Time Series and Multiple Regression Analysis PM10 with Missing Filling

摘要

PM10 monitoring data showed significant time series characteristics and its accuracy is affected by the monitoring instruments and meteorological factors. 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 PM10. 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 Regression Additive Model based on linear interpolation was the best (0.3433), followed by ARIMA and Multiple Linear Regression Additive Model based on mean filling (0.3810) , and the third was ARIMA and Multiple Regression Additive Model based on mean filling (0.3974). The three models reduced the average relative errors and improved the effects of forecast.