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• We propose a novel hybrid model namely MEEMD-Mix-KELM to forecast crude oil price. • The emerging MEEMD, with mode-splitting prevention, is used to decompose oil price. • We create a new mix-kernel for ELM by absorbing merits of local and global kernels. • Empirical results show that the proposed hybrid model achieves significant effects. It is of vital importance for governments, enterprises, and investors to forecast crude oil prices accurately, while this task is beset with difficulties and challenges due to the complex patterns in oil prices. This paper aims to propose a novel hybrid method to model and forecast the crude oil price by integrating median ensemble empirical mode decomposition (MEEMD) and mix-kernel extreme learning machine (Mix-KELM). Firstly, the emerging MEEMD is employed to decompose the crude oil price into several simple subseries. Secondly, a novel mix-kernel is developed for extreme learning machine (ELM) by combining the advantage of the local kernel (i.e., Radial Basis Function in learning ability) and global kernel (i.e., Sigmoid in generalization ability), with weights of the kernels optimized through genetic algorithm. Thirdly, the proposed Mix-KELM is applied to forecast the subseries of crude oil price, and the sub-forecasting results are integrated to generate the final results. The empirical results show that our proposed MEEMD-Mix-KELM model with different forecasting horizons significantly outperforms the benchmarks in terms of forecasting accuracy and robustness test. Taking one-step-ahead forecasting as an example, the proposed model exhibits the lowest prediction errors in terms of mean absolute error, symmetric mean absolute percentage error, and root mean squared error with values of 1.1767, 0.0135, and 1.5717, respectively. [ABSTRACT FROM AUTHOR]

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 R 2 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. [ABSTRACT FROM AUTHOR]