A Study on Building a Prediction Model of Fine Dust (PM10) And Carbon Monoxide (CO) Using VAR Model

    Jae-Hyun Kim ,Chang-Ho An
    Keywords: Fine dust (10PM), Carbon monoxide (CO), VAR model. Granger causality test, Cross-correlation matrix, Multivariate Portmanteau test ,

    Abstract

    Abstract
    In this study, a prediction model is proposed using the vector autoregression (VAR) model, which is a
    vector time series model. The observation data was obtained from a measurement system installed in
    Jungdaebu Middle School, Dongjak-gu, Seoul. Fine dust (PM10) and carbon monoxide (CO) were used
    as time series variables of the proposed VAR (2) model, and the applicability of the model was checked
    by performing the unit root test and Granger causality test after the first order differencing of the two time
    series variables. The validity of the proposed prediction model was confirmed by performing the
    Schematic Representation of the cross-correlation matrix (CCM) and the multivariate Portmanteau test.
    Prediction by the model resulted that fine dust (PM10) and carbon monoxide (CO) would decrease in
    the short term

    Open chat
    Need help in submission of article?