PM2.5 Concentration Prediction Model Based on Complete Ensemble Empirical Mode Decomposition and Deep Learning
This research proposes a novel approach based on Complete Ensemble Empirical Mode Decomposi-tion and the Informer forecasting model to address the issue of low prediction accuracy in existing PM2.5 concentra-tion forecasting models.Historical pollutant data are used as input,which are decomposed into Intrinsic Mode Functions(IMFs)of different frequencies using Complete Ensemble Empirical Mode Decomposition,and the se-quences are reconstructed.These reconstructed sequences are then fed into the Informer model to capture long-term dependencies within the input sequence and model the complex nonlinear relationships among influencing factors,thereby improving prediction accuracy.The results show that the reconstructed model achieves the best performance metrics and significantly improves the prediction accuracy after training,validating,and testing the air pollutant da-ta.
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