FOREX Rate Prediction: A Hybrid Approach Using Chaos Theory and Multivariate Adaptive Regression Splines

In order to predict foreign exchange (FOREX) rates, this paper proposes a new hybrid forecasting approach viz., Chaos+MARS involving chaos theory and multivariate adaptive regression splines (MARS). Chaos theory aims at constructing state space from the given exchange rate data with the help of embedding parameters, whereas MARS aims at yielding accurate predictions using state space constructed. The proposed model is tested for predicting three major FOREX Rates- JPY/USD, GBP/USD, and EUR/USD. The results obtained unveil that the Chaos+MARS yields the accurate predictions than other chaos-based hybrid forecasting models and recommend it as an alternative approach to FOREX rate prediction.

[1]  A. N. Edmonds,et al.  Simultaneous prediction of multiple financial time series using supervised learning and chaos theory , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[2]  Ajith Abraham,et al.  Analysis of hybrid soft and hard computing techniques for forex monitoring systems , 2002, 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291).

[3]  Tian-Shyug Lee,et al.  A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines , 2005, Expert Syst. Appl..

[4]  Vadlamani Ravi,et al.  FOREX Rate Prediction Using Chaos, Neural Network and Particle Swarm Optimization , 2014, ICSI.

[5]  Arleen J. Hoag,et al.  Introductory Economics: Classroom Material , 2006 .

[6]  H. Akaike.  A new look at the statistical model identification , 1974 .

[7]  James P. Crutchfield,et al.  Geometry from a Time Series , 1980 .

[8]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[9]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[10]  Jingtao Yao,et al.  A case study on using neural networks to perform technical forecasting of forex , 2000, Neurocomputing.

[11]  Ahmed BenSaïda,et al.  Using the Lyapunov Exponent as a Practical Test for Noisy Chaos , 2007 .

[12]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[13]  Volker Roth,et al.  The generalized LASSO , 2004, IEEE Transactions on Neural Networks.

[14]  F. Takens.  Detecting strange attractors in turbulence , 1981 .

[15]  L. Cao.  Practical method for determining the minimum embedding dimension of a scalar time series , 1997 .

[16]  Dimitris K. Tasoulis,et al.  Financial forecasting through unsupervised clustering and evolutionary trained neural networks , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[17]  J. Gooijer,et al.  Forecasting exchange rates using TSMARS , 1998 .

[18]  Shian-Chang Huang,et al.  Chaos-based support vector regressions for exchange rate forecasting , 2010, Expert Syst. Appl..

[19]  Chih-Chou Chiu,et al.  Stock index prediction: A comparison of MARS, BPN and SVR in an emerging market , 2009, 2009 IEEE International Conference on Industrial Engineering and Engineering Management.

[20]  J. Friedman.  Multivariate adaptive regression splines , 1990 .