PreprintReviewVersion 1Preserved in Portico This version is not peer-reviewed
Systematic Literature Review on An Integrated Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA) Model with Heteroscedastic Error and Kriging Method for Forecasting Climate
Version 1
: Received: 22 August 2023 / Approved: 23 August 2023 / Online: 24 August 2023 (03:24:26 CEST)
How to cite:
Monika, P.; Ruchjana, B.N.; Abdullah, A.S.; Budiarto, R. Systematic Literature Review on An Integrated Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA) Model with Heteroscedastic Error and Kriging Method for Forecasting Climate. Preprints2023, 2023081651. https://doi.org/10.20944/preprints202308.1651.v1
Monika, P.; Ruchjana, B.N.; Abdullah, A.S.; Budiarto, R. Systematic Literature Review on An Integrated Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA) Model with Heteroscedastic Error and Kriging Method for Forecasting Climate. Preprints 2023, 2023081651. https://doi.org/10.20944/preprints202308.1651.v1
Monika, P.; Ruchjana, B.N.; Abdullah, A.S.; Budiarto, R. Systematic Literature Review on An Integrated Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA) Model with Heteroscedastic Error and Kriging Method for Forecasting Climate. Preprints2023, 2023081651. https://doi.org/10.20944/preprints202308.1651.v1
APA Style
Monika, P., Ruchjana, B.N., Abdullah, A.S., & Budiarto, R. (2023). Systematic Literature Review on An Integrated Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA) Model with Heteroscedastic Error and Kriging Method for Forecasting Climate. Preprints. https://doi.org/10.20944/preprints202308.1651.v1
Chicago/Turabian Style
Monika, P., Atje Setiawan Abdullah and Rahmat Budiarto. 2023 "Systematic Literature Review on An Integrated Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA) Model with Heteroscedastic Error and Kriging Method for Forecasting Climate" Preprints. https://doi.org/10.20944/preprints202308.1651.v1
Abstract
Rapid climate change requires more powerful and precise modeling methods to forecast future climate variability. The GSTARIMA Model is efficient, combining space-time analysis with the Autoregressive Moving Average (ARIMA) Model. The integration of heteroscedasticity error and the Kriging method can strengthen the Model's ability to handle the problem of non-constant error variance in the GSTARIMA Model and forecast at unobserved locations of climate observations. This paper's Systematics Literature Review (SLR) is presented comprehensively with the principal aim of developing a thorough understanding of applying the GSTARIMA Model with heteroskedasticity error and the Kriging Method in climate forecasting following the Data Analytics Lifecycle methodology. The Systematic Literature Review (SLR) process consists of three main stages. We sourced the articles from databases such as Scopus, Dimensions, and EBSCO-Host The subsequent stage involved conducting a comprehensive literature review using the PRISMA method to ensure rigor and depth. Additionally, we performed bibliometric analysis to enhance rigor. Lastly, we conducted a gap analysis session to scrutinize existing research on the GSTARIMA Model and identify new opportunities. This literature review reveals that integrating GSTARIMA Model with heteroscedasticity errors and the Kriging method is suitable for climate forecasting. This research inspires researchers to contribute to the improvement and refinement of the Model, making it a more potent and valuable tool in climate forecasting.
Keywords
GSTARIMA; heteroscedastics error; Kriging method; climate; data analytics life cycle
Subject
Computer Science and Mathematics, Applied Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
