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Integration of GSTARIMA Model with Heteroskedastic Error and Kriging for Climate Forecasting: A Systematic Review |
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PP: 551-567 |
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doi:10.18576/amis/180307
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Author(s) |
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Putri Monika,
Budi Nurani Ruchjana,
Atje Setiawan Abdullah,
Rahmat Budiarto,
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Abstract |
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| This paper discusses the systematic literature review (SLR) for the integration of the Generalized Space-Time Autoregressive
Integrated Moving Average (GSTARIMA) model with heteroscedastic error and the Kriging method for climate forecasting.
The GSTARIMA model is one of the Spatio-Temporal Models with powerful forecasting capabilities. GSTARIMA model with
Autoregressive Conditional Heteroscedasticity (ARCH) model to overcome the non-constant error variance and Kriging method for
forecasting at unobserved locations. The modelling framework and procedures follow the data analytics life cycle methodology to
handle climate big data. This paper aims to show the gap analysis in the research of the GSTARIMA model for climate modelling. The
SLR method includes three stages: collecting papers from the database, filtering and selection process using the PRISMA method, and
conducting a gap analysis for future work. This research inspires researchers to contribute to improving and refining the model, making
it a more potent and valuable tool in climate forecasting. |
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