ARIMA and Multiple Regression Additive Models for PM2.5 Based on Linear Interpolation
Xu Yan, Xu Enhua
Abstract
PM2.5 monitoring data of air pollutants was calibrated and predicted. Due to the lack of monitoring data limited by equipment, the mean and linear interpolation was used to fill in the missing data. ARIMA model (A) was established based on the fluctuation characteristics of PM2.5. The difference between the monitoring value and the standard value was taken as the dependent variable, and five Meteorological factors, namely wind, pressure, precipitation, temperature and humidity, were taken as the independent variables. Multiple regression model (B) was developed. Then, the additive model y = A + B was built. By comparing the average relative error, ARIMA and Multiple Linear Regression Additive Model based on linear interpolation was the best (0.1616), followed by the model with interaction (0.1828), and the third was ARIMA and Multiple Linear Regression Additive Model based on mean filling (0.3080). The three models reduced the average relative errors and improved the effects of forecast.