Melola Discovery

← Melola Discovery

Open access Journal

Forecasting World Crude Oil Prices using the Fuzzy Time Series Method with a Comparison of the Chen and Lee Model

Journal of Advances in Information Systems and Technology · Vol. 2 No. 1 (2020) · pp. 1–12

DOI
10.15294/jaist.v2i1.44362
ISSN
2715-999X, 2714-9714
Published
2020-04-30
Publisher
Department of Computer Science, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

In this study, the Fuzzy Time Series (FTS) method compared to Chen and Lee models is used to predict world crude oil prices. The goal is to determine which model results are best between Chen and Lee models in the fuzzy time series method in predicting world crude oil prices. In the calculation of FTS number and width specified intervals beginning of the process, the process is very influential to the outcome prediction. The method for determining the number and width of the interval that effectively is by using Rules Sturgess. So that the formation of fuzzy logical relationships will be appropriate and effective yield predictive results. Of the 50 trials that have been done using daily data from the Organization of the Petroleum Exporting Countries (OPEC), it is known that the FTSLee model can predict better than the Chen model with a comparison of the results of the AFER fuzzy time series Lee model by 97.4% and RMSE of 1.617 and the results of the AFER fuzzy time series Chen model by 97.2% and RMSE of 1.693.

Collaboration network of the authors

40 authors · 103 co-authorship links · 7 across different organisations · showing the 40 most connected

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

  • State University of Semarang
  • Tun Hussein Onn University of Malaysia
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Endang SugihartiState University of Semarang5
Florentina Yuni AriniState University of Semarang3
Rochmad RochmadAffiliation unknown3
Amin SuyitnoAffiliation unknown3
Alamsyah AlamsyahState University of Semarang2
Anggyi Trisnawan PutraAffiliation unknown2
Novanka Agnes SekartajiAffiliation unknown2
Irfan Fajar MuttaqinAffiliation unknown2
Ramadhan Ridho ArrohmanAffiliation unknown2
Dwijanto DwijantoAffiliation unknown2
Putriaji HendikawatiAffiliation unknown2
Mashuri MashuriAffiliation unknown2
Budi PrasetiyoState University of Semarang1
Much Aziz MuslimTun Hussein Onn University of Malaysia1
Jumanto JumantoState University of Semarang1
Rofik RofikState University of Semarang1
Arif WidiyatmokoState University of Semarang1
Stephani Diah PamelasariState University of Semarang1
Arka YanitamaState University of Semarang1
Melissa Salma DarmawanState University of Semarang1
Aji SaputraAffiliation unknown1
Desy Fitria AstutianingtyasAffiliation unknown1
Syafa Nabilah SyahputraAffiliation unknown1
Athoillah AthoillahAffiliation unknown1
Kholiq BudimanAffiliation unknown1
zaenal abidinAffiliation unknown1
Anggoro Sakti AnindhitoAffiliation unknown1
Fajar SujitoAffiliation unknown1
Anita Wahyu MelindaAffiliation unknown1
Isa AkhlisState University of Semarang1
Achmad Wahyu HidayatAffiliation unknown1
Safit FirmansyahAffiliation unknown1
Anggil AgustaAffiliation unknown1
Fachrizal Ahdy AndoyoAffiliation unknown1
Margareta Ayu RiantikAffiliation unknown1
Dandi Indra WijayaAffiliation unknown1
Hanif Nur CahyaniAffiliation unknown1
Andhika Seno TamtamaAffiliation 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.unnes.ac.id/sju/index.php/jaist/oai, first harvested 6 Nov 2026, last seen 6 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-US">Forecasting World Crude Oil Prices using the Fuzzy Time Series Method with a Comparison of the Chen and Lee Model</dc:title>
	<dc:creator>Syahputra, Dian Rahman</dc:creator>
	<dc:creator>Arifudin, Riza</dc:creator>
	<dc:description xml:lang="en-US">In this study, the Fuzzy Time Series (FTS) method compared to Chen and Lee models is used to predict world crude oil prices. The goal is to determine which model results are best between Chen and Lee models in the fuzzy time series method in predicting world crude oil prices. In the calculation of FTS number and width specified intervals beginning of the process, the process is very influential to the outcome prediction. The method for determining the number and width of the interval that effectively is by using Rules Sturgess. So that the formation of fuzzy logical relationships will be appropriate and effective yield predictive results. Of the 50 trials that have been done using daily data from the Organization of the Petroleum Exporting Countries (OPEC), it is known that the FTSLee model can predict better than the Chen model with a comparison of the results of the AFER fuzzy time series Lee model by 97.4% and RMSE of 1.617 and the results of the AFER fuzzy time series Chen model by 97.2% and RMSE of 1.693.</dc:description>
	<dc:publisher xml:lang="en-US">Department of Computer Science, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang</dc:publisher>
	<dc:date>2020-04-30</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-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/sju/jaist/article/view/44362</dc:identifier>
	<dc:identifier>10.15294/jaist.v2i1.44362</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Advances in Information Systems and Technology; Vol 2 No 1 (2020): April; 1-12</dc:source>
	<dc:source>2715-999X</dc:source>
	<dc:source>2714-9714</dc:source>
	<dc:source>10.15294/jaist.v2i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/sju/jaist/article/view/44362/18052</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 Journal of Advances in Information Systems and Technology</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>