Wavelet-Based Elman Neural Network with the Modified Differential Evolution Algorithm for Forecasting Foreign Exchange Rates

Renquan Huang,Jing Tian

doi:10.21078/jssi-2021-421-19

Renquan Huang, Jing Tian

Open Access

https://doi.org/10.21078/jssi-2021-421-19

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Abstract

Abstract It is challenging to forecast foreign exchange rates due to the non-linear characters of the data. This paper applied a wavelet-based Elman neural network with the modified differential evolution algorithm to forecast foreign exchange rates. Elman neural network has dynamic characters because of the context layer in the structure. It makes Elman neural network suit for time series problems. The main factors, which affect the accuracy of the Elman neural network, included the transfer functions of the hidden layer and the parameters of the neural network. We applied the wavelet function to replace the sigmoid function in the hidden layer of the Elman neural network, and we found there was a “disruption problem” caused by the non-linear performance of the wavelet function. It didn’t improve the performance of the Elman neural network, but made it get worse in reverse. Then, the modified differential evolution algorithm was applied to train the parameters of the Elman neural network. To improve the optimizing performance of the differential evolution algorithm, the crossover probability and crossover factor were modified with adaptive strategies, and the local enhanced operator was added to the algorithm. According to the experiment, the modified algorithm improved the performance of the Elman neural network, and it solved the “disruption problem” of applying the wavelet function. These results show that the performance of the Elman neural network would be improved if both of the wavelet function and the modified differential evolution algorithm were applied integratedly.

Highlights

There are non-linear and volatile characters of the foreign exchange rate data, and it is not efficient to forecast by current statistical models[1]
The results show that the modified Elman neural network (ENN) has lower mean square error (MSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) values in comparison with multilayer perceptron (MLP)
To study the performances of the models, which were ENN, wavelet-based Elman neural network (WENN), ADLEDE-ENN and ADLEDE-WENN, all the 4 models were researched in the experiments to each group of the foreign exchange rate

Summary

Introduction

There are non-linear and volatile characters of the foreign exchange rate data, and it is not efficient to forecast by current statistical models[1]. HUANG R Q, TIAN J. which was applied with the modified differential evolution algorithm integratedly, is proposed to forecast foreign exchange rates. ANNs performs better than the traditional statistical models such as ARIMA in forecasting the foreign exchange rate. Et al.[2] explored both the ARIMA model and neural networks for the Turkish TL/US dollar exchange rate series. Results show that the ANN method has far better accuracy compared to the ARIMA time series model. Et al.[3] compared the feedforward multilayer perceptron (MLP) with a modified Elman neural network (ENN) model to predict a company stock value. To the structure of ENN, it does not require the state as an input or training signal This makes it superior to static feedforward networks, and can be widely used in a dynamic system[4−6]. Zhang[17] studied the frames of wavelet

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