Natural Gas Price Forecasting by a New Hybrid Model Combining Quadratic Decomposition Technology and LSTM Model

• Published in: Mathematical problems in engineering Vol. 2022; pp. 1 - 13
• Main Authors: Zhan, Linjie, Tang, Zhenpeng
• Format: Journal Article
• Language: English
• Published: New York Hindawi 05.12.2022; Hindawi Limited
• Subjects: Accuracy; Algorithms; Artificial intelligence; Climate change; Decomposition; Econometrics; Empirical analysis; Energy prices; Forecasting; Futures; Gasoline prices; Machine learning; Natural gas; Natural gas prices; Neural networks; Performance prediction; Prediction models; Pricing; Production planning; Research methodology; Stochastic models; Time series
• ISSN: 1024-123X; 1563-5147
• DOI: 10.1155/2022/5488053

Research on the price prediction of natural gas is of great significance to market participants of all kinds. In order to predict natural gas prices more reliably, this paper introduces a quadratic decomposition technology based on the combination of variational modal decomposition (VMD) and ensemble empirical modal decomposition (EEMD), which decomposes the residual term (Res) after VMD by EEMD; then, a new hybrid model called VMD-EEMD-Res.-LSTM is constructed in combination with the long short-term memory (LSTM) prediction model. The contribution of this new hybrid model is that, unlike existing application research that combines existing decomposition technology with the LSTM model, it does not ignore the important information contained in the residual after the VMD. In order to verify the predictive performance of the proposed new model, this paper uses the data of the spot price of natural gas in the United States to conduct a multistep-ahead empirical comparative analysis. The results show that the new hybrid model constructed in this paper has significant predictive advantages.