Foreign Exchange Rates Prediction using Fuzzy Based Support Vector Regression


  • Satyanarayana Reddy Beeram Associate Professor, Department of Computer Science and Engineering, KKR&KSR Institute of Technology and Sciences, Guntur, Andhra Pradesh, India
  • Lakshmikanth Paleti Associate Professor, Department of CSBS, RVR & JC College of Engineering, Guntur, Andhra Pradesh, India
  • Sambasiva Rao Aaradhyuala Assistant Professor, Department of IT, RVR & JC College of Engineering, Guntur, Andhra Pradesh, India.
  • Mahesh Reddy Gogula Assistant Professor, Department of Computer Science and Engineering, KITS Engineering College, Guntur, India
  • Narasimha Rao Yamarthi Professor, School of Computer Science and Engineering, VIT-AP University, India


Support Vector Machines, Fuzzy Logic, F-SVR, Foreign exchange value forecasting, USD-INR data set


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.


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How to Cite

Beeram, S. R. ., Paleti , L. ., Aaradhyuala, S. R. ., Gogula, M. R. ., & Yamarthi, N. R. . (2023). Foreign Exchange Rates Prediction using Fuzzy Based Support Vector Regression. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 355–364. Retrieved from



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