Research on the forecast of coal price based on LSTM with improved Adam optimizer

Abstract

Coal is a primary basic energy in China and the changes in coal prices will affect the production of its downstream industries. Forecasting coal prices will benefit companies decision-making on future development. With coal price as the research object, this paper selects 6 factors that affect coal prices, and uses deep learning technology to propose an LSTM neural network model with improved Adam optimizer to forecast it. The experimental results show that the forecast performance of this model is obviously better than that of the traditional model, which not only improves the forecast accuracy, but also provides a reference for the future development and decision-making.