Journal
GREY SYSTEMS-THEORY AND APPLICATION
Volume 11, Issue 1, Pages 80-94Publisher
EMERALD GROUP PUBLISHING LTD
DOI: 10.1108/GS-03-2020-0031
Keywords
Crude oil; Grey prediction; Long short-term memory; GM (1; 1); EMD
Categories
Funding
- National Natural Science Foundation of China [71701105, 71503135]
- Philosophical and Social Science Foundation of Higher Education of Jiangsu Province of China [2016SJB630023]
- Natural Science Foundation of Higher Education of Jiangsu Province of China [16KJB120003]
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This paper proposes a multi-step prediction method combining empirical mode decomposition, long short-term memory network and GM (1,1) model for forecasting crude oil price series. The model shows high accuracy in predicting long-term influences with different parameters for specific components.
Purpose According to the problem of crude oil price forecasting, the purpose of this paper is to propose a multi-step prediction method based on the empirical mode decomposition, long short-term memory network and GM (1,1) model. Design/methodology/approach First, the empirical mode decomposition method is used to decompose the crude oil price series into several components with different frequencies. Then, each subsequence is classified and synthesized based on the specific periodicity and other properties to obtain several components with different significant characteristics. Finally, all components are substituted into a suitable prediction model for fitting. LSTM models with different parameters are constructed for predicting specific components, which approximately and respectively represent short-term market disturbance and long-term influences. Rolling GM (1,1) model is constructed to simulate a series representing the development trend of oil price. Eventually, all results obtained from forecasting models are summarized to evaluate the performance of the model. Findings The model is respectively applied to simulate daily, weekly and monthly WTI crude oil price sequences. The results show that the model has high accuracy on the prediction, especially in terms of series representing long-term influences with lower frequency. GM (1,1) model has excellent performance on fitting the trend of crude oil price. Originality/value This paper combines GM (1,1) model with LSTM network to forecast WTI crude oil price series. According to the different characteristics of different sequences, suitable forecasting models are constructed to simulate the components.
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