Forecasting the Composite Stock Price Index Using Fuzzy Time Series Type 2
Peramalan Indeks Harga Saham Gabungan dengan menggunakan Metode Fuzzy Time Series Tipe 2
DOI:
https://doi.org/10.52187/rdt.v4i2.167Keywords:
cpsi, fuzzy time series type 2, length of the interval, mapeAbstract
The movement and fluctuation of the IHSG are one of the references for investors in making investment decisions for buying, selling, or holding share ownership. Forecasting the value of the IHSG can assist investors in making this decision. This study used the fuzzy time series type 2 method to predict the IHSG. This study uses monthly IHSG data for 2017-2021 with 3 variables, namely close prices, high prices, and low prices. In fuzzy time series forecasting, the length of the interval affects the prediction results. This study uses a distribution and average-based method in determining the length of the interval to obtain optimal forecasting results. Based on the calculation of MAPE, the forecasting using average-based and distribution-based interval lengths had errors of 2.56% and 2.46%. The MAPE value shows that the forecasting results from the two methods of taking the length of the interval are very good. The IHSG forecasting results in January 2022 use a distribution-based interval length is 6450 while the IHSG forecasting results in January 2022 use an average-based interval length is 6510. The results of this study indicate that the interval length affects the forecasting results in fuzzy time series.
Keywords: IHSG, fuzzy time series type 2, length of the interval, MAPE
References
A. Bayu Elfajar,, B. C. (2017). Peramalan Jumlah Kunjungan Wisatawan Kota Batu Menggunakan Metode Time Invariant Fuzzy Time Series. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 1(2), 85-94.
Asrini, L. J. (2017). Penentuan Model Indeks Harga Saham Gabungan (IHSG) Menggunakan Analisis Fungsi Transfer. 18–28.
C. Sri,, W. L. (t.thn.). Comparison of Several Methods of The Measurement Error Estimation.
Huarng, K. (2001). Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy Sets and Systems, 123(3), 387-394. doi:https://doi.org/10.1016/S0165-0114(00)00057-9
K. Huarng, H. (2004). A type 2 fuzzy time series model for stock index forecasting. Physica A: Statistical Mechanics and its Applications, 353(1–4), 445–462. doi:doi: 10.1016/j.physa.2004.11.070
M. Muhammad,, S. M. (2021). Peramalan Nilai Tukar Petani Subsektor Peternakan Menggunakan Fuzzy Time Series Lee. Jambura Journal of Mathematics, 3(1), 1–15. doi:doi: 10.34312/jjom.v3i1.5940
N. A. Nugroho,, A. (2015). Analisis 9 Saham Sektor Industri di Indonesia Menggunakan Metode SVR.
P. C. Chang,, Y. C. (2007). The development of a weighted evolving fuzzy neural network for PCB sales forecasting. Expert Syst Appl, 32(1), 86–96. doi:10.1016/j.eswa.2005.11.021
P. Verma, ,. M. (2020). Forecasting the COVID-19 Outbreak: An Application of Arima and Fuzzy Time Series Models. Forecasting the COVID-19 Outbreak: An Application. doi:https://doi.org/10.21203/rs.3.rs-36585/v1
V. Virgianti, S. M. (2021). Penerapan Fuzzy Time Series Chen Average Based pada Peramalan Curah Hujan. Bimaster : Buletin Ilmiah Matematika, Statistika dan Terapannya, 10(4). doi:http://dx.doi.org/10.26418/bbimst.v10i4.50913
