Financial time series modelling with hybrid model based on customized RBF neural network combined with genetic algorithm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F14%3A86091167" target="_blank" >RIV/61989100:27510/14:86091167 - isvavai.cz</a>

Výsledek na webu

<a href="http://advances.utc.sk/index.php/AEEE/index" target="_blank" >http://advances.utc.sk/index.php/AEEE/index</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.15598/aeee.v12i4.1206" target="_blank" >10.15598/aeee.v12i4.1206</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Financial time series modelling with hybrid model based on customized RBF neural network combined with genetic algorithm

Popis výsledku v původním jazyce

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 approachinto 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.

Název v anglickém jazyce

Financial time series modelling with hybrid model based on customized RBF neural network combined with genetic algorithm

Popis výsledku anglicky

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 approachinto 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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/EE2.3.20.0296" target="_blank" >EE2.3.20.0296: Výzkumný tým pro modelování ekonomických a finančních procesů na VŠB-TU Ostrava</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název periodika

Advances in Electrical and Electronic Engineering

ISSN

1336-1376

e-ISSN

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Svazek periodika

12

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

11

Strana od-do

307-317

Kód UT WoS článku

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EID výsledku v databázi Scopus

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