Research on artificial intelligence predictions of international crude oil prices based on variational modal decomposition,long short-term memory,and the Elman neural network
Aiming at the complex characteristics of international crude oil prices,which are highly nonlinear,non-stationary,and time varying,this paper proposed a method based on variational modal decomposition(VMD),long short-term memory network(LSTM),and the Elman neural network(ELMAN)to predict international crude oil prices.First,the original crude oil prices were decomposed into subsequences of different frequencies with the VMD method.Then,different models were used to predict high frequency and low frequency sequences.ELMAN was used to predict the last high frequency component,and LSTM was used as the main prediction model to predict other subsequences.Finally,the subsequence prediction values of different models were reconstructed to obtain the final prediction results.The empirical results showed that the VMD-LSTM-ELMAN hybrid model proposed in this paper not only significantly improved the prediction accuracy of international crude oil prices compared with the comparison model,but also maintained strong prediction advantage under different training set lengths and market conditions.It was shown that the model had strong generalization ability and was reliable.Overall,experiments based on international crude oil prices demonstrated that the VMD-LSTM-ELMAN method was an effective and stable forecasting model that provided effective intelligent technical support for governments and businesses.
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