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

Susilo Hariyanto, Y. D. Sumanto, Siti Khabibah and Zaenurrohman

Journal of Applied Mathematics, 2023, vol. 2023, issue 1

Abstract: 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.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1155/2023/9319883

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:jnljam:v:2023:y:2023:i:1:n:9319883

Access Statistics for this article

More articles in Journal of Applied Mathematics from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().