PM2.5 forecasting with hybrid LSE model-based approach

PM2.5 time series have the features of non-stationary and nonlinear. Existing forecasting methods for PM2.5 cannot achieve high accuracy for they have ignored the potential characteristics of PM2.5 time series. Aiming at this problem, a hybrid approach using local mean decomposition and Support Vector Regression (SVR)-Elman (LSE) is firstly proposed in this paper to analyse 5days ahead PM2.5 concentrations for forecasting in Wuhan, China: (1) the meaningful PF1-PF5 components are extracted from original PM2.5 time series by local mean decomposition; (2) the first high-frequency product function is managed by using the SVR model, such that the relationship between PM2.5 and other air quality data can be revealed accurately; (3) the other components are trained by Elman model with the sliding window method. Experimental results show that, compared with multiple linear regression, autoregressive integrated moving average, BP neural network, and SVR models, the proposed hybrid LSE model-based approach exhibits the best performance in terms of R-2, MAE, MAPE, RMSE, while it is applied for forecasting in real datasets. Copyright (C) 2016 John Wiley & Sons, Ltd.