Recurrent self-organising maps and local support vector machine models for exchange rate prediction

Authors:
He Ni;Hujun Yin
Affiliations:
School of Electrical and Electronic Engineering, University of Manchester, Manchester, UK;School of Electrical and Electronic Engineering, University of Manchester, Manchester, UK
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

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Abstract

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.