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

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

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“…Compared with short‐time Fourier transform and wavelet decomposition, the EMD is intuitive, direct, posterior, and adaptive. Hence, the hybrid EMD‐ANN is a promising approach to solve the problem of complex time series, which is widely used in the field of engineering, such as in fault diagnosis system, exchange rate prediction, apparent‐age estimation system, and so on.…”