Financial Time Series Modelling with Hybrid Model Based on Customized RBF Neural Network Combined With Genetic Algorithm

Advances in Electrical and Electronic Engineering

Dusan Marcek¹  Lukas Falat² 

[1] ;Department of Applied InformaticsFaculty of EconomicsVSB-Technical University of OstravaSokolska 33, 701 21 Ostrava;

关键词: artificial neural network;    generic algorithm;    hybrid model;    rbf;    time series;    usd/cad.;   

DOI  :  10.15598/aeee.v12i4.1206

来源: DOAJ

【 摘 要 】

In this paper, authors apply feed-forward artificial neural network (ANN) of RBF type into the process of modelling and forecasting the future value of USD/CAD time series. Authors test the customized version of the RBF and add the evolutionary approach into it. They also combine the standard algorithm for adapting weights in neural network with an unsupervised clustering algorithm called K-means. Finally, authors suggest the new hybrid model as a combination of a standard ANN and a moving average for error modeling that is used to enhance the outputs of the network using the error part of the original RBF. Using high-frequency data, they examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, authors perform the comparative out-of-sample analysis of the suggested hybrid model with statistical models and the standard neural network.

【 授权许可】

Unknown