Deterministic and uncertainty crude oil price forecasting based on outlier detection and modified multi-objective optimization algorithm

Abstract

Crude oil price forecasting provides reference for stabilizing the energy economic, significantly improving the investment and operation decision-making. However, owing to the complexity and significant fluctuations of crude oil market, it is difficult to achieve accurate and reliable crude oil price forecasting. In this study, we develop a novel hybrid crude oil price forecasting model. Firstly, the Hampel identifier is employed to remove outliers in crude oil price series. Subsequently, complete ensemble empirical mode decomposition is used to reduce noise. Then a novel modified multi-objective water cycle algorithm is proposed to optimize the parameters of echo state network. Finally, deterministic and uncertainty prediction is conducted to verify the model performance. The results reveal that the proposed hybrid model outperforms various contract models in deterministic and interval predictions, as well as in daily and weekly forecasting. Therefore, the proposed hybrid model is a reliable tool for crude oil price forecasting and serve as a reference for decision making in energy economic market.