Jie Wang, Jun Wang

Jul 13, 2021
Computational Intelligence and Neuroscience
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The crude oil futures prices forecasting is a significant research topic for the management of the energy futures market. In order to optimize the accuracy of energy futures prices prediction, a new hybrid model is established in this paper which combines wavelet packet decomposition (WPD) based on long short term memory network (LSTM) with stochastic time effective weight (SW) function method (WPD SW LSTM). In the proposed framework, WPD is a signal processing method employed to decompose the original series into subseries with different frequencies and the SW LSTM model is constructed based on random theory and the principle of LSTM network. To investigate the prediction performance of the new forecasting approach, SVM, BPNN, LSTM, WPD BPNN, WPD LSTM, CEEMDAN LSTM, VMD LSTM, and ST GRU are considered as comparison models. Moreover, a new error measurement method (multiorder multiscale complexity invariant distance, MMCID) is improved to evaluate the forecasting results from different models, and the numerical results demonstrate that the high accuracy forecast of oil futures prices is realized.
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