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Financial forecasting through unsupervised clustering and evolutionary trained neural networks

Pavlidis, Nicos and Tasoulis, DK and Vrahatis, Michael N. (2003) Financial forecasting through unsupervised clustering and evolutionary trained neural networks. In: IEEE Congress on Evolutionary Computation. IEEE, pp. 2314-2321. ISBN 0-7803-7804-0

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Abstract

We present a time series forecasting methodology and applies it to generate one-step-ahead predictions for two daily foreign exchange spot rate time series. The methodology draws from the disciplines of chaotic time series analysis, clustering, artificial neural networks and evolutionary computation. In brief, clustering is applied to identify neighborhoods in the reconstructed state space of the system; and subsequently neural networks are trained to model the dynamics of each neighborhood separately. The results obtained through this approach are promising.

Item Type: Contribution in Book/Report/Proceedings
Subjects: UNSPECIFIED
Departments: Lancaster University Management School > Management Science
ID Code: 50924
Deposited By: ep_importer_pure
Deposited On: 09 Nov 2011 14:27
Refereed?: No
Published?: Published
Last Modified: 10 Apr 2014 00:43
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/50924

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