Application research on PM2.5 concentration prediction of multivariate chaotic time series

Abstract

PM2.5 is affected by complex factors such as meteorological elements in the air system and other pollutants in the air. So PM2.5 has chaotic property, which makes the prediction of PM2.5 concentration extremely difficult. In order to improve the prediction accuracy of PM2.5 concentration, this paper introduces the chaotic time series prediction method to establish multivariate time for PM2.5 concentration. The sequence prediction model achieves short-term predictions based on the hour concentration of PM2.5 in Beijing. Firstly, the chaotic time series phase space of the relevant unit is expanded into the multi-time sequence phase space, and the multi-time sequence phase space matrix of PM2.5 concentration is constructed. Then the RBF neural network is used to predict the state point in the multi-phase space system. The phase space points of the PM2.5 concentration sequence is separated for prediction. Finally, the comparison between the prediction model and the traditional prediction model is carried out. The results show that the root mean square error of the predicted PM2.5 concentration in the multivariate chaotic time series prediction model based on the phase space reconstruction is 4.92% in the next 5 hours. The average absolute error is 2.40%, which is more effective than the commonly used statistical prediction method.