Foreign Exchange Rates Forecasting with Multilayer Perceptrons Neural Network by Bayesian Learning

2008 ◽  
Author(s):  
Wei Huang ◽  
Kin Keung Lai ◽  
Jinlong Zhang ◽  
Yukun Bao
Keyword(s):  
Neural Network ◽  
Exchange Rates ◽  
Foreign Exchange ◽  
Bayesian Learning ◽  
Multilayer Perceptrons ◽  
Foreign Exchange Rates ◽  
Foreign Exchange Rates Forecasting

Foreign Exchange Rates Forecasting with Convolutional Neural Network

2017 ◽  
Vol 46 (3) ◽  
pp. 1095-1119 ◽  
Author(s):  
Chen Liu ◽  
Weiyan Hou ◽  
Deyin Liu
Keyword(s):  
Neural Network ◽  
Exchange Rates ◽  
Convolutional Neural Network ◽  
Foreign Exchange ◽  
Foreign Exchange Rates ◽  
Foreign Exchange Rates Forecasting

Forecasting Foreign Exchange Rates Using an SVR-Based Neural Network Ensemble

2008 ◽  
pp. 261-277 ◽  
Author(s):  
Lean Yu ◽  
Shouyang Wang ◽  
Kin Keung Lai
Keyword(s):  
Neural Network ◽  
Exchange Rates ◽  
Support Vector Regression ◽  
Foreign Exchange ◽  
Support Vector ◽  
Foreign Exchange Rates ◽  
Neural Network Ensemble ◽  
Second Stage ◽  
Previous Phase ◽  
Foreign Exchange Rates Forecasting

In this study, a triple-stage support vector regression (SVR) based neural network ensemble forecasting model is proposed for foreign exchange rates forecasting. In the first stage, multiple single neural predictors are generated in terms of diversification. In the second stage, an appropriate number of neural predictors are selected as ensemble members from the considerable number of candidate predictors generated by the previous phase. In the final stage, the selected neural predictors are combined into an aggregated output in a nonlinear way based on the support vector regression principle. For further illustration, four typical foreign exchange rate series are used for testing. Empirical results obtained reveal that the proposed nonlinear neural network ensemble model can improve the performance of foreign exchange rates forecasting.

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

2021 ◽  
Vol 9 (4) ◽  
pp. 421-439
Author(s):  
Renquan Huang ◽  
Jing Tian
Keyword(s):  
Neural Network ◽  
Differential Evolution ◽  
Exchange Rates ◽  
Foreign Exchange ◽  
Differential Evolution Algorithm ◽  
Wavelet Function ◽  
Foreign Exchange Rates ◽  
Elman Neural Network ◽  
Evolution Algorithm ◽  
Hidden Layer

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.

Sign in / Sign up

Export Citation Format

Share Document