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Modeling multivariable air pollution data in Malaysia using vector autoregressive model

In this study, the vector autoregressive (VAR) model was used to model and forecast the multivariable air pollution data in Klang area. Stationary test, Hannan–Quinn evaluation criteria, Granger causality test, R2 coefficient and Root Square Mean Error (RMSE) measurements have been conducted to get...

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Main Authors: 'Ulya Abdul Rahim,, Nurulkamal Masseran,
格式: Article
語言:English
出版: Penerbit Universiti Kebangsaan Malaysia 2019
在線閱讀:http://journalarticle.ukm.my/13875/1/jqma-15-2-paper8.pdf
http://journalarticle.ukm.my/13875/
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spelling my-ukm.journal.138752020-01-08T09:26:02Z http://journalarticle.ukm.my/13875/ Modeling multivariable air pollution data in Malaysia using vector autoregressive model 'Ulya Abdul Rahim, Nurulkamal Masseran, In this study, the vector autoregressive (VAR) model was used to model and forecast the multivariable air pollution data in Klang area. Stationary test, Hannan–Quinn evaluation criteria, Granger causality test, R2 coefficient and Root Square Mean Error (RMSE) measurements have been conducted to get the best model and will be used in forecasting. The VAR (7) model is found to be the best model with the highest R2 and lowest RMSE value recorded for each dependent pollutant variable. Based on the fitted VAR (7) model, the VAR model is able to describe the dynamic behavior of multivariable air pollution data of Klang. Forecasts of up to 12 days ahead were constructed with confidence intervals. The VAR model found to provides good forecast accuracy on the data. Penerbit Universiti Kebangsaan Malaysia 2019-12 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/13875/1/jqma-15-2-paper8.pdf 'Ulya Abdul Rahim, and Nurulkamal Masseran, (2019) Modeling multivariable air pollution data in Malaysia using vector autoregressive model. Journal of Quality Measurement and Analysis, 15 (2). pp. 85-93. ISSN 1823-5670 http://www.ukm.my/jqma/current.html
institution Universiti Kebangsaan Malaysia
building Tun Sri Lanang Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Kebangsaan Malaysia
content_source UKM Journal Article Repository
url_provider http://journalarticle.ukm.my/
language English
description In this study, the vector autoregressive (VAR) model was used to model and forecast the multivariable air pollution data in Klang area. Stationary test, Hannan–Quinn evaluation criteria, Granger causality test, R2 coefficient and Root Square Mean Error (RMSE) measurements have been conducted to get the best model and will be used in forecasting. The VAR (7) model is found to be the best model with the highest R2 and lowest RMSE value recorded for each dependent pollutant variable. Based on the fitted VAR (7) model, the VAR model is able to describe the dynamic behavior of multivariable air pollution data of Klang. Forecasts of up to 12 days ahead were constructed with confidence intervals. The VAR model found to provides good forecast accuracy on the data.
format Article
author 'Ulya Abdul Rahim,
Nurulkamal Masseran,
spellingShingle 'Ulya Abdul Rahim,
Nurulkamal Masseran,
Modeling multivariable air pollution data in Malaysia using vector autoregressive model
author_facet 'Ulya Abdul Rahim,
Nurulkamal Masseran,
author_sort 'Ulya Abdul Rahim,
title Modeling multivariable air pollution data in Malaysia using vector autoregressive model
title_short Modeling multivariable air pollution data in Malaysia using vector autoregressive model
title_full Modeling multivariable air pollution data in Malaysia using vector autoregressive model
title_fullStr Modeling multivariable air pollution data in Malaysia using vector autoregressive model
title_full_unstemmed Modeling multivariable air pollution data in Malaysia using vector autoregressive model
title_sort modeling multivariable air pollution data in malaysia using vector autoregressive model
publisher Penerbit Universiti Kebangsaan Malaysia
publishDate 2019
url http://journalarticle.ukm.my/13875/1/jqma-15-2-paper8.pdf
http://journalarticle.ukm.my/13875/
http://www.ukm.my/jqma/current.html
_version_ 1657565432599543808
score 13.250246

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