Improved Weight Fuzzy Time Series as used in the Exchange rates Forecasting of US Dollar to Ringgit Malaysia

Foreign exchange rate (forex) forecasting has been the subject of several rigorous investigations due to its importance in evaluating the bene ̄ts and risks of the international business environments. Many methods have been researched with the ultimate goal being to increase the reliability and e±ciency 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 e±ciency 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 e±ciency of the daily exchange rate forecasting opportunities.