Oil price forecasting: A hybrid GRU neural network based on decomposition–reconstruction methods

Significant fluctuations in the price of crude oil in recent years make accurate price estimations of critical importance. A reliable method for crude oil price forecasting is beneficial in guiding production and investment. This study presents two new hybrid predictors for forecasting oil prices based on recurrent neural networks using variational modal decomposition (VMD), sample entropy (SE), and gated recurrent units (GRUs). We consider the West Texas Intermediate daily closing oil prices from August 2, 2010, to December 31, 2019. The hybrid prediction methods first decompose the original time series and then reconstruct the decomposed data using SE. The reconstructed data are used to formulate independent price predictions, which are subsequently combined to produce an ensemble output. With the West Texas Intermediate dataset, the VMD-SE-GRU framework proposed in this paper gives a root mean square error of 0.6735, mean absolute error of 0.4585, mean absolute percentage error of 0.8059, and R2 value of 0.9272. Overall, the hybrid VMD-SE-GRU framework has several advantages over previous models and produces highly accurate forecasts with a shorter runtime. Thus, the new approach provides an effective tool for predicting oil prices. •Two hybrid prediction frameworks based on VMD and GRU are proposed.•A high-performance oil forecasting framework is proposed.•Prediction performance of hybrid deep learning frameworks is compared.•Prediction performance of signal reorganization methods is compared.•Use of sample entropy is justified.