A hybrid model for multi-step coal price forecasting using decomposition technique and deep learning algorithms

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.

Automated summary

Accurate coal price prediction is crucial for market stability. A novel hybrid VMD-A-LSTM-SVR model is proposed, with strategies including VMD decomposition, LSTM training, and SVR ensemble. Experimental results show the model's value in improving forecasting performance.