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COMPARISON OF SEASONAL TIME SERIES FORECASTING USING SARIMA AND HOLT WINTER’S EXPONENTIAL SMOOTHING (CASE STUDY: WEST SUMATRA EXPORT DATA)

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 17 No. 3 (2023) · pp. 1773–1784

DOI
10.30598/barekengvol17iss3pp1773-1784
ISSN
2615-3017, 1978-7227
Published
2023-09-30
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Export is the activity of selling goods or services from one country to another. This activity usually occurs in a specific region or country. Export data is a type of data that has a seasonal pattern. This study aims to compare SARIMA and Holt Winter’s methods in forecasting export data. In this study, the SARIMA model ((1,1,1) (0,1,1))12 and Holt Winter's simulation were obtained. The data used is the export data of West Sumatra from 2016 to 2022. The best model is the one with the smallest MAPE or MAD. The SARIMA model yielded a MAPE of 0,437% and MAD of 78,821. Meanwhile, the Holt Winter's method yielded a MAPE of 0,894% and MAD of 163,320 with α=0,2, β=0,5, γ=0,1. Therefore, the SARIMA outperformed the Holt Winter’s method due to its higher accuracy. It can be concluded that the SARIMA is suitable to use as the forecasting model in this case. In this study, forecast have been made for the next 24 periods, from January 2023 to December 2024.

Collaboration network of the authors

28 authors · 59 co-authorship links

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Halu Oleo
  • University of Vaasa
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Afrimayani AfrimayaniAffiliation unknown3
Fitri Rahmah Ul HasanahAffiliation unknown3
Makmur KambolongUniversitas Halu Oleo1
Candra Puspita NingtyasAffiliation unknown1
Yohanes KristiantoUniversity of Vaasa1
Dwipajati DwipajatiAffiliation unknown1
Muhammad Ahsar KarimAffiliation unknown1
Yuni YulidaAffiliation unknown1
La Ode SabranUniversitas Halu Oleo1
La Ode NasharAffiliation unknown1
Ilham Dangu RianjayaAffiliation unknown1
Rudiyanto RudiyantoAffiliation unknown1
Dodi DeviantoAffiliation unknown1
Ferra YanuarAffiliation unknown1
Maiyastri MaiyastriAffiliation unknown1
Abdillah Khofial LuthfiAffiliation unknown1
Rachel Aura AzzahraAffiliation unknown1
Abd Kadir JaelaniAffiliation unknown1
Ilhamuddin IlhamuddinAffiliation unknown1
Fathrul ArriahAffiliation unknown1
Shanti RiskiyaniAffiliation unknown1
Arsyad RahmanAffiliation unknown1
Baharuddin BaharuddinAffiliation unknown1
Ira TaskirawatiAffiliation unknown1

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 5 Nov 2026.
Original metadata (oai_dc)
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	<dc:title xml:lang="en-US">COMPARISON OF SEASONAL TIME SERIES FORECASTING USING SARIMA AND HOLT WINTER’S EXPONENTIAL SMOOTHING (CASE STUDY: WEST SUMATRA EXPORT DATA)</dc:title>
	<dc:creator>Hasibuan, Lilis Harianti</dc:creator>
	<dc:creator>Musthofa, Syarto</dc:creator>
	<dc:creator>Putri, Darvi Mailisa</dc:creator>
	<dc:creator>Jannah, Miftahul</dc:creator>
	<dc:description xml:lang="en-US">Export is the activity of selling goods or services from one country to another. This activity usually occurs in a specific region or country. Export data is a type of data that has a seasonal pattern. This study aims to compare SARIMA and Holt Winter’s methods in forecasting export data. In this study, the SARIMA model ((1,1,1) (0,1,1))12 and Holt Winter's simulation were obtained. The data used is the export data of West Sumatra from 2016 to 2022. The best model is the one with the smallest MAPE or MAD. The SARIMA model yielded a MAPE of 0,437% and MAD of 78,821. Meanwhile, the Holt Winter's method yielded a MAPE of 0,894% and MAD of 163,320 with α=0,2, β=0,5, γ=0,1. Therefore, the SARIMA outperformed the Holt Winter’s method due to its higher accuracy. It can be concluded that the SARIMA is suitable to use as the forecasting model in this case. In this study, forecast have been made for the next 24 periods, from January 2023 to December 2024.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2023-09-30</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/9090</dc:identifier>
	<dc:identifier>10.30598/barekengvol17iss3pp1773-1784</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications; 1773-1784</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol17iss3year2023</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/9090/6590</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Lilis Harianti Hasibuan, Syarto Musthofa, Darvi Mailisa Putri, Miftahul Jannah</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>