AQI Prediction Based on CEEMDAN-ARMA-LSTM
Yong Sun, Jiwei Liu
Abstract
In the context of carbon neutrality and air pollution prevention, it is of great research significance to achieve high-accuracy prediction of the air quality index. In this paper, Beijing is used as the study area; data from January 2014 to December 2019 are used as the training set, and data from January 2020 to December 2021 are used as the test set. The CEEMDAN-ARMA-LSTM model constructed in this paper is used for prediction and analysis. The CEEMDAN model is used to decompose the data to improve the data information utilization. The smooth non-white noise components are fed into the ARMA model, and the remaining components and residuals are fed into the LSTM model. The results show that the MAE, MAPE, MSE, and RMSE of this model are the smallest. Compared with the CEEMDAN-LSTM, LSTM, and ARMA-GARCH models, MAE improved by 22.5%, 53.4%, and 21.5%, MAPE improved by 21.4%, 55.3%, and 26.1%, MSE improved by 39.9%, 76.9%, and 28.5%, and RMSE improved by 22.5%, 52.0%, and 15.4%. The accuracy improvement is significant and has good application prospects.