The cross-association relation based on intervals ratio in fuzzy time series

Abstract

<span lang="EN-US">The fuzzy time series (FTS) is a forecasting model based on linguistic values. This forecasting method was developed in recent years after the existing ones were insufficiently accurate. Furthermore, this research modified the accuracy of existing methods for determining and the partitioning universe of discourse, fuzzy logic relationship (FLR), and variation historical data using intervals ratio, cross association relationship, and rubber production Indonesia data, respectively. The modified steps start with the intervals ratio to partition the determined universe discourse. Then the triangular fuzzy sets were built, allowing fuzzification. After this, the FLR are built based on the cross-association relationship, leading to defuzzification. The average forecasting error rate (AFER) was used to compare the modified results and the existing methods. Additionally, the simulations were conducted using rubber production Indonesia data from 2000-2020. With an AFER result of 4.77%<10%, the modification accuracy has a smaller error than previous methods, indicating very good forecasting criteria. In addition, the coefficient values of </span><em><span lang="EN-US">D<sub>1</sub></span></em><span lang="EN-US"> and </span><em><span lang="EN-US">D<sub>2</sub></span></em><span lang="EN-US"> were automatically obtained from the intervals ratio algorithm. The future works modified the partitioning of the universe of discourse using frequency density to eliminate unused partition intervals.</span>

Association rules forecasting for the foreign exchange market

“…The long-association relationship, demonstrated by the long-distance historical data, was proposed by Li and Yu [20] and applied to the synthetic time series data. Vianita et al [21] initially presented the issue of association rule mining and recognized the identification of frequent itemsets as a crucial step in the process of association rule. Most researchers employed a data mining approach to investigate frequent itemsets and uncover association rules.…”

Ratio Interval-Frequency Density with Modifications to the Weighted Fuzzy Time Series

“…The FTS was first presented by Song & Chissom (1993), and since then, it has seen a lot of development, including interval ratio (Huarng & Yu, 2006;Haikal et al, 2022;Vianita et al, 2023), frequency density partitioning (Hariyanto et al, 2023;Irawanto et al, 2019;Jilani et al, 2007;Mukminin et al, 2021), the weighted FTS (Jiang et al, 2017;Yu, 2005). Forecasting model uses first-order (Lu et al, 2015;Mirzaei Talarposhti et al, 2016;Singh & Borah, 2013) or highorder (Lu et al, 2015;Mirzaei Talarposhti et al, 2016;Singh & Borah, 2013) methods.…”

“…Interval ratio Huarng & Yu (2006); Haikal et al (2022); Vianita et al (2023) and frequency density partitioning (Hariyanto et al, 2023;Irawanto et al, 2019;Jilani et al, 2007;Mukminin et al, 2021) in fuzzy relationships that were consistently disregarded. In Yu (2005) studied that forecasting must devote responsibility for recurring fuzzy connections and provide varied weights to different fuzzy interactions.…”