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PERAMALAN MENGGUNAKAN METODE VECTOR AUTOREGRESSIVE MOVING AVERAGE (VARMA)

JUTI: Jurnal Ilmiah Teknologi Informasi · Vol. 7 No. 2 (2008) · pp. 107–114

DOI
10.12962/j24068535.v7i2.a180
ISSN
2406-8535, 1412-6389
Published
2008-07-01
Publisher
Institut Teknologi Sepuluh Nopember
Language
eng

Abstract

Forecasting technique is an important component of decision making because it aims to predict values of data in the future. Many existing forecasting methods only predict single variable data and thus do not consider correlation between variable in a dataset. This paper proposes Vector Autoregressive Moving Average (VARMA) as a forecasting method to predict data with more than one variable. This method combines regression concept i.e. autoregressive (VAR) and moving average method (VMA) for multi-variables data. The first step in VARMA method is testing the stationary of the data. Differencing process is conducted in order to change non stationary data to stationary. The next step is to identify the order of the VARMA model of the stationary data. The parameters are then estimated according to the order and co-integration test is conducted on the variables. The model is tested to assess its validity. If the model is valid then forecasting can be done using the model generic formula. The errors of the forecast are calculated to evaluate the performance of the model. Random values are found in the forecast of VARMA method. However, the error remains within a certain interval. The error interval is below 10% so it can be argued that this model is very accurate in predicting the data. Keywords: Forecasting, Multi-Variables, VAR, VMA, VARM, Co-integration Test

Collaboration network of the authors

18 authors · 57 co-authorship links

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  • Institut Teknologi Sepuluh Nopember
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Edwin RiksakomaraInstitut Teknologi Sepuluh Nopember2
Handayani TjandrasaAffiliation unknown1
Febriliyan SamopaInstitut Teknologi Sepuluh Nopember1
Radityo Prasetianto WibowoInstitut Teknologi Sepuluh Nopember1
Tresnaning ArifiyahInstitut Teknologi Sepuluh Nopember1
Pujiadi PujiadiAffiliation unknown1
Siti Aminatus ZehrohInstitut Teknologi Sepuluh Nopember1
Nur Aini LestariInstitut Teknologi Sepuluh Nopember1
Yeyen SudiartiAffiliation unknown1
Muhammad Ilham PerdanaAffiliation unknown1
Adri Gabriel SooaiAffiliation unknown1
Retno Aulia VinartiAffiliation unknown1
Ahmad MuklasonInstitut Teknologi Sepuluh Nopember1
Faizal MahanantoAffiliation unknown1
Raras TyasnuritaAffiliation unknown1
Mursyidatun NabilahAffiliation 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://juti.if.its.ac.id/index.php/juti/oai, first harvested 26 Oct 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">PERAMALAN MENGGUNAKAN METODE VECTOR AUTOREGRESSIVE MOVING AVERAGE (VARMA)</dc:title>
	<dc:creator xml:lang="en">Anggraeni, Wiwik</dc:creator>
	<dc:creator xml:lang="en">Dewi, Leivina Kartika</dc:creator>
	<dc:description xml:lang="en">   Forecasting technique is an important component of decision making because it aims to predict values of data in the future. Many existing forecasting methods only predict single variable data and thus do not consider correlation between variable in a dataset. This paper proposes Vector Autoregressive Moving Average (VARMA) as a forecasting method to predict data with more than one variable. This method combines regression concept i.e. autoregressive (VAR) and moving average method (VMA) for multi-variables data. The first step in VARMA method is testing the stationary of the data. Differencing process is conducted in order to change non stationary data to stationary. The next step is to identify the order of the VARMA model of the stationary data. The parameters are then estimated according to the order and co-integration test is conducted on the variables. The model is tested to assess its validity. If the model is valid then forecasting can be done using the model generic formula. The errors of the forecast are calculated to evaluate the performance of the model. Random values are found in the forecast of VARMA method. However, the error remains within a certain interval. The error interval is below 10% so it can be argued that this model is very accurate in predicting the data.   Keywords: Forecasting, Multi-Variables, VAR, VMA, VARM, Co-integration Test</dc:description>
	<dc:publisher xml:lang="en">Institut Teknologi Sepuluh Nopember</dc:publisher>
	<dc:date>2008-07-01</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">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://juti.if.its.ac.id/index.php/juti/article/view/180</dc:identifier>
	<dc:identifier>10.12962/j24068535.v7i2.a180</dc:identifier>
	<dc:source xml:lang="en">JUTI: Jurnal Ilmiah Teknologi Informasi; Vol 7, No 2, Juli 2008; 107-114</dc:source>
	<dc:source>2406-8535</dc:source>
	<dc:source>1412-6389</dc:source>
	<dc:source>10.12962/j24068535.v7i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://juti.if.its.ac.id/index.php/juti/article/view/180/135</dc:relation>
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