Energy futures and spots prices forecasting by hybrid SW-GRU with EMD and error evaluation

Abstract

Abstract Forecasting energy market volatility by artificial neural network has long been a focus of economic research. Based on the discriminatory attitude to the historical price information, a novel hybrid forecasting model of gated recurrent unit with stochastic time effective weights (SW-GRU) is proposed and applied to global energy prices forecasting with empirical mode decomposition (EMD). Since crude oil and gasoline count much in global energy markets, the futures and spots prices of them are adopted in the present research. After real energy price series is decomposed into intrinsic mode functions (IMFs) and a residual, the forecasting IMFs and residual in the test set can be performed by SW-GRU and utilized to calculate the prediction prices. With several error criteria and double-scale complexity invariant distance, the forecasting errors of proposed model SW-GRU with EMD and other models are evaluated and compared. According to the empirical study in energy markets, the forecasting model of SW-GRU with EMD is distinguished from other models by its best performances.