Crude oil price forecasting: An ensemble‐driven long short‐term memory model based on CEEMDAN decomposition and ALS‐PSO optimization

Abstract

AbstractAs the lifeline of various industries, crude oil is frequently considered a pillar of economic development. The accurate and reliable prediction of crude oil price can provide investors and decision makers with valuable guidance for formulating their strategies. However, the complexity of the crude oil market and the volatility of oil prices pose significant challenges to forecasting crude oil price. To achieve higher prediction accuracy, this paper proposes a new model in which a new modal component reconstruction rule based on sample entropy (SE) is innovatively proposed and includes an improved particle swarm optimization model to avoid getting trapped in a local optimal solution. Two real crude oil price series are selected for experimentation, and the results demonstrate that the proposed model exhibits superior performance in predicting crude oil price. The developed model outperforms the compared models in terms of mean absolute error, root‐mean‐square error, mean absolute percentage error, and coefficient of determination. This result indicates that Complete Ensemble Empirical Mode Decomposition with Adaptive Noise exhibits better decomposition performance. SE can effectively reduce error accumulation, and particle swarm optimization with an adaptive learning strategy (ALS‐PSO) achieves better optimization results for a long short‐term memory network compared with PSO only. Therefore, the proposed model is an effective crude oil price prediction tool that can be applied in practice for investment and policy‐making.