A hybrid slantlet denoising least squares support vector regression model for exchange rate prediction

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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  • Kaijian He
  • Kin Keung Lai
  • Jerome Yen

Related Research Unit(s)


Original languageEnglish
Pages (from-to)2407-2415
Journal / PublicationProcedia Computer Science
Issue number1
Publication statusPublished - 2010


Title10th International Conference on Computational Science 2010, ICCS 2010
Period31 May - 2 June 2010



Despite the active exploration of linear and nonlinear modeling of exchange rates, there is no consensus on the optimal forecasting model other than the traditional random walk and ARMA benchmark models in the literature. Given the increasing recognition of heterogeneous market structure, this paper proposes an alternative Slantlet denoising based hybrid methodology that attempts to incorporate the linear and nonlinear data features. The recently emerging Slantlet analysis is introduced to separate the linear data features as it constructs filters with varying lengths at different scales and has more appealing time localization features than the normal wavelet analysis. Meanwhile, the Least Squares Support Vector Regression (LSSVR) is used to model and correct for the empirical errors nonlinear in nature. As empirical studies were conducted in the Euro exchange rate market, the performance of the proposed algorithm was compared with those of benchmark models including random walk, ARMA and LSSVR models. The proposed algorithm outperforms the benchmark models. More importantly the proposed methodology explores and offers deeper insights as to the underlying data generating process. © 2010 Published by Elsevier Ltd.

Research Area(s)

  • ARMA model, Denoising algorithm, Least squares support vector regression model, Random walk model, Slantlet analysis