基于降噪编码与时序分块机制的PM2.5浓度预测模型DAE-PatchTSTOA

To address the issues of low efficiency and insufficient accuracy in multivariate long-sequence forecasting of PM2.5 concentration time series data,a DAE-PatchTST-based PM2.5 concentration prediction model is proposed.The data were collected from seven meteorological observation stations and atmospheric composition stations in different regions of Chongqing.Six sets of hourly historical meteorological and environmental data were selected using Pearson correlation coefficient analysis,and linear interpolation and reversible instance normalization methods were applied for data preprocessing.The constructed model utilizes a denoising autoencoder mechanism to reduce noise in the original input,thereby ensuring model stability.Meanwhile,channel independence and time series patch mechanisms were introduced into the classical Transformer architecture to extensively capture long-term dependencies in the input sequences.Comparative experiments with existing research methods such as RNN,GRU and LSTM demonstrate that the proposed model achieves the best performance in MAE,RMSE and R² metrics for short-and medium-term PM2.5 concentration time series forecasting tasks.Additionally,the model exhibits strong reliability in predicting PM2.5 concentrations in surrounding regions.