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All Journal ESTIMASI: Journal of Statistics and Its Application
Andrianingrum, Amalia
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Mathematics

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Perbandingan Model Threshold Generalized utoregressive Conditional Heteroscedasticity dan Exponential Generalized Autoregressive Conditional eteroscedasticity pada Peramalan Curah Hujan Andrianingrum, Amalia; Sahriman, Sitti; Jaya, Andi Kresna
ESTIMASI: Journal of Statistics and Its Application Vol. 6, No. 2, Juli, 2025 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v6i2.43100

Abstract

Rainfall plays an important role in life and is closely related to other weather elements. Rainfall data is used for various purposes, including flood and drought risk mitigation and water resource planning. Makassar City has significant rainfall variability and requires accurate forecasting to manage its negative impacts. This study aims to predict rainfall in Makassar City from January 2021 to May 2023. The methods used are Threshold Generalized Autoregressive Conditional Heteroscedasticity (TGARCH) and Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH). The results showed that the ARMA (2,1)-GARCH (1,2) model had MAPE and RMSEP values ​​of 1.234 and 33.411, respectively. The ARMA (2,1)-TGARCH (2,1) model had MAPE and RMSEP values ​​of 1.330 and 29.357, respectively. The ARMA (2,1)-EGARCH (1,2) model has MAPE and RMSEP values ​​of 0.924 and 32.641, respectively. The smallest MAPE and RMSEP values ​​are in the ARMA (2,1)-EGARCH (1,2) model. Thus, the ARMA (2,1)-EGARCH (1,2) model was selected as the best or optimal model for rainfall forecasting in Makassar City.
Co-Authors Andi Kresna Jaya Sahriman, Sitti