Research on LSTM Based 24-hour Temperature Prediction Technology in the Future
LEI Ming
CHEN Kaihua
GUO Yang
GOU Zhijing
Abstract：Temperature is an important meteorological element related to life and production,in order to better improve the ac-curacy of temperature prediction.Based on LSTM neural network,this paper proposes a rolling temperature prediction algorithm for the next 24 hours.The experimental research shows that this method is very stable in different forecast periods,and performs well in the future 24-hour forecast.It can effectively reduce the forecast error and improve the forecast accuracy.The mean absolute error(MAE)of the forecast results is 0.634,the root mean square error(RMSE)is 0.638,and the correlation coefficient is 0.999.
Keywords：deep learningLSTMartificial intelligencetemperature predictiondata processing
Publication Date：2025-08-20
Online Publishing Date：2025-12-12(First online date of this platform, not the publication date of the document)
Pages：5( 2112-2116 )