Applying a Fuzzy and Neural Approach for Forecasting the Foreign Exchange Rate

Abstract

Accurately forecasting the foreign exchange rate is important for export-oriented enterprises. For this purpose, a fuzzy and neural approach is applied in this study. In the fuzzy and neural approach, multiple experts construct fuzzy linear regression (FLR) equations from various viewpoints to forecast the foreign exchange rate. Each FLR equation can be converted into two equivalent nonlinear programming problems to be solved. To aggregate these fuzzy foreign exchange rate forecasts, a two-step aggregation mechanism is applied. At the first step, fuzzy intersection is applied to aggregate the fuzzy forecasts into a polygon-shaped fuzzy number to improve the precision. A back propagation network is then constructed to defuzzify the polygon-shaped fuzzy number and generate a representative/crisp value to enhance accuracy. To evaluate the effectiveness of the fuzzy and neural approach, a practical case of forecasting the foreign exchange rate in Taiwan is used. According to the experimental results, the fuzzy and neural approach improved both the precision and accuracy of the foreign exchange rate forecasting by 79% and 81%, respectively.