Point and Interval Forecasting of Coal Price Adopting a Novel Decomposition Integration Model

Junjie Liu and Lang Liu ()
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Junjie Liu: School of Economics and Management, East China Jiaotong University, Nanchang 330013, China
Lang Liu: School of Business Administration, Guizhou University of Finance and Economics, Guiyang 550031, China

Energies, 2024, vol. 17, issue 16, 1-17

Abstract: Accurate and trustworthy forecasting of coal prices can offer theoretical support for the rational planning of coal industry output, which is of great importance in ensuring a stable and sustainable energy supply and in achieving carbon neutrality targets. This paper proposes a novel decomposition integration model, called VCNQM , to perform point and interval forecasting of coal price by a combination of variational modal decomposition (VMD), chameleon swarm algorithm (CSA), N-BEATS, and quantile regression. Initially, the variational modal decomposition is enhanced by the chameleon swarm algorithm for decomposing the coal price sequence. Then, N-BEATS is used to forecast each subsequence of coal prices, integrating all results to obtain a point forecast of coal prices. Next, interval forecasting of coal prices is achieved through quantile regression. Finally, to demonstrate the superiority of the VCNQM model’s prediction, we make a cross-comparison about predictive performance between the VCNQM model and other benchmark models. According to the experimental findings, we demonstrate the following: after the decomposition by CSA-VMD, the coal price subseries’ fluctuation is significantly weakened; using quantile regression provides a reliable interval prediction, which is superior to point prediction; the predicted interval coverage probability (PICP) is higher than the confidence level of 90%; the share power industry index and coal industry index have the greatest impact on coal prices in China; compared to these benchmark models, the VCNQM model’s prediction errors are all reduced. Therefore, we conclude that when forecasting coal prices, the VCNQM model has an accurate and reliable prediction.

Keywords: point and interval forecasting; variational modal decomposition; N-BEATS; importance analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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