PM-25 forecasting use reconstruct phase space LS-SVM

Particulate matter (PM) is a mixture of solid and liquid particles which remains suspended in the air. It affects on human health. Analysis PM in the air is very important based on the monitor measure data. At the same time, it is need to forecast PM. A method is proposed to predict the states of chaos based on the algorithm of LS-SVM (least square support vectors machine) in this study. Our approach is based on reconstruct phase space coming from the Takens embedding theorem. In this approach, the data are divided into two parts; the first part is used to train the model, another part is used as testing. The learning model can be obtained by moving the window, whose width is n, along the axis time. The results show that the method based on LS-SVM, which has better performance, can be used effectively in PM25 prediction by numerical experiments.