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STOCK PRICE FORECASTING USING FUZZY C-MEANS AND TYPE-2 FUZZY TIME SERIES

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 19 No. 2 (2025) · pp. 1365–1378

DOI
10.30598/barekengvol19iss2pp1365-1378
ISSN
2615-3017, 1978-7227
Published
2025-04-01
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Stock prices have unstable movements, so forecasting is needed to decide to invest appropriately according to the strategy. Fuzzy Time Series (FTS) uses fuzzy sets to forecast future time series values using historical data. However, interval partitioning in FTS needs to be considered as it can affect the forecasting results. FCM is applied to solve the problem of interval assignment in the universe of discourse. It allows the evaluation of the distribution of historical data and forming intervals of different sizes. Type 2 Fuzzy Time Series (T2FTS) is an extension of FTS to improve forecasting performance and refine fuzzy relationships. This research aims to improve forecasting accuracy using the Fuzzy C-Means (FCM)-T2FTS combination. This research uses daily data on BBRI stock prices from January 2023 to May 2024, with the variables used being close, high, and low prices. The results showed that determining the interval length using unequal length is more efficient than fixed interval length and can improve model performance, demonstrated from the MAPE values of T2FTS and FCM-T2FTS, which are 2.09% and 1.97%, respectively, the difference between the two MAPEs, is 0.12%. Hence, FCM-T2FTS is 12% more efficient than T2FTS. Therefore, FCM-T2FTS can improve forecasting accuracy.

Collaboration network of the authors

24 authors · 43 co-authorship links · 7 across different organisations

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

  • Diponegoro University
  • Edith Cowan University
  • Universitas Halu Oleo
  • Satya Wacana Christian University
  • University of Indonesia
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Oky Dwi NurhayatiDiponegoro University3
Budi WarsitoDiponegoro University2
Dinar Mutiara Kusumo NugraheniDiponegoro University2
Retno KusumaningrumDiponegoro University2
Wahyul Amien SyafeiDiponegoro University2
Nuzulul Khairu NissaUniversity of Indonesia2
Fitriana MasrurohDiponegoro University1
Willy Bima AlfajriDiponegoro University1
Nitami Lestari PutriDiponegoro University1
Bambang SubenoDiponegoro University1
Ferry JieEdith Cowan University1
Irza Nur FauziDiponegoro University1
Eko SediyonoSatya Wacana Christian University1
Subhan SubhanUniversitas Halu Oleo1
Desty DwiyanasariAffiliation unknown1
Dinar NugraheniAffiliation unknown1
Nurhita RahmawatiAffiliation unknown1
Arif Nur FaudinAffiliation unknown1
Hardi SuyitnoAffiliation unknown1
Fitri KensiwiAffiliation unknown1
Selvi Fitria KhoerunnisaAffiliation 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 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">STOCK PRICE FORECASTING USING FUZZY C-MEANS AND TYPE-2 FUZZY TIME SERIES</dc:title>
	<dc:creator>Satriani, Rineka Brylian Akbar</dc:creator>
	<dc:creator>Farikhin, Farikhin</dc:creator>
	<dc:creator>Surarso, Bayu</dc:creator>
	<dc:description xml:lang="en-US">Stock prices have unstable movements, so forecasting is needed to decide to invest appropriately according to the strategy. Fuzzy Time Series (FTS) uses fuzzy sets to forecast future time series values using historical data. However, interval partitioning in FTS needs to be considered as it can affect the forecasting results. FCM is applied to solve the problem of interval assignment in the universe of discourse. It allows the evaluation of the distribution of historical data and forming intervals of different sizes. Type 2 Fuzzy Time Series (T2FTS) is an extension of FTS to improve forecasting performance and refine fuzzy relationships. This research aims to improve forecasting accuracy using the Fuzzy C-Means (FCM)-T2FTS combination. This research uses daily data on BBRI stock prices from January 2023 to May 2024, with the variables used being close, high, and low prices. The results showed that determining the interval length using unequal length is more efficient than fixed interval length and can improve model performance, demonstrated from the MAPE values of T2FTS and FCM-T2FTS, which are 2.09% and 1.97%, respectively, the difference between the two MAPEs, is 0.12%. Hence, FCM-T2FTS is 12% more efficient than T2FTS. Therefore, FCM-T2FTS can improve forecasting accuracy.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2025-04-01</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/16147</dc:identifier>
	<dc:identifier>10.30598/barekengvol19iss2pp1365-1378</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application; 1365-1378</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol19iss2year2025</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/16147/9989</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Rineka Brylian Akbar Satriani, Farikhin Farikhin, Bayu Surarso</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>