Open AccessCoal Price Prediction based on LSTM
Author(s) -
Sitong Pan
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 - 10.1088/1742-6596/1802/4/042055
Subject(s) - coal , recurrent neural network , long short term memory , focus (optics) , government (linguistics) , computer science , artificial neural network , artificial intelligence , sensitivity (control systems) , machine learning , econometrics , economics , engineering , waste management , electronic engineering , linguistics , philosophy , physics , optics
The price of coal has always been the focus of the government and is affected by various factors. This paper uses days, weeks and months as time units to establish a Long-Short Term Memory Recurrent Neural Network Model (LSTM RNN) to predict the change trend of coal prices. The accuracy and sensitivity of the model are tested, and the results show that the model is stable and has high accuracy