Combining Empirical Mode Decomposition with Neural Networks for the Prediction of Exchange Rates

J. Mouton, A. J. Hoffman

2014

Abstract

This paper proposes a neural network based model applied to empirical mode decomposition (EMD) filtered data for multi-step-ahead prediction of exchange rates. EMD is used to decompose the returns of exchange rates into intrinsic mode functions (IMFs) which are partially recomposed to produce a low-pass filtered time series. This series is used to train a neural network for multi-step-ahead prediction. Out-of-sample tests on EUR/USD and USD/JPY rates show superior performance compared to random walk and neural network models that do not employ EMD filtering. The novel approach of using EMD as a filtering technique in combination with neural networks consistently delivers higher returns on investment and demonstrates its utility in multi-step-ahead prediction.

References

  1. Cheng, C.-H. & Wei, L.-Y., 2014. A novel time-series model based on empirical mode decomposition for forecasting TAIEX. Economic Modelling, 36, pp.136- 141.
  2. Flandrin, P., 2004. Empirical mode decomposition as a filter bank. IEEE signal processing letters, 11(2), pp.112-114.
  3. Fu, C., 2010. Forecasting exchange rate with EMD-based support vector regression. IEEE submissions, pp.0-3.
  4. Hsieh, D., 1989. Testing for nonlinear dependence in daily foreign exchange rates. Journal of Business, 62(3), pp.339-368.
  5. Huang, N.E. et al., 1996. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In Proceedings of the Royal Society London. pp. 904- 993.
  6. Huang, N.E. et al., 2003. Applications of Hilbert-Huang transform to non-stationary financial time series analysis. Applied Stochastic Models in Business and Industry, 19(4), pp.361-361.
  7. Imam, T., 2012. Intelligent Computing and Foreign Exchange Rate Prediction?: What We Know and We Don 78 t. Progress in Intelligent Computing and Applications, 1(1), pp.1-15.
  8. Laurene Fausett, 1994. Fundamentals of Neural Networks 1st ed., Upper Saddle River: Prentice Hall.
  9. Lin, C., Chiu, S.-hsiung & Lin, T., 2012. Empirical mode decomposition-based least squares support vector regression for foreign exchange rate forecasting. Economic Modelling, 29(6), pp.2583-2590.
  10. Lu, C.-J., Lee, T.-S. & Chiu, C.-C., 2009. Financial time series forecasting using independent component analysis and support vector regression. Decision Support Systems, 47(2), pp.115-125.
  11. Nusair, S.A., 2013. Real exchange rate dynamics in transition economies: a nonlinear analysis. International Journal of Finance and Economics, 18(March 2012), pp.188-204.
  12. Pavlidis, E.G., Paya, I. & Peel, D.A., 2012. Forecast Evaluation of Nonlinear Models?: The Case of LongSpan Real Exchange Rates. Journal of Forecasting, 595(September 2011), pp.580-595.
  13. Tay, F. & Cao, L., 2001. Application of support vector machines in financial time series forecasting. Omega, 29, pp.309-317.
  14. Wang, W. et al., 2009. A Novel Hybrid Intelligent Model for Financial Time Series Forecasting and Its Application. 2009 International Conference on Business Intelligence and Financial Engineering, pp.279-282.
  15. Xiong, T., Bao, Y. & Hu, Z., 2013. Beyond one-stepahead forecasting: Evaluation of alternative multistep-ahead forecasting models for crude oil prices. Energy Economics, 40, pp.405-415.
  16. Yang, H. & Lin, H., 2012. Combining Artificial Intelligence with Non-linear Data Processing Techniques for Forecasting Exchange Rate Time Series. International Journal of Digital Content Technology and its Applications, 6(April), pp.276-284.
  17. Yu, L., Wang, S. & Lai, K.K., 2008. Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Economics, 30(5), pp.2623-2635.
  18. Yu, L. et al., 2010. A multiscale neural network learning paradigm for financial crisis forecasting. Neurocomputing, 73(4-6), pp.716-725.
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Paper Citation


in Harvard Style

Mouton J. and Hoffman A. (2014). Combining Empirical Mode Decomposition with Neural Networks for the Prediction of Exchange Rates . In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014) ISBN 978-989-758-054-3, pages 244-249. DOI: 10.5220/0005130702440249


in Bibtex Style

@conference{ncta14,
author={J. Mouton and A. J. Hoffman},
title={Combining Empirical Mode Decomposition with Neural Networks for the Prediction of Exchange Rates},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)},
year={2014},
pages={244-249},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005130702440249},
isbn={978-989-758-054-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)
TI - Combining Empirical Mode Decomposition with Neural Networks for the Prediction of Exchange Rates
SN - 978-989-758-054-3
AU - Mouton J.
AU - Hoffman A.
PY - 2014
SP - 244
EP - 249
DO - 10.5220/0005130702440249