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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 8/2011

01.11.2011 | ISNN 2010

Correlation-aided support vector regression for forex time series prediction

verfasst von: Shaoning Pang, Lei Song, Nik Kasabov

Erschienen in: Neural Computing and Applications | Ausgabe 8/2011

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Abstract

Market is often found behaving surprisingly similar to history, which implies that correlation exists significant for market trend analysis. In the context of Forex market analysis, this paper proposes a correlation-aided support vector regression (cSVR) for time series application, where correlation data are extracted through a graphical channel correlation analysis, compensated by a parameterized Pearson’s correlation to exclude noise meanwhile minimize useful information lost. The effectiveness of cSVR against SVR is confirmed by experiments on 5 contracts (NZD/AUD, NZD/EUD, NZD/GBP, NZD/JPY, and NZD/USD) exchange rate prediction within the period from January 2007 to December 2008.
Vorheriger Artikel A real-time BCI with a small number of channels based on CSP
Nächster Artikel Global exponential stability of a class of Hopfield neural networks with delays

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Metadaten
Titel
Correlation-aided support vector regression for forex time series prediction
verfasst von
Shaoning Pang
Lei Song
Nik Kasabov
Publikationsdatum
01.11.2011
Verlag
Springer-Verlag
Erschienen in
Neural Computing and Applications / Ausgabe 8/2011
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-010-0482-5

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