Foreign Exchange Rates Prediction using Fuzzy Based Support Vector Regression
Keywords:
Support Vector Machines, Fuzzy Logic, F-SVR, Foreign exchange value forecasting, USD-INR data setAbstract
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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J. Brown, 2015. Forex Trading: The Basics Explained in Simple Terms, Plus Free Bonus Trading System. Forex, Forex for Beginners, Make Money Online, Currency Trad. CreateSpace Independent Publishing Platform, ISBN- 9781535198561, 72.
D.A. Hsieh, 1992. A nonlinear stochastic rational expectations model of exchange rates. Journal of International Money and Finance, 11(3):235–250.
S. Mukherjee, E. Osuna, and F. Girosi, 1997. Nonlinear prediction of chaotic time series using support vector machines. In Neural Networks for Signal Processing VII. Proceedings of the IEEE Signal Processing Society Workshop, pages 511–520. IEEE.
G.J. Deboeck, 1994. Trading on the edge: neural, genetic, and fuzzy systems for chaotic financial markets. volume 39. John Wiley & Sons.
W. Huang, K.K. Lai, Y. Nakamori, and S. Wang, 2004. Forecasting foreign exchange rates with artificial neural networks: A review. International Journal of Information Technology & Decision Making, 3(01):145–165.
E.W. Tyree, and J. Long, 1995. Forecasting currency exchange rates: neural networks and the random walk model. In City University Working Paper, Proceedings of the Third International Conference on Artificial Intelligence Applications, Citeseer.
C. Banchhor, and N. Srinivasu, 2018. Fcnb: Fuzzy correlative naı̈ve bayes classifier with mapreduce framework for big data classification. Journal of Intelligent Systems, 29(1):994–1006.
M. Alamili, 2011. Exchange rate prediction using support vector machines: a comparison with artificial neural networks. Delft University of Technology Master of Science in Management of Technology, 2011, Thesis.
P. Nanthakumaran, and C. Tilakaratne, 2017. A comparison of accuracy of forecasting models: A study on selected foreign exchange rates. In 2017 Seventeenth International Conference on Advances in ICT for Emerging Regions (ICTer), , pages 1–8. IEEE.
R. Tripathy, R.K. Nayak, P. Das, and D. Mishra, 2020. Cellular cholesterol prediction of mammalian atp-binding cassette (abc) proteins based on fuzzy c-means with support vector machine algorithms. Journal of Intelligent & Fuzzy Systems, Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 1611-1618.
A. Prathyusha, 2020. Diabetic prediction using kernel based support vector machine. International Journal of Advanced Trends in Computer Science and Engineering, 9:1178–1183.
T. Inoue, and S. Abe, 2001. Fuzzy support vector machines for pattern classification. In IJCNN’01. International Joint Conference on Neural Networks. Proceedings (Cat. No. 01CH37222), volume 2, pages 1449–1454.300IEEE.
R.K. Nayak, R. Tripathy, D. Mishra, V.K. Burugari, P. Selvaraj, A. Sethy, and B. Jena. 2019. Indian stock market prediction based on rough set and support vector machine approach. In Intelligent and Cloud Computing , pages 345–355. Springer.
R. Leelavathi, G.S. Kumar, and M. Murty, 2018. Nabla integral for fuzzy functions on time scales, International Journal of Applied Mathematics, 31(5):669–678.
F. Rosenblatt. 1957. The perceptron, a perceiving and recognizing automaton Project Para. Report: Cornell Aeronautical Laboratory , 85, 460–461.
B.E. Boser, I.M. Guyon, and V.N. Vapnik, 1992. A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational learning theory, 144–152.
C. Cortes, and V. Vapnik, 1995. Support-vector networks. Machine learning , 20(3):273–297.
H. Drucker, C.J. Burges, L. Kaufman, A.J. Smola, and V. Vapnik, 1997. Support vector regression machines. In Advances in neural information processing systems, 155–161.
P.S. Bhargav, G.N. Reddy, R.R.K.P. Chand, and A. Mathur, 2019. Sentiment analysis for hotel rating using machine learning algorithms. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(6).
M. Ismail, V.H. Vardhan, V.A. Mounika, and K.S. Padmini. 2019. An effective heart disease prediction method using artificial neural network. International Journal of Innovative Technology and Exploring Engineering, International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8:1529–1532.
P. Rajesh, N. Srinivas, K.V. Reddy, G. VamsiPriya, M.V. Dwija, and D. Himaja, 2019. Stock trend prediction using ensemble learning techniques in python. International Journal of Innovative Technology and Exploring Engineering, 8(5):150–155.
K.M. Krishna, N.K. Reddy, and M.R. Sharma, 2019. Forecasting of daily prices of gold in india using ARIMA and FFNN Models. International journal of engineering and advanced technology(IJEAT), 8, 3:516-521.
S.S. Imambi, P. Vidyullatha, M.V.B.T. Santhi, and P.H. Babu, 2018. Explore Big Data and forecasting future values using univariate ARIMA model in R. International Journal of Engineering & Technology, 7(2.7), 1107-1110..
W.Y. Cheng, and C.F. Juang, 2013. A fuzzy model with online incremental svm and margin-selective gradient descent learning for classification problems. IEEE Transactions on Fuzzy systems, 22(2):324–337.
Y. Dash, S.K. Mishra, and B.K. Panigrahi, 2019. Prediction of south west monsoon rainfall for kerala, india using ε-svr model. In 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), 1, 746–748. IEEE.
O.L. Mangasarian, and D.R. Musicant, 2001. Lagrangian support vector machines. Journal of Machine Learning Research, 1:161–177.
X. Zhang, 1999. Using class-center vectors to build support vector machines. In Neural Networks for Signal Processing IX: Proceedings of the IEEE Signal Processing Society Workshop (Cat. No. 98TH8468), pages 3–11. IEEE.
C.F. Lin, and S.D. Wang, 2002. Fuzzy support vector machines. IEEE transactions on neural networks, 13(2):464–471.
X. Jiang, Z. Yi, and J.C. Lv, 2006. Fuzzy svm with a new fuzzy membership function. Neural Computing & Applications, 15(3-4):268–276.
A. Botchkarev, 2019. A new typology design of performance metrics to measure errors in machine learning regression algorithms. Interdisciplinary Journal of Information, Knowledge & Management, 14.
J.X. Liu, J. Li, and Y.J. Tan, 2005. An empirical assessment on the robustness of support vector regression with different kernels. In 2005 International Conference on Machine Learning and Cybernetics, 7, 4289–4294. IEEE.
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