Melola Discovery

← Melola Discovery

Open access Jurnal

A Study of Multivariate Time Series Clustering Based on Vector Autoregressive with Exogenous Models for Forecasting Cooking Oil Prices in Indonesia

Jurnal Matematika, Statistika dan Komputasi · Vol. 23 No. 1 (2026) · pp. 59–75

DOI
10.20956/kxgq2516
ISSN
2614-8811, 1858-1382
Published
2026-09-15
Publisher
Department of Mathematics, Hasanuddin University
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

The fluctuations in cooking oil prices in 77 regencies and cities included in the Strategic Food Price Information Center data show varying levels of volatility over time. This phenomenon presents a challenge for forecasting cooking oil prices in Indonesia in the future. Consequently, a method capable of estimating these prices is required. One such approach involves Multivariate Time Series Clustering (MTSCLUST) method based on the Vector Autoregressive with Exogenous (VARX) model. The MTSClust method is employed to map spatial time series pattern interdependencies across regions, while the VARX model is utilized for its ability to capture the dynamics of intervariable relationships influenced by external factors. This study aims to examine the application of the MTSClust method based on the VARX model in forecasting cooking oil prices in Indonesia in the future. Cluster formation results with K-Means with the Chebyshev distance metric indicate that the 77 regencies and cities in Indonesia can be grouped into three clusters, achieving excellent forecasting accuracy with MAPE values below 10%. Cluster 3 became the best cluster using the VARX (24,1) model, demonstrating optimal performance with a MAPE value of 0.228%. The findings of this study confirm that the MTSClust model with VARX integration is effective for forecasting future cooking oil prices.

Keywords

Collaboration network of the authors

4 authors · 4 co-authorship links

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

  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Isti KamilaAffiliation 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://journal.unhas.ac.id/index.php/jmsk/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">A Study of Multivariate Time Series Clustering Based on Vector Autoregressive with Exogenous Models for Forecasting Cooking Oil Prices in Indonesia</dc:title>
	<dc:creator xml:lang="en">Cahyanindri, Mawar</dc:creator>
	<dc:creator xml:lang="en">Embay, Rohaeti</dc:creator>
	<dc:creator xml:lang="en">Widyastiti, Maya</dc:creator>
	<dc:subject xml:lang="en">VARX</dc:subject>
	<dc:subject xml:lang="en">MTSClust</dc:subject>
	<dc:subject xml:lang="en">Forecasting</dc:subject>
	<dc:subject xml:lang="en">Cooking Oil</dc:subject>
	<dc:subject xml:lang="en">K-Means Chebyshev</dc:subject>
	<dc:subject xml:lang="en">Time Series Analysis</dc:subject>
	<dc:subject xml:lang="en">Statistics</dc:subject>
	<dc:description xml:lang="en">The fluctuations in cooking oil prices in 77 regencies and cities included in the Strategic Food Price Information Center data show varying levels of volatility over time. This phenomenon presents a challenge for forecasting cooking oil prices in Indonesia in the future. Consequently, a method capable of estimating these prices is required. One such approach involves Multivariate Time Series Clustering (MTSCLUST) method based on the Vector Autoregressive with Exogenous (VARX) model. The MTSClust method is employed to map spatial time series pattern interdependencies across regions, while the VARX model is utilized for its ability to capture the dynamics of intervariable relationships influenced by external factors. This study aims to examine the application of the MTSClust method based on the VARX model in forecasting cooking oil prices in Indonesia in the future. Cluster formation results with K-Means with the Chebyshev distance metric indicate that the 77 regencies and cities in Indonesia can be grouped into three clusters, achieving excellent forecasting accuracy with MAPE values below 10%. Cluster 3 became the best cluster using the VARX (24,1) model, demonstrating optimal performance with a MAPE value of 0.228%. The findings of this study confirm that the MTSClust model with VARX integration is effective for forecasting future cooking oil prices.</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2026-09-15</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Text</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unhas.ac.id/index.php/jmsk/article/view/50520</dc:identifier>
	<dc:identifier>10.20956/kxgq2516</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 23 No. 1 (2026): September 2026; 59-75</dc:source>
	<dc:source>2614-8811</dc:source>
	<dc:source>1858-1382</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/jmsk/article/view/50520/15004</dc:relation>
	<dc:coverage xml:lang="en">77 regions in Indonesia that are included in the PIHPS data, 2021-2025</dc:coverage>
	<dc:rights xml:lang="en">Copyright (c) 2026 Jurnal Matematika, Statistika dan Komputasi</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>