Improved Weight Fuzzy Time Series as Used in the Exchange Rates Forecasting of Us Dollar to Ringgit Malaysia

Abstract

Foreign exchange rate (forex) forecasting has been the subject of several rigorous investigations due to its importance in evaluating the benefits and risks of the international business environments. Many methods have been researched with the ultimate goal being to increase the reliability and efficiency of the forecasting method. However as the data are inherently dynamic and complex, the development of accurate forecasting method remains a challenging task if not a formidable one. This paper proposes a new weight of the fuzzy time series model for a daily forecast of the exchange rate market. Through this method, the weights are assigned to the fuzzy relationships based on a probability approach. This can be implemented to carry out the frequently recurring fuzzy logical relationship (FLR) in the fuzzy logical group (FLG). The US dollar to the Malaysian Ringgit (MYR) exchange rates are used as an example and the efficiency of the proposed method is compared with the methods proposed by Yu and Cheng et al. The result shows that the proposed method has enhanced the accuracy and efficiency of the daily exchange rate forecasting opportunities.

A simulation study of first-order autoregressive to evaluate the performance of measurement error based symmetry triangular fuzzy number

“…Data preparation transforms the data sets so that their information content is best exposed to the next process. Many studies [14][15][16][17][18] in AR frequently uses single point values as data input in building forecasting model. However, most single point values use the average data where this data will affect the standard deviation.…”

“…To address the limitation, the fuzzy theory is introduced in the AR model building which solves the uncertainty issues. In previous literatures, the issue of uncertainty has been studied by several authors by introducing fuzzy theory [14][15][16][17][18]. For example, fuzzy inference system was used to create a new air quality index while autoregressive model used to predict future air quality condition.…”

Prediction of Malaysian–Indonesian Oil Production and Consumption Using Fuzzy Time Series Model

“…On the other hand, Ismail et al [2013] proposed that the sub-interval number is equal to the range at interval i divided by 1+3.3 log (f i ), (i = 1, 2, 3, 4). The modification of our proposed interval can be mathematically compared with the interval proposed in Efendi et al [2013] as shown below. …”

“…The advantage of the first model, besides not needing assumptions and explanatory variables for the data set, is that it can be used to forecast linguistic values [Song and Chissom (1993a)]. The univariate-FTS model has been frequently implemented to forecast real data in sectors such as education [Song and Chissom (1993b);Chen (1996);Singh (2007); Kuo (2009);Ismail and Efendi (2011)], economy [Yu (2005); Yu and Huarng (2008); Lee et al (2006); Efendi et al (2013); Sun et al (2015); Gholizade and Chafi (2015)], and energy [Bolturuk et al (2012); Alpaslan and Cagcag (2012); Azadeh et al (2012); Efendi et al (2015); Ismail et al (2015); Efendi and Deris (2017)]. The forecasting accuracy of this model is improved by modifying the interval numbers of the data set and using out-sample model for linguistic time series.…”

Pythagorean fuzzy time series model based on Pythagorean fuzzy c-means and improved Markov weighted in the prediction of the new COVID-19 cases

“…Passionate about ANN algorithm, Egrioglu [18] also proposed the Picture fuzzy time series model, and also used ANN to replace the construction of fuzzy logic relations. However, because of the inexplicability of artificial neural networks, many scholars still prefer traditional prediction methods, such as Tsaur [19] and Efendi [20] using Markov weight method for prediction, and Li [21,22] is based on hidden Markov chains and long-term models.…”