Forecasting FTSE Bursa Malaysia KLCI Trend with Hybrid Particle Swarm Optimization and Support Vector Machine Technique

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F13%3A86089370" target="_blank" >RIV/61989100:27740/13:86089370 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/NaBIC.2013.6617856" target="_blank" >http://dx.doi.org/10.1109/NaBIC.2013.6617856</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/NaBIC.2013.6617856" target="_blank" >10.1109/NaBIC.2013.6617856</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Forecasting FTSE Bursa Malaysia KLCI Trend with Hybrid Particle Swarm Optimization and Support Vector Machine Technique

Popis výsledku v původním jazyce

Stock trend forecasting is one of the important issues in stock market research. However, forecasting stock trend remains a challenge because of its irregular characteristic in the stock indices distribution, which changes over time. Support Vector Machine (SVM) produces a fairly good result in stock trend forecasting, but the performance of SVM can be affected by the high dimensional input features and noisy data. This paper hybridizes the Particle Swarm Optimization (PSO) algorithm to generate the optimum features set prior to facilitate SVM learning. The SVM algorithm uses the Radial Basis Function (RBF) kernel function and optimization of the gamma and large margin parameters are done using the PSO algorithm. The proposed algorithm was tested on apre-sampled 17 years record of daily Kuala Lumpur Composite Index (KLCI) data. The PSOSVM approach is applied to eliminate unnecessary or insignificant features, and effectively determine the parameter values, in turn improving the overal

Název v anglickém jazyce

Forecasting FTSE Bursa Malaysia KLCI Trend with Hybrid Particle Swarm Optimization and Support Vector Machine Technique

Popis výsledku anglicky

Stock trend forecasting is one of the important issues in stock market research. However, forecasting stock trend remains a challenge because of its irregular characteristic in the stock indices distribution, which changes over time. Support Vector Machine (SVM) produces a fairly good result in stock trend forecasting, but the performance of SVM can be affected by the high dimensional input features and noisy data. This paper hybridizes the Particle Swarm Optimization (PSO) algorithm to generate the optimum features set prior to facilitate SVM learning. The SVM algorithm uses the Radial Basis Function (RBF) kernel function and optimization of the gamma and large margin parameters are done using the PSO algorithm. The proposed algorithm was tested on apre-sampled 17 years record of daily Kuala Lumpur Composite Index (KLCI) data. The PSOSVM approach is applied to eliminate unnecessary or insignificant features, and effectively determine the parameter values, in turn improving the overal

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: Centrum excelence IT4Innovations</a><br>

Návaznosti

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

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název statě ve sborníku

2013 World Congress on Nature and Biologically Inspired Computing, NaBIC 2013

ISBN

978-1-4799-1415-9

ISSN

—

e-ISSN

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Počet stran výsledku

6

Strana od-do

169-174

Název nakladatele

Elsevier

Místo vydání

New York

Místo konání akce

Fargo

Datum konání akce

12. 8. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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