Forecasting the Urban Air Quality Using Various Statistical Techniques

Abstract

A statistical approach is about working through the historical data and finding guides to future behaviour. In the present study, five statistical techniques i.e. Single Exponential Smoothing (SES), Adaptive Response Rate Single Exponential Smoothing (ARRSES), Holt’s Linear Method (HLM) ARX (Auto Regressive eXogenous) Model and Auto Regressive Integrated Moving Averages (ARIMA) are adopted for predicting the urban air quality over Delhi. Considering the uncertainty and unavailability of most of the inputs of deterministic and advance statistical techniques, the methods adopted here are proposed to have great potential for air quality forecasting.