STOCK PRICE FORECASTING USING FUZZY C-MEANS AND TYPE-2 FUZZY TIME SERIES
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 affe...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Pattimura
2025-04-01
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| Series: | Barekeng |
| Subjects: | |
| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/16147 |
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| Summary: | 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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| ISSN: | 1978-7227 2615-3017 |