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Forecasting international crude oil is a well-known issue. The hybrid modeling principle tells us that combining different methods could take full advantage of all the merits and leave out the shortcomings. Therefore, hybrid methodology has been widely used in current research. In this study, a novel decompose-ensemble prediction process combining the ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) is proposed. Moreover, this method, i.e., EEMD-ANN-ADD method, adds the decompose-ensemble to the single AI model to further improve the predicting accuracy. The overall process can be divided into four steps: model selection via Akaike's information criterion (AIC), data decomposition via EEMD, individual prediction via ANN and ensemble prediction through addition ensemble method. To verify the results, we use the official data of oil price to conduct the predicting. The result confirms that “decompose-ensemble” models are better than the normal hybrid one, in terms of prediction accuracy (both level and directional measurement) and modified Diebold-Mariano test. What's more, back to the decompose-ensemble models, the EEMD-based one outperforms the empirical mode decomposition (EMD) one. At last but not the least, AIC gives us reasonable and convincing statement about determining the value of lag. Generally speaking, this novel forecasting technique is a prominent insight for the price of crude oil. [ABSTRACT FROM AUTHOR]

The effective utilization of wind energy is a positive development trend in renewable energy that provides an effective impetus to alleviate global warming. However, the complex volatility and extreme suddenness of wind speeds make it difficult to accurately predict wind speeds. To overcome this challenge, a novel short-term wind speed prediction model based on double decomposition, piecewise error correction, Elman neural network and the autoregressive integrated moving average model is proposed. In this model, the fuzzy entropy is applied to judge the complexity of each subseries generated by variational mode decomposition. To address error correction, a piecewise error correction method that needs to extract only a part of the error sequence as a training set is proposed. Three experiments utilizing 6 Datasets and 14 compared models are conducted to verify the effectiveness of the proposed model. The results show that (i) the hybrid strategy based on double decomposition and piecewise error correction can improve the final prediction accuracy of different single models; (ii) the proposed hybrid model can effectively handle both linear and nonlinear problems; (iii) the root mean squared error of the proposed model for Dataset1-6 in seven-step forecasting are 0.1243, 0.1444, 0.0233, 0.1015, 0.1846 and 0.2513, respectively. • The double decomposition strategy is applied to wind speed prediction. • A novel piecewise error correction method has been developed. • Fuzzy entropy is used to judge the complexity of subsequences. • The proposed model is verified using 6 wind speed datasets in multi-step forecasting. [ABSTRACT FROM AUTHOR]