Abstract
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.
Highlights
Crude oil is a natural and nonrenewable resource that has an irreplaceable effect on the development of the global economy and international financial markets
Novel error evaluation methods are proposed to detect the predicted performance. e new analysis method is based on complexity-invariant distance (CID) which generally brings about major improvements in time series classification and clustering accuracy [35]
A new hybrid forecasting model, WPD-stochastic time effective weight (SW)-long short-term memory network (LSTM), has been set up by integrating the wavelet packet decomposition based on LSTM with stochastic time strength weight function method
Summary
Crude oil is a natural and nonrenewable resource that has an irreplaceable effect on the development of the global economy and international financial markets. International crude oil price series are regarded as nonlinear and nonstationary time series. Accurate forecasting of the crude oil price is a challenging task of energy market and has increasingly become an active research field. Numerous methods for time series predictions have been proposed [2,3,4,5,6,7,8,9,10,11,12,13]. E autoregressive integrated moving average model (ARIMA) is a popular statistical model applied to time series prediction. Abdollahi and Ebrahimi [4] established a new composite model to predict Brent crude oil prices by integrating the adaptive neuro fuzzy inference system (ANFIS), autoregressive fractionally integrated moving average (ARFIMA), and Markov-switching models. It is difficult to capture the characters if the datasets are nonstationary
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