A new hybrid deep learning model for monthly oil prices forecasting

Keqin Guan and Xu Gong

Energy Economics, 2023, vol. 128, issue C

Abstract: The forecast of crude oil prices has always been important for investors and scholars and has drawn more attention to applying deep learning techniques in recent years. Under this circumstance, firstly, this paper proposes a novel hybrid deep learning forecasting model named Mod-VMD-BiLSTM based on the variational mode decomposition (VMD) and bidirectional long short-term memory (BiLSTM) algorithms. Next, several empirical studies and statistical evaluations are conducted to evaluate its forecasting performance. Our empirical results show that the preprocessing of decomposed series is beneficial to capture temporal general feature patterns hidden in sub-series, thereby helping to produce more accurate and robust forecasting results than the competing benchmark models among all scenarios. And all the evaluation metric values can pass the corresponding statistical tests, making the conclusions more convincing and comprehensive. Finally, the robustness tests confirm that the proposed forecasting framework is robust and superior for modeling and forecasting monthly oil prices time series.

Keywords: Long short-term memory; Empirical mode decomposition; Deep learning; Energy finance; Oil price forecasting (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:128:y:2023:i:c:s0140988323006345

DOI: 10.1016/j.eneco.2023.107136

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