Development of time series models for various pollutants in Bangalore city using the Akaike information criterion

Engineering and Applied Science Research

关键词: time series;    air pollution;    akaike information criterion;    arima;    statistics;   

DOI  :  10.14456/easr.2020.28

来源: DOAJ

【 摘 要 】

Pollution levelsindevelopingcountries, suchasIndia, havebecomeamajorsourceofhealthproblems. They needtobe monitored and controlled. Bangalore, one of the major cities in India, faces a huge amount of pollution. Due to the dire need tocontrolthesepollutants, asoundmathematicalmodelingapproachneedstobecreatedforforecasting,controllingand monitoring.Onesuch approach istimeseriesmodeling.Thecurrentwork addresses a timeseriesmodel that hasbeen developed for the major pollutants in Bangalore city. These pollutants include PM10, PM2.5, NOx and SO2. The models used varyfromAR(autoregressive),ARMA(autoregressivemovingaverage)andARIMA(autoregressive integratedmoving average) for modeling air pollution in Bangalore city. Additionally, the selection of the best models was based on the Akaike Information Criterion, p-valueandBox-Piercetest. Various stepswerefollowed tobuildthemodel, whichincluded identification of missing and extreme values followed by creating an appropriate imputing method and then identification of time series models using autocorrelation and partial autocorrelation plots to obtain various time series models. The best time series models were chosen based on the Akaike Information criterion (AIC) and various other statistical tests.

【 授权许可】

Unknown