Predicting air quality using ARIMA, ARFIMA and HW smoothing

Abstract

Air quality indices (AQIs), used to classify and report the ambient air quality all across the world, computed for pol- lutants SPM, RSPM, NO2 and SO2 for the city Chandigarh using 24-hourly data revealed, RSPM as one of the main responsible air pollutants. Three time series models viz. ARIMA, ARFIMA and Holt and Winters (HW) smoothing techniques are employed to assess and predict the air quality status with respect to responsible pollutant RSPM. Various model selection criteria like Mean Absolute Error, Mean Absolute Percentage Error, Root Mean Square Error, and Bias corrected Akaike's information criterion (AICC) suggested ARFIMA as the appropriate model. Different long memory tests also justify the use of ARFIMA model and its comparison with ARIMA and HW smoothing techniques yield improved estimators/predictors for the responsible pollutant RSPM.