In order to further enhance the prediction performance of oil futures price, this study proposes a novel CEEMDAN-PSO-ELM model for oil futures price forecasting based on CEEMDAN decomposition algorithm, extreme learning machine, and particle swarm optimization technology. Firstly, the original oil futures price series is decomposed by CEEMDAN algorithm into several intrinsic mode functions and a residual. Secondly, all the intrinsic mode functions and the residual are reconstructed based on Lempel-Ziv value. Then, the high, medium, and low frequency component are obtained respectively. Thirdly, the extreme learning machine optimized by particle swarm optimization algorithm is employed to predict each component and three component prediction results are obtained. Finally, integrate the prediction results of three components. The empirical research demonstrates that the CEEMDAN-PSO-ELM model proposed in this study has the best prediction performance compared with other 15 benchmark forecasting models. Moreover, the model confidence set and Diebold-Mariano test results further confirm the robustness of the proposed model.