Daily Temperature Prediction Model Based on DWT-CNN-LSTM
FAN Shuqi
LIU Huiming
ZHUANG Runjie
WANG Shiyu
WEN Yongxian
Abstract：Accurate temperature prediction is crucial for human production and life.To address the challenges of traditional temperature prediction methods,which struggle to capture dynamic data changes and often yield poor accuracy,this paper proposed a combined temperature prediction model that integrates discrete wavelet transform(DWT),convolutional neural network(CNN),and long short-term memory network(LSTM).First,the original temperature observation data were decomposed and reconstructed using the DWT.Second,the CNN was used to perform feature extraction,and the LSTM was applied to process the extracted feature information to achieve temperature prediction.Meanwhile,root mean square error(RMSE),mean absolute error(MAE),and coefficient of determination(R2)were used as the evaluation indexes.Lastly,temperature observation data were used to validate the effectiveness of the proposed model,and a comparative analysis was conducted using the LSTM model,the CNN-LSTM model,and the DWT-LSTM model.Experimental results show that,compared with the LSTM model,the CNN-LSTM model,and the LSTM model based on discrete wavelet transform,the DWT-CNN-LSTM model reduced the RMSE by 1.00924,1.00274,and 0.10023,respectively,and the MAE by 0.91836,0.86265,and 0.14489 respectively.It also improved R2 by 0.04703,0.04662,and 0.00400 respectively.These findings confirm the effectiveness of the model in temperature prediction and provide a new reference for temperature prediction,indicating potential for broader future applications.
Keywords：temperature predictiontime seriesdiscrete wavelet transformconvolutional neural networklong short-term memory network
Publication Date：2024-12-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：11( 1063-1073 )