. Experimental results on two real-world coke price datasets from a certain smart coking plant suggest DGOLSCPP outperforms other competitive methods.