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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Penerbit Universiti Kebangsaan Malaysia
2023
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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 |
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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. |
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Nur Maisara Mohamed, Nur Haizum Abd Rahman, Hani Syahida Zulkafli, |
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Nur Maisara Mohamed, Nur Haizum Abd Rahman, Hani Syahida Zulkafli, Generalized Space-Time Autoregressive (GSTAR) for forecasting air pollutant index in Selangor |
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Nur Maisara Mohamed, Nur Haizum Abd Rahman, Hani Syahida Zulkafli, |
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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 |
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Penerbit Universiti Kebangsaan Malaysia |
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2023 |
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http://journalarticle.ukm.my/23303/1/Paper12.pdf http://journalarticle.ukm.my/23303/ http://www.ukm.my/jqma |
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