Generalized Space-Time Autoregressive (GSTAR) for forecasting air pollutant index in Selangor

This study presents the Generalized Space-Time Autoregressive (GSTAR) model, a multivariate time series approach that integrates spatial and temporal observations for data forecasting. This study's primary objective is to develop and apply the GSTAR model to forecast the Air Pollutant Index (AP...

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Main Authors: Nur Maisara Mohamed, Nur Haizum Abd Rahman, Hani Syahida Zulkafli
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2023
Online Access:http://journalarticle.ukm.my/23303/1/Paper12.pdf
http://journalarticle.ukm.my/23303/
http://www.ukm.my/jqma
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Institution: Universiti Kebangsaan Malaysia
Language: English
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spelling my-ukm.journal.233032024-04-03T04:25:43Z http://journalarticle.ukm.my/23303/ Generalized Space-Time Autoregressive (GSTAR) for forecasting air pollutant index in Selangor Nur Maisara Mohamed, Nur Haizum Abd Rahman, Hani Syahida Zulkafli, This study presents the Generalized Space-Time Autoregressive (GSTAR) model, a multivariate time series approach that integrates spatial and temporal observations for data forecasting. This study's primary objective is to develop and apply the GSTAR model to forecast the Air Pollutant Index (API), which exhibits spatial-temporal dependencies between locations and time. Three areas in Selangor have been used in this study: Banting, Petaling, and Shah Alam. The model employs uniform and inverse distance weights to consider spatial relationships. The forecasting performance is assessed using Root Mean Square Error (RMSE). Although both weight methods yield comparable results, the GSTAR model with inverse distance weight is promising for API data forecasting with consistently low RMSE values. The result of this study emphasises the significance of location-based information in generating more efficient and informed solutions. Penerbit Universiti Kebangsaan Malaysia 2023-11 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/23303/1/Paper12.pdf Nur Maisara Mohamed, and Nur Haizum Abd Rahman, and Hani Syahida Zulkafli, (2023) Generalized Space-Time Autoregressive (GSTAR) for forecasting air pollutant index in Selangor. Journal of Quality Measurement and Analysis, 19 (3). pp. 143-153. ISSN 2600-8602 http://www.ukm.my/jqma
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 This study presents the Generalized Space-Time Autoregressive (GSTAR) model, a multivariate time series approach that integrates spatial and temporal observations for data forecasting. This study's primary objective is to develop and apply the GSTAR model to forecast the Air Pollutant Index (API), which exhibits spatial-temporal dependencies between locations and time. Three areas in Selangor have been used in this study: Banting, Petaling, and Shah Alam. The model employs uniform and inverse distance weights to consider spatial relationships. The forecasting performance is assessed using Root Mean Square Error (RMSE). Although both weight methods yield comparable results, the GSTAR model with inverse distance weight is promising for API data forecasting with consistently low RMSE values. The result of this study emphasises the significance of location-based information in generating more efficient and informed solutions.
format Article
author Nur Maisara Mohamed,
Nur Haizum Abd Rahman,
Hani Syahida Zulkafli,
spellingShingle Nur Maisara Mohamed,
Nur Haizum Abd Rahman,
Hani Syahida Zulkafli,
Generalized Space-Time Autoregressive (GSTAR) for forecasting air pollutant index in Selangor
author_facet Nur Maisara Mohamed,
Nur Haizum Abd Rahman,
Hani Syahida Zulkafli,
author_sort Nur Maisara Mohamed,
title Generalized Space-Time Autoregressive (GSTAR) for forecasting air pollutant index in Selangor
title_short Generalized Space-Time Autoregressive (GSTAR) for forecasting air pollutant index in Selangor
title_full Generalized Space-Time Autoregressive (GSTAR) for forecasting air pollutant index in Selangor
title_fullStr Generalized Space-Time Autoregressive (GSTAR) for forecasting air pollutant index in Selangor
title_full_unstemmed Generalized Space-Time Autoregressive (GSTAR) for forecasting air pollutant index in Selangor
title_sort generalized space-time autoregressive (gstar) for forecasting air pollutant index in selangor
publisher Penerbit Universiti Kebangsaan Malaysia
publishDate 2023
url http://journalarticle.ukm.my/23303/1/Paper12.pdf
http://journalarticle.ukm.my/23303/
http://www.ukm.my/jqma
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