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.
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Ni, H., Yin, H. (2006). Recurrent Self-Organising Maps and Local Support Vector Machine Models for Exchange Rate Prediction. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_74
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DOI: https://doi.org/10.1007/11760191_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34482-7
Online ISBN: 978-3-540-34483-4
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