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Abstract :
Forecasting the Indonesia Composite Index (ICI) is one of the important efforts in making investment decisions in the capital market. This index helps investors see changes in stock prices directly, making it easier to know whether stock prices are rising or falling. In this research, a combined approach between Fuzzy Time Series (FTS) Type-2 and an automatic clustering algorithm is applied to improve the accuracy of ICI forecasting. The advantage of FTS Type-2 is that it can provide more accurate results than FTS Type-1 because it can express more information. At the same time, automatic clustering is also used because it can partition the universe of discourse efficiently. The data used is monthly data on the closing, highest, and lowest prices of the Indonesia Composite Index from January 2019 to July 2024. The results of this study show an AFER value of 0.6827 and a forecasting accuracy of 99.317%. This shows that the forecasting results using FTS Type-2 and automatic clustering algorithms provide excellent forecasting results. This research has the novelty of using automatic clustering in determining the interval of Fuzzy Time Series Type-2 for ICI forecasting.
Keywords :
AFER, Automatic Clustering, Fuzzy Time Series Type-2, Indonesia Composite Index.References :
- Fuad and I. Yuliadi, “Determinants of the Composite Stock Price Index (IHSG) on the Indonesia Stock Exchange,” J. Econ. Res. Soc. Sci., vol. 5, no. 1, pp. 27–41, 2021, doi: 10.18196/jerss.v5i1.11002.
- and B. S. C. Song, “Forecasting Enrollments with Fuzzy time series – Part 1,” Int. J. Fuzzy Set Syst., vol. 54, no. 1, pp. 1–9, 1993.
- Wang, X. Liu, M. Chi, and Y. Li, “Bayesian network based probabilistic weighted high-order fuzzy time series forecasting,” Expert Syst. Appl., vol. 237, p. 121430, 2024, doi: https://doi.org/10.1016/j.eswa.2023.121430.
- R. Rahma, T. Udjiani, B. Irawanto, and B. Surarso, “Fuzzy time series forecasting with picture fuzzy clustering (FC-PFS) and picture composite cardinality (PCC),” in AIP Conference Proceedings, AIP Publishing, 2022.
- S. Mukminin, B. Irawanto, B. Surarso, and Farikhin, “Two-factor fuzzy time series forecasting based on centroid method for forecasting air quality index (AQI),” AIP Conf. Proc., vol. 2566, no. 1, p. 30003, Nov. 2022, doi: 10.1063/5.0116540.
- S. Mukminin, B. Irawanto, B. Surarso, and Farikhin, “Fuzzy time series based on frequency density-based partitioning and k-means clustering for forecasting exchange rate,” J. Phys. Conf. Ser., vol. 1943, no. 1, p. 12119, 2021, doi: 10.1088/1742-6596/1943/1/012119.
- Irawanto, R. W. Ningrum, B. Surarso, and Farikhin, “An improved forecasting method of frequency density partitioning (FDP) based on fuzzy time series (FTS),” J. Phys. Conf. Ser., vol. 1321, no. 2, p. 22082, 2019, doi: 10.1088/1742-6596/1321/2/022082.
- dan H.-K. Y. Huarng, “A Type 2 fuzzy time series model for stock index forecasting,” J. Phys., vol. 353, pp. 445–462, 2005.
- Liu, “Forecasting stock prices based on multivariable fuzzy time series,” AIMS Math., vol. 8, no. 6, pp. 12778–12792, 2023.
- Tirta, N. Perdana, and B. Mulyawan, “Sparepart sales clusterization and prediction using an automatic clustering algorithm,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1007, p. 12191, Dec. 2020, doi: 10.1088/1757-899X/1007/1/012191.
- et al Kapoor, “A Grey Wolf Optimizer Based Automatic Clustering Algorithm for Satellite Image Segmentation,” Procedia Comput. Sci., vol. 115, pp. 415–422, 2017.
- P. de A. Pinto, Arthur C. Vargas, Larissa C. C. da Silva, Petronio C. L. Silva, Frederico G. Guimaraes, “Autonomous data partitioning for type‑2 fuzzy set based time series,” J. Envol. Syst., 2023, doi: https://doi.org/10.1007/s12530-023-09532-x.
- -M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy Sets Syst. 81, pp. 311–319, 1996.
- O. Lucas, O. Orang, P. C. L. Silva, E. M. A. M. Mendes, and F. G. Guimarães, “A Tutorial on Fuzzy Time Series Forecasting Models: Recent Advances and Challenges,” Learn. Nonlinear Model., vol. 19, no. 2, pp. 29–50, 2022, doi: 10.21528/lnlm-vol19-no2-art3.
- N. H. dan S. W. Sumartini, “Peramalan Menggunakan Metode Fuzzy Time Series Cheng,” J. Eksponensial, vol. 8, no. 1, pp. 51–56, 2017.
