Open AccessResearch on the forecast of coal price based on LSTM with improved Adam optimizer
Author(s) -
Xinrong Liu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1941/1/012069
Subject(s) - coal , artificial neural network , research object , computer science , deep learning , affect (linguistics) , artificial intelligence , downstream (manufacturing) , object (grammar) , production (economics) , china , operations research , economics , operations management , business , engineering , microeconomics , psychology , waste management , law , business administration , political science , communication
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.