Recurrent Self-Organising Maps and Local Support Vector Machine Models for Exchange Rate Prediction

  • He Ni
  • Hujun Yin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


This paper considers the problem of predicting non-linear, non-stationary financial time sequence data, which is often difficult for traditional regressive models. The Self-Organising Map (SOM) is a vector quantisation method that represents statistical data sets in a topology preserving fashion. The method, which uses the Recurrent Self-Organising Map(RSOM) to partition the original data space into several disjointed regions and then uses Support Vector Machines (SVMs) to make the prediction as a regression method. It is model free and does not require a prior knowledge of the data. Experiments show that the method can make certain degree of profits and outperforms the GARCH method.


Support Vector Machine Local Model GARCH Model Time Series Forecast Time Series Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • He Ni
    • 1
  • Hujun Yin
    • 1
  1. 1.School of Electrical and Electronic EngineeringUniversity of ManchesterManchesterUK

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