The Price Association of China
Coal Industry Planning Institute Co., Ltd.

深刻理解煤炭价格走势背后的原因并准确预测煤炭价格对促进煤炭产业链平稳运行和提高煤炭产业链上下游企业运营收益具有重要意义,针对煤炭价格预测的问题,提出了基于SSA-LSTM煤炭价格预测模型。在分析影响煤炭供需因素的基础上,将麻雀搜索算法(SSA)和长短期记忆网络模型(LSTM)有效集成,建立了一种基于SSA-LSTM模型的多输入变量的预测模型。利用多输入变量预测模型对环渤海动力煤价格指数进行了预测,结果表明:基于SSA-LSTM煤炭价格预测模型对煤炭价格的预测准确度更高且具有较好的泛化能力。

Deeply understanding the reasons behind the coal price trend and accurately predicting the coal price are of great significance to promote the smooth operation of thecoal industry chain and improve the operating income of the upstream and downstream enterprises in the coal industry chain,and a coal price prediction model based on SSA-LSTM is proposed to address the problem of coal price prediction. In this study, based on analyzing the factors affecting coal supply and demand,a multi-input variable prediction model based on SSA-LSTM model is established by effectively integrating the Sparrow Search Algorithm (SSA) and the Long Short-Term Memory Network Model (LSTM). The multi-input variable prediction model is used to predict the Bohai Rim Power Coal Price Index, and the results show that the coal price prediction model based on the SSA-LSTM model is more accurate and has better generalization ability for coal price prediction.

孟霞, 郭建利, 孙艺. 基于SSA-LSTM模型的煤炭价格预测研究. 煤炭经济研究. 2024, 44(12): 16-22

MENG Xia, GUO Jianli, SUN Yi. Research on coal price prediction based on SSA-LSTM model. Coal Economic Research. 2024, 44(12): 16-22