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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) · hlm. 1365–1378

DOI
10.30598/barekengvol19iss2pp1365-1378
ISSN
2615-3017, 1978-7227
Terbit
2025-04-01
Penerbit
PATTIMURA UNIVERSITY
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

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.

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  • Diponegoro University
  • Edith Cowan University
  • University of Indonesia
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Rekan penulisOrganisasiArtikel bersama
Oky Dwi NurhayatiDiponegoro University3
Budi WarsitoDiponegoro University2
Dinar Mutiara Kusumo NugraheniDiponegoro University2
Retno KusumaningrumDiponegoro University2
Fitriana MasrurohDiponegoro University1
Willy Bima AlfajriDiponegoro University1
Nitami Lestari PutriDiponegoro University1
Bambang SubenoDiponegoro University1
Wahyul Amien SyafeiDiponegoro University1
Ferry JieEdith Cowan University1
Irza Nur FauziDiponegoro University1
Dinar NugraheniAfiliasi tidak diketahui1
Desty DwiyanasariDiponegoro University1
Nurhita RahmawatiDiponegoro University1
Hardi SuyitnoAfiliasi tidak diketahui1
Fitri KensiwiAfiliasi tidak diketahui1
Nuzulul Khairu NissaUniversity of Indonesia1
Farikhin FarikhinAfiliasi tidak diketahui1
Selvi Fitria KhoerunnisaDiponegoro University1

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Sumber metadata: OAI-PMH (metadata jurnal) https://ojs3.unpatti.ac.id/index.php/barekeng/oai, pertama di-harvest 5 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (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>