Year: 2023
Type: article
Abstract: Foreign exchange value prediction models are playing a vital role in financial decision making and global business trading. Forecasting the foreign exchange values with high accuracy, became a prominent research topic for both academic and economic research scholars. Due to the complexity and chaotic nature of foreign exchange dataset values, minimizing the range of prediction errors became another prominent research challenge in this area. Although many former researchers designed various time series prediction models, they were suffering from the prediction accuracy and prediction errors. In this paper, we proposed a Fuzzy based Support Vector Regression (F-SVR) model to address the former research limitations by incorporating the fuzzy logic with SVR. Fuzzy membership functions are used in this model to assign the time coefficients along with the data points, to get more control on training and prediction process of SVR. Experiments conducted on USD-INR dataset using the proposed F-SVR model with various kernel functions proven that the F-SVR with radial basis kernel function recorded the high prediction accuracy and less prediction error range than the traditional SVR kernels.
Institution: VIT-AP University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W7217001053