A novel hybrid-Garch model based on ARIMA and SVM for PM2.5 concentrations forecasting

Air-quality forecasting is difficult because air quality time series are heterogeneous, consisting of one-dimension series data and multi-dimension panel data. Therefore, a hybrid forecasting model with both linear and nonlinear models may be appropriate to represent the complex behavior of a heterogeneous time series data set. In this paper, a new hybrid-Garch (Generalized Autoregressive Conditional Heteroskedasticity) methodology is proposed in order to integrate the individual forecasting models of the ARIMA (Autoregressive Integrated Moving Average) and SVM(Support Vector Machine). The hybrid-Garch approach for time series prediction is tested by 10-day hourly PM2.5 concentrations data including linear and non-linear, in Shenzhen, China. Empirical results from six station data sets indicate that: 1) the PM2.5 concentrations of Shenzhen experiences a regular fluctuation during the 24 h of the whole day with the peak value in working hours due to factory and vehicle emissions. 2) Spatial difference of PM2.5 concentrations is not noticeable because of the geographical and meteorological conditions. 3) The proposed hybrid model generates a more reliable and accurate forecast capability. 4) The proposed hybrid model analyses the time series data with possibly conditional heteroscedasticity characteristics and estimates the variance for the volatility of the PM2.5 concentrations.