Analysis of Price Prediction for Bohai-Rim Steam-Coal Price and Operational Strategies for Coal-Consuming Enterprises—Based on LSTM and Probability Interval Assessment.
In the context of the"dual-carbon"initiative,coal prices are influenced by various nonlinear and non-sta-tionary factors.Accurate and reliable prediction of coal prices becomes increasingly important for enhancing the stability of the coal market.This study utilizes the Long Short-Term Memory(LSTM)neural network as the foundational algorithm to investigate the fluctuation patterns of the Circum-Bohai-Sea thermal coal price index and achieve accurate forecasting of its future trends.The research employs a hybrid kernel density estimation method to explore the uncertainty of future trends in the coal price index.This approach facilitates a probability assessment of the future price fluctuation range distribution for the index,providing comprehensive and high-precision predictive information for decision-makers in enterprises.In comparison with classical BPNN and RNN models,the new model enhances the accuracy of predictions for the Circum-Bohai-Sea thermal coal price index.Simultaneously,it accurately provides probability assessments of future price fluctuations for enter-prises,offering decision support for their operational practices.
Bohai Rim thermal coal price predictionLSTM modelkernel density estimationvolatility pvrediction
NETL
NSTL
万方数据
