 Forecasting the crude oil prices with an EMD-ISBM-FNN modelTianhui Fang, Chunling Zheng and Donghua Wang Energy, 2023, vol. 263, issue PA Abstract: In this paper, an improved slope-based method (ISBM) based on empirical mode decomposition (EMD) and feed-forward neural network (FNN) method, namely, the EMD-ISBM-FNN method is introduced to decompose and forecast the crude oil prices. Firstly, the ISBM-based EMD method is used to decompose the time series of Brent crude oil prices into several IMFs (intrinsic mode functions) and residuals rn(t). Then IMFs and residuals rn(t) are inputted into the FNN model as input layer neurons, which are trained and integrated by the FNN model to study the relationship between the output values of the FNN and actual values. In order to verify the forecasting results of the EMD-ISBM-FNN model, two research frameworks and three strategies are designed, and the EMD-FNN model and the FNN model as the benchmark models are constructed to compare their forecasting results. The research shows that the EMD-ISBM-FNN model proposed in this paper has the best forecasting effect under the three strategies, and the research framework of this paper is better than the previous scholars' research frameworks, too. Keywords: Crude oil prices forecasting; EMD-ISBM-FNN; Forecasting strategy (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:263:y:2023:i:pa:s0360544222022897 DOI: 10.1016/j.energy.2022.125407 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
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