A novel hybrid model integrating modified ensemble empirical mode decomposition and LSTM neural network for multi-step precious metal prices prediction. (September 2022)

• Record Type: Journal Article
• Authors: Lin, Yu; Liao, Qidong; Lin, Zixiao; Tan, Bin; Yu, Yuanyuan
• Is Part Of: Resources policy. Volume 78(2022)
• Publication Date: 2022-09
• Subjects: Precious metal prices -- Modified ensemble empirical mode decomposition -- Long short-term memory -- Multi-step ahead forecasting -- Multi-horizon model confidence set -- Business expansion periods and recession periods; C53 -- C61 -- E37 -- Q31; Mines and mineral resources -- Periodicals; Ressources minérales -- Périodiques; Ressources naturelles -- Gestion -- Périodiques; Environnement -- Politique gouvernementale -- Périodiques; 333.8
• DOI: 10.1016/j.resourpol.2022.102884
• Languages: English
• ISSNs: 0301-4207

Abstract

Abstract: Accurately predicting precious metal prices is of extreme significance as they possess an essential position in both financial and industrial fields. To achieve higher prediction accuracy, in this paper, a modified ensemble empirical mode decomposition (MEEMD) method integrated with long short-term memory neural network (LSTM) is used for precious metal price prediction. The multi-scale permutation entropy (MPE) analysis demonstrated that MEEMD has better decomposition effect than ensemble empirical mode decomposition (EEMD). Then input each intrinsic mode function (IMF) obtained by MEEMD into LSTM for prediction. Finally add each IMF forecasting value to get the final prediction results. Compared with traditional multilayer perceptron neural network (MLP), support vector regression (SVR) and a combination forecasting model super learner (SL), MEEMD-LSTM improves the prediction performance in one-step ahead prediction and multi-step ahead prediction. The multi-horizon model confidence set (MCS) test are adopted to comprehensively and statistically testify the best prediction performance of MEEMD-LSTM. Furthermore, this research indicates that the model still shows better prediction accuracy either under different proportions of the training set to the test set or in different periods of business cycle. This competitive precious metal price forecasting model is a promising technique for government agencies, investors and related enterprises. Highlights: This study adopts a novel hybrid MEEMD-LSTM model for multi-step precious metal prices prediction. MEEMD can improve the decomposition effects of the original sequence compared with EEMD according to the MPE analysis. The prediction ability of MEEMD-LSTM significantly surpasses the comparison models at each forecasting horizon by the multi-horizon MCS test. MEEMD-LSTM model still shows robustness either under different proportions of training set to test set or in different business cycle periods.