skip to main content

ANALISIS CURAH HUJAN BULANAN DI KOTA AMBON MENGGUNAKAN MODEL HETEROSKEDASTISITAS: SARIMA-GARCH

*Lexy Janzen Sinay orcid scopus  -  Jurusan Matematika, Universitas Pattimura, Indonesia
Ferry Kondo Lembang  -  Jurusan Matematika, Universitas Pattimura, Indonesia
Salmon Notje Aulele  -  Jurusan Matematika, Universitas Pattimura, Indonesia
Dominique Mustamu  -  Jurusan Matematika, Universitas Pattimura, Indonesia
Open Access Copyright (c) 2020 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract
Non-linear characteritics in rainfall allow volatility clustering. This condition occurs in Ambon City with seasonal rainfall patterns. The aims of this research are to find the best model and to forecast monthly rainfall in Ambon City using heteroscedasticity model. This research examines secondary data from BMKG for monthly rainfall data in Ambon City from January 2005 – December 2018. The data is divided into two parts. First part, is called in-sample data, consist of data form January 2005 – December 2017. Second part, is called out-sample data, consist data from Januari 2018 – December 2018. The research used SARIMA–GARCH to model the data. The results are the  is the best model and the residual model satisfied assumptions of normality, white noise, and there is no ARCH effect. The MAPE value in simulation using in-sample data is 0.73%. On the other side, the MAPE value of forecast results is 30%.

Note: This article has supplementary file(s).

Fulltext View|Download |  CTA Form
Copyright Transfer Agreement (CTA)
Subject
Type CTA Form
  Download (252KB)    Indexing metadata
Keywords: Ambon City; Heteroscedasticity; Rainfall; SARIMA-GARCH volatility clustering

Article Metrics:

Last update:

  1. Perceptions and Learning Motivation of Indonesian Students: Multiple Regression Analysis of The Practice of Organizing Film Festivals in Indonesia

    Nala Nandana Undiana, Aceng Abdullah, Herlina Agustin, Teddy Kurnia Wirakusumah. Journal of Law and Sustainable Development, 12 (1), 2024. doi: 10.55908/sdgs.v12i1.2465
  2. Indonesian rupiah exchange rate prediction using a hybrid ARIMA and neural network model

    Clarita Yunet Rumaruson, Lexy Janzen Sinay, Meilin Imelda Tilukay. INTERNATIONAL CONFERENCE ON ENERGY AND ENVIRONMENT (ICEE 2021), 2396 , 2021. doi: 10.1063/5.0059512

Last update: 2024-06-16 20:18:04

No citation recorded.