A Hybrid Deep Learning Model for Coal Index Forecasting Based on Sentiment Analysis and Decomposition–Reconstruction Methods

ABSTRACT The accurate prediction of the Coal Index is vital due to its substantial impact on economic and environmental policy. This study represents a significant advancement in the field of coal index forecasting by introducing a hybrid deep learning model that effectively tackles the challenge of nonlinear time series data. This innovation overcomes the limitations of traditional statistical and basic machine learning approaches. The core of this model is a unique combination of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), variational mode decomposition (VMD), sentiment analysis from a multicloud platform, and a gated recurrent unit (GRU) with an attention mechanism. This research marks the inaugural application of sentiment analysis in the predictive domain of the coal industry, enhancing predictive accuracy. In this research, the CEEMDAN method is applied to decompose China's coal index data from March 2015 to November 2023, which, in conjunction with the sentiment analysis results, are processed using an attention‐GRU layer to enhance the accuracy and depth of forecasting. Experimental results demonstrate that the proposed model achieves superior performance over several benchmarks in accuracy and error reduction. These results underscore the potential of advanced, integrated analytical techniques in enhancing economic forecasting models.