Frequency density-based partitioning (FDP) for forecasting IHSG

Irawanto, Bambang; Ningrum, R. W.; Wulandari, R.; Surarso, Bayu; Farikhin,

doi:10.1088/1742-6596/1321/2/022081

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…For example, the adjustment value for June 16, 2019, the next state is A 39 , and the current state is A 38 ; then, the adjustment calculation uses the forecast adjustment rule point c with equation ( 19) D t2 = ðl/2Þs = ð0:5/2Þ1 = 0:25. For the 18), (19), and (20).…”

Section: Resultsmentioning

confidence: 99%

“…Chen and Hsu's research shows that after the redivided interval, the fuzzy time series gets a better accuracy value than other existing fuzzy time series. Irawanto et al [20] used frequency density-based partitioning for stock index forecasting. Wulandari et al [21] used frequency density partitioning for forecasting the production of petroleum which resulted in a small error value.…”

Section: Introductionmentioning

confidence: 99%

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Average-Based Fuzzy Time Series Markov Chain Based on Frequency Density Partitioning

Hariyanto

Sumanto

Khabibah

et al. 2023

Journal of Applied Mathematics

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Fuzzy time series (FTS) is one of the forecasting methods that has been developed until now. The fuzzy time series is a forecasting method that uses the concept of fuzzy logic, which Song and Chissom first introduced. The fuzzy time series (FTS) Markov chain uses the Markov chain in defuzzification. The determination of the length of the interval in the fuzzy time series plays an important role in forming a fuzzy logic relationship (FLR), and this FLR will be used to determine the forecasting value. One method that can be used to determine the interval length is average-based. However, several studies use partitioning based on frequency density to obtain the optimal interval length to get better forecasting accuracy. This study combines the fuzzy time series Markov chain, Average-based fuzzy time series, and Fuzzy time series based on frequency density partitioning to become average-based fuzzy time series Markov chain based on the Frequency Density Partition which conducts redivided intervals based on frequency density in the average-based fuzzy time series Markov chain method. This method is implemented in forecasting the Indonesian Islamic stock index (ISSI) for the selected period. The calculation of the accuracy level using the mean square error (MSE) and the mean average percentage error (MAPE) shows that the fuzzy Markov chain-based fuzzy time series based on the frequency density partition has a high level of accuracy in forecasting.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%