Forecasting Currency Pairs with RBF Neural Network Using Activation Function Based on Generalized Normal Distribution Experimental Results

Result code in IS VaVaI

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Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Forecasting Currency Pairs with RBF Neural Network Using Activation Function Based on Generalized Normal Distribution Experimental Results

Original language description

In this paper, we implement an effective way for forecasting financial time series with nonlinear relationships. We use the artificial neural network of feedforward type for making the decision-process in a company more efficient, more flexible and more accurate. The main objective of this study is to design new method for improving the performance of RBF artificial neural networks. Based on pre-experimental statistical analysis of 1225 financial time series and inspired by GARCH model, RBF neural networks with new shapes of activation functions based on generalized Normal distribution function (GED) are suggested and discussed. Within this study various types of GED activation function in RBF networks are investigated to find the best ones. Firstly, the presence of homoscedasticity and the occurrence of normality of the time series data is investigated. To test our hypothesis about the application of GED distribution in the RBF neural network, we implemented a neural network application (RBFNN) in JAVA. Using the software, we investigate the RMSE error based on the value of p parameter in GED. The optimized size of the p parameter is determined for classic and soft RBF network related to minimal prediction error. We then test our model on 25 financial datasets to explore the contribution of our suggested and implemented method. We also evaluate the forecasting performance of suggested neural network in comparison to established models based on RMSE. Our results show that the proposed approach achieves higher forecasting accuracy on the validation set than available techniques. The suggested modification form of the shape of activation function of the RBF neural network using GED distribution improves the approximation and prediction accuracy of the RBF network models used for financial time series. From performed experiments we find that the optimal size of the parameter p will likely be in the interval (1.4, 2.4) for a standard RBF and less than 2 for the soft RBF.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

10200 - Computer and information sciences

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Multiple-Valued Logic and Soft Computing

ISSN

1542-3980

e-ISSN

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Volume of the periodical

6

Issue of the periodical within the volume

33

Country of publishing house

US - UNITED STATES

Number of pages

24

Pages from-to

539-563

UT code for WoS article

000510485200002

EID of the result in the Scopus database

2-s2.0-85078211124