A hybrid model for multi-step coal price forecasting using decomposition technique and deep learning algorithms. (15th January 2022)

• Record Type: Journal Article
• Authors: Zhang, Kefei; Cao, Hua; Thé, Jesse; Yu, Hesheng
• Is Part Of: Applied energy. Volume 306:Part A(2022)
• Publication Date: 2022-01-15
• Subjects: Coal price forecasting -- Variational mode decomposition (VMD) -- Attention mechanism -- LSTM -- SVR; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2021.118011
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Propose a novel hybrid model for multi-step ahead daily coal price forecasting. Decompose original data into multiple modes via variational mode decomposition. Use long short-term memory network containing attention layer to predict each mode. Predicted mode results are ensembled by support vector regression model. Proposed hybrid model is superior to baseline models in coal price forecasting. Abstract: Accurate and reliable coal price prediction is of great significance to enhance the stability of the coal market. Numerous methods have been developed to improve the prediction performance. However, most of the studies adopt single model for coal price forecasting, and their accuracy and applicability are usually restricted. In this paper, we propose a novel hybrid VMD-A-LSTM-SVR model to achieve accurate multi-step ahead prediction of coal price. The proposed model consists of three valuable strategies. First, variational mode decomposition (VMD) decomposes the original coal price into several relatively regular sub modes to reduce the non-stationarity and uncertainty of the data. Second, the long short-term memory (LSTM) integrated with attention mechanism trains and predicts the decomposed modes individually to better capture the temporal information of historical data. Lastly, a support vector regression (SVR) model ensembles the predicted results of each mode into the final forecasted coal price. The experimental results of three typical coal price datasets demonstrate that the proposed strategies are all valuable for improving the forecasting performance. Moreover, the proposed model outperforms all state-of-the-art baseline models in terms of both model accuracy and stability. Extensive cross-comparisons of performance between models clearly indicate that the proposed hybrid algorithm is more effective and practical for coal price forecasting.