GSTARI-X-ARCH Model with Data Mining Approach for Forecasting Climate in West Java
The spatiotemporal model consists of stationary and non-stationary data, respectively known as the Generalized Space–Time Autoregressive (GSTAR) model and the Generalized Space–Time Autoregressive Integrated (GSTARI) model. The application of this model in forecasting climate with rainfall variables...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Computation |
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| Online Access: | https://www.mdpi.com/2079-3197/10/12/204 |
| _version_ | 1797460847308570624 |
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| author | Putri Monika Budi Nurani Ruchjana Atje Setiawan Abdullah |
| author_facet | Putri Monika Budi Nurani Ruchjana Atje Setiawan Abdullah |
| author_sort | Putri Monika |
| collection | DOAJ |
| description | The spatiotemporal model consists of stationary and non-stationary data, respectively known as the Generalized Space–Time Autoregressive (GSTAR) model and the Generalized Space–Time Autoregressive Integrated (GSTARI) model. The application of this model in forecasting climate with rainfall variables is also influenced by exogenous variables such as humidity, and often the assumption of error is not constant. Therefore, this study aims to design a spatiotemporal model with the addition of exogenous variables and to overcome the non-constant error variance. The proposed model is named GSTARI-X-ARCH. The model is used to predict climate phenomena in West Java, obtained from National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) data. Climate data are big data, so we used knowledge discovery in databases (KDD) in this study. The pre-processing step is collecting and cleaning data. Then, the data mining process with the GSTARI-X-ARCH model follows the Box–Jenkins procedure: model identification, parameter estimation, and diagnostic checking. Finally, the post-processing step for visualization and interpretation of forecast results was conducted. This research is expected to contribute to developing the spatiotemporal model and forecast results as recommendations to the relevant agencies. |
| first_indexed | 2024-03-09T17:10:58Z |
| format | Article |
| id | doaj.art-fc47fda88820449dbe7fff420587eed3 |
| institution | Directory Open Access Journal |
| issn | 2079-3197 |
| language | English |
| last_indexed | 2024-03-09T17:10:58Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj.art-fc47fda88820449dbe7fff420587eed32023-11-24T14:06:57ZengMDPI AGComputation2079-31972022-11-01101220410.3390/computation10120204GSTARI-X-ARCH Model with Data Mining Approach for Forecasting Climate in West JavaPutri Monika0Budi Nurani Ruchjana1Atje Setiawan Abdullah2Master of Mathematics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, IndonesiaDepartment of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, IndonesiaDepartment of Computer Science, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, IndonesiaThe spatiotemporal model consists of stationary and non-stationary data, respectively known as the Generalized Space–Time Autoregressive (GSTAR) model and the Generalized Space–Time Autoregressive Integrated (GSTARI) model. The application of this model in forecasting climate with rainfall variables is also influenced by exogenous variables such as humidity, and often the assumption of error is not constant. Therefore, this study aims to design a spatiotemporal model with the addition of exogenous variables and to overcome the non-constant error variance. The proposed model is named GSTARI-X-ARCH. The model is used to predict climate phenomena in West Java, obtained from National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) data. Climate data are big data, so we used knowledge discovery in databases (KDD) in this study. The pre-processing step is collecting and cleaning data. Then, the data mining process with the GSTARI-X-ARCH model follows the Box–Jenkins procedure: model identification, parameter estimation, and diagnostic checking. Finally, the post-processing step for visualization and interpretation of forecast results was conducted. This research is expected to contribute to developing the spatiotemporal model and forecast results as recommendations to the relevant agencies.https://www.mdpi.com/2079-3197/10/12/204GSTARI-X-ARCHKDDclimateforecasting |
| spellingShingle | Putri Monika Budi Nurani Ruchjana Atje Setiawan Abdullah GSTARI-X-ARCH Model with Data Mining Approach for Forecasting Climate in West Java Computation GSTARI-X-ARCH KDD climate forecasting |
| title | GSTARI-X-ARCH Model with Data Mining Approach for Forecasting Climate in West Java |
| title_full | GSTARI-X-ARCH Model with Data Mining Approach for Forecasting Climate in West Java |
| title_fullStr | GSTARI-X-ARCH Model with Data Mining Approach for Forecasting Climate in West Java |
| title_full_unstemmed | GSTARI-X-ARCH Model with Data Mining Approach for Forecasting Climate in West Java |
| title_short | GSTARI-X-ARCH Model with Data Mining Approach for Forecasting Climate in West Java |
| title_sort | gstari x arch model with data mining approach for forecasting climate in west java |
| topic | GSTARI-X-ARCH KDD climate forecasting |
| url | https://www.mdpi.com/2079-3197/10/12/204 |
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