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 0-7803-7804-0Full text not available from this repository.
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|
|Departments:||Lancaster University Management School > Management Science|
|Deposited On:||09 Nov 2011 14:27|
|Last Modified:||16 Jan 2017 02:06|
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