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VECTOR AUTOREGRESSIVE WITH OUTLIER DETECTION ON RAINFALL AND WIND SPEED DATA

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 18 No. 1 (2024) · pp. 0117–0128

DOI
10.30598/barekengvol18iss1pp0117-0128
ISSN
2615-3017, 1978-7227
Published
2024-03-01
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Vector Autoregressive (VAR) is a multivariate time series model that analyzes more than one variable where each variable in the model is endogenous. VAR is one of the models used in forecasting rainfall and wind speed. In observations of rainfall and wind speed, there are usually a series of events whose values are far from other observations or can be said to be outliers. The purpose of this study is to compare the VAR model on rainfall and wind speed data before and after outlier detection. This study uses secondary data, namely monthly data on rainfall and wind speed from 2019 to 2021. From the analysis results, the smallest AIC value obtained in the VAR model before outlier detection was 4.94, then the smallest AIC value in the VAR model after outlier detection was 0.25. Thus, it can be concluded that the best model is obtained in the VAR model after outlier detection seen from the smallest AIC value of the two VAR models.

Collaboration network of the authors

31 authors · 82 co-authorship links

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  • Tanjungpura University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Neva SatyahadewiTanjungpura University18
Ray TamtamaTanjungpura University5
Shantika MarthaTanjungpura University3
Wirda AndaniTanjungpura University3
Siti AprizkiyandariTanjungpura University2
Dinda TiaraTanjungpura University2
Dadan KusnandarTanjungpura University1
Hani Dwi RetnaniTanjungpura University1
Inggriani Millennia TaralyTanjungpura University1
Steven Jansen SinagaTanjungpura University1
Tiffany ArdhithaTanjungpura University1
Indah OktavianiTanjungpura University1
Widad ImtiyazTanjungpura University1
Fritzgerald Muhammad ArsyiTanjungpura University1
Aditya Nugraha PratamaTanjungpura University1
Wiji UmiatiTanjungpura University1
Ainun ZakiahTanjungpura University1
Noerul HaninTanjungpura University1
Della ZariaTanjungpura University1
Muhammad Ilham FauzanTanjungpura University1
Nabila Izza HarnantaTanjungpura University1
Hendri AgustonoTanjungpura University1
Rifki PebriyandiTanjungpura University1
Risky GunawanTanjungpura University1
Cinta Priscillia MaharaniTanjungpura University1
David Jordy DhandioTanjungpura University1
Nurhanifa NurhanifaTanjungpura University1
Sania PujiantiTanjungpura University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">VECTOR AUTOREGRESSIVE WITH OUTLIER DETECTION ON RAINFALL AND WIND SPEED DATA</dc:title>
	<dc:creator>Lestari, Lisa</dc:creator>
	<dc:creator>Sulistianingsih, Evy</dc:creator>
	<dc:creator>Perdana, Hendra</dc:creator>
	<dc:description xml:lang="en-US">Vector Autoregressive (VAR) is a multivariate time series model that analyzes more than one variable where each variable in the model is endogenous. VAR is one of the models used in forecasting rainfall and wind speed. In observations of rainfall and wind speed, there are usually a series of events whose values are far from other observations or can be said to be outliers. The purpose of this study is to compare the VAR model on rainfall and wind speed data before and after outlier detection. This study uses secondary data, namely monthly data on rainfall and wind speed from 2019 to 2021. From the analysis results, the smallest AIC value obtained in the VAR model before outlier detection was 4.94, then the smallest AIC value in the VAR model after outlier detection was 0.25. Thus, it can be concluded that the best model is obtained in the VAR model after outlier detection seen from the smallest AIC value of the two VAR models.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2024-03-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/10010</dc:identifier>
	<dc:identifier>10.30598/barekengvol18iss1pp0117-0128</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application; 0117-0128</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol18iss1year2024</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/10010/7555</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Lisa Lestari, Evy Sulistianingsih, Hendra Perdana</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>